Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works

Textural Features for Image Classification

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works

Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1

Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575 · see citing works

Stochastic gradient boosting

Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works

Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis

Cortazar, P., Zhang, L., Untch, M., Mehta, K. M., Costantino, J. P., Wolmark, N., Bonnefoi, H. R., Cameron, D., Gianni, L., Valagussa, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62422-8 · Cited by 4,777 · see citing works

SUSAN—a new approach to low level image processing

Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333 · see citing works

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works

Gene Expression and Benefit of Chemotherapy in Women With Node-Negative, Estrogen Receptor–Positive Breast Cancer

Paik, S., Tang, G., Shak, S., Kim, C., Baker, J. B., Kim, W., Cronin, M. T., Baehner, F. L., Watson, D., Bryant, J., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.7985 · Cited by 2,576 · see citing works

Texture analysis using gray level run lengths

Galloway, M. M. (1975) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(75)80008-6 · Cited by 2,168 · see citing works

On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data

Uno, H., Cai, T., Pencina, M. J., D'Agostino, R. B., & Wei, L. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4154 · Cited by 1,535 · see citing works

Textural features corresponding to textural properties

Amadasun, M., & King, R. A. (1989) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.44046 · Cited by 1,163 · see citing works

Imaging biomarker roadmap for cancer studies

O'Connor, J. P. B., Aboagye, E. O., Adams, J. E., Aerts, H. J. W. L., Barrington, S. F., Beer, A. J., Boellaard, R., Bohndiek, S. E., Brady, M., Brown, G., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2016.162 · Cited by 1,091 · see citing works

New variants of a method of MRI scale standardization

Nyúl, L. G., Udupa, J. K., & Zhang, X. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.836373 · Cited by 958 · see citing works

The logrank test

Bland, M., & Altman, D. G. (2004) · BMJ · doi:10.1136/bmj.328.7447.1073 · Cited by 931 · see citing works

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

Vallières, M., Freeman, C. R., Skamene, S., & Naqa, I. E. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/14/5471 · Cited by 892 · see citing works

Clustering Fiber Traces Using Normalized Cuts

Brun, A., Knutsson, H. E., Park, H., Shenton, M. E., & Westin, C. (2004) · Lecture notes in computer science · doi:10.1007/b100265 · Cited by 785 · see citing works

The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review

Aerts, H. J. W. L. (2016) · JAMA Oncology · doi:10.1001/jamaoncol.2016.2631 · Cited by 669 · see citing works

Texture information in run-length matrices

Tang, X. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.725367 · Cited by 655 · see citing works

Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY TRIAL

Hylton, N. M., Blume, J. D., Bernreuter, W. K., Pisano, E. D., Rosen, M. A., Morris, E. A., Weatherall, P. T., Lehman, C. D., Newstead, G. M., Polin, S. M., et al. (2012) · Radiology · doi:10.1148/radiol.12110748 · Cited by 499 · see citing works

Statistical normalization techniques for magnetic resonance imaging

Shinohara, R. T., Sweeney, E. M., Goldsmith, J., Shiee, N., Mateen, F. J., Calabresi, P. A., Jarso, S., Pham, D. L., Reich, D. S., & Crainiceanu, C. M. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.08.008 · Cited by 426 · see citing works

Response-Guided Neoadjuvant Chemotherapy for Breast Cancer

von Minckwitz, G., Blohmer, J., Costa, S. D., Denkert, C., Eidtmann, H., EIERMANN, W., Gerber, B., Hanusch, C. A., Hilfrich, J., Huober, J., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.45.0940 · Cited by 415 · see citing works

DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting

Ou, Y., Sotiras, A., Nikos Paragios, A., & Davatzikos, C. A. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.07.002 · Cited by 399 · see citing works

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

Macyszyn, L. J., Akbari, H., Pisapia, J. M., Da, X., Attiah, M. A., Pigrish, V., Bi, Y., Pal, S., Davuluri, R. V., Roccograndi, L., et al. (2015) · Neuro-Oncology · doi:10.1093/neuonc/nov127 · Cited by 300 · see citing works

Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival—Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL

Hylton, N. M., Gatsonis, C. A., Rosen, M. A., Lehman, C. D., Newitt, D. C., Partridge, S. C., Bernreuter, W. K., Pisano, E. D., Morris, E. A., Weatherall, P. T., et al. (2015) · Radiology · doi:10.1148/radiol.2015150013 · Cited by 278 · see citing works

Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art

Lee, G., Lee, H. Y., Park, H., Schiebler, M. L., van Beek, E. J. R., Ohno, Y., Seo, J. B., & Leung, A. N. C. (2016) · European Journal of Radiology · doi:10.1016/j.ejrad.2016.09.005 · Cited by 277 · see citing works

Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

Itakura, H., Achrol, A. S., Mitchell, L. A., Loya, J. J., Liu, T. T., Westbroek, E. M., Feroze, A. H., Rodriguez, S., Echegaray, S., Azad, T. D., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa7582 · Cited by 270 · see citing works

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011

Gabor Fichtinger, A., Peters, T., & Martel, A. L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6 · Cited by 262 · see citing works

An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects

Hogea, C. S., Davatzikos, C. A., & Biros, G. (2007) · Journal of Mathematical Biology · doi:10.1007/s00285-007-0139-x · Cited by 253 · see citing works

Probabilistic Radiographic Atlas of Glioblastoma Phenotypes

Ellingson, B. M., Lai, A., Harris, R. J., Selfridge, J. M., Yong, W. H., Das, K., Pope, W. B., Nghiemphu, P. L., Vinters, H. V., Liau, L. M., et al. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3253 · Cited by 221 · see citing works

GLISTR: Glioma Image Segmentation and Registration

Gooya, A., Pohl, K. M., Bilello, M., Cirillo, L., Biros, G., Melhem, E. R., & Davatzikos, C. A. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2210558 · Cited by 217 · see citing works

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c‐means clustering and support vector machine segmentation

Keller, B. M., Nathan, D. L., Wang, Y., Zheng, Y., Gee, J. C., Conant, E. F., & Despina Kontos, B. (2012) · Medical Physics · doi:10.1118/1.4736530 · Cited by 209 · see citing works

SHAPE AND TEXTURE INDEXES APPLICATION TO CELL NUCLEI CLASSIFICATION

THIBAULT, G., Fertil, B., Navarro, C. L., Pereira, S., Cau, P., Lévy, N., Séqueira, J., & MARI, J.-L. (2013) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001413570024 · Cited by 195 · see citing works

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

Akbari, H., Macyszyn, L. J., Da, X., Bilello, M., Wolf, R. L., Martinez‐Lage, M., Biros, G., Alonso‐Basanta, M., O’Rourke, D. M., & Davatzikos, C. A. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001202 · Cited by 164 · see citing works

Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles

Ashraf, A. B., Daye, D., Gavenonis, S. C., Mies, C., Feldman, M. D., Rosen, M. A., & Despina Kontos, B. (2014) · Radiology · doi:10.1148/radiol.14131375 · Cited by 151 · see citing works

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

Gastounioti, A., Conant, E., & Kontos, D. (2016) · Breast Cancer Research · doi:10.1186/s13058-016-0755-8 · Cited by 147 · see citing works

Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers

Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., & Rachid Deriche, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.004 · Cited by 142 · see citing works

A method for clustering white matter fiber tracts.

O’Donnell, L. J., Kubicki, M. R., Shenton, M. E., Dreusicke, M., Grimson, W. E. L., & Westin, C. (2006) · PubMed Central · Cited by 136 · see citing works

Role of Imaging in Neoadjuvant Therapy for Breast Cancer

Dialani, V. M., Chadashvili, T., & Slanetz, P. J. (2015) · Annals of Surgical Oncology · doi:10.1245/s10434-015-4403-9 · Cited by 133 · see citing works

A unified framework for clustering and quantitative analysis of white matter fiber tracts

Maddah, M., Grimson, W. E. L., Warfield, S. K., & Wells, W. M. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.10.003 · Cited by 130 · see citing works

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

Zheng, Y., Keller, B. M., Ray, S., Wang, Y., Conant, E. F., Gee, J. C., & Despina Kontos, B. (2015) · Medical Physics · doi:10.1118/1.4921996 · Cited by 115 · see citing works

Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case–control study with digital mammography

Keller, B. M., Chen, J., Daye, D., Conant, E. F., & Despina Kontos, B. (2015) · Breast Cancer Research · doi:10.1186/s13058-015-0626-8 · Cited by 112 · see citing works

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Crimi, A., Menze, B. H., Maier, O., Reyes, M., & Heinz Handels, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30858-6 · Cited by 108 · see citing works

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., & Davatzikos, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-30858-6_13 · Cited by 101 · see citing works

Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

Akbari, H., Macyszyn, L. J., Da, X., Wolf, R. L., Bilello, M., Verma, R., O’Rourke, D. M., & Davatzikos, C. A. (2014) · Radiology · doi:10.1148/radiol.14132458 · Cited by 100 · see citing works

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

Li, H., Galperin-Aizenberg, M., Pryma, D. A., Simone, C. B., & Yong Fan, A. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.06.025 · Cited by 99 · see citing works

Deformable Registration of Glioma Images Using EM Algorithm and Diffusion Reaction Modeling

Gooya, A., Biros, G., & Davatzikos, C. A. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2078833 · Cited by 94 · see citing works

Radiomics applied to lung cancer: a review

Scrivener, M., de Jong, E. E. C., van Timmeren, J. E., Pieters, T., Ghaye, B., & Geets, X. (2016) · Translational Cancer Research · doi:10.21037/tcr.2016.06.18 · Cited by 93 · see citing works

In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ϕ -Index

Bakas, S., Akbari, H., Pisapia, J. M., Martinez‐Lage, M., Rozycki, M., Rathore, S., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-1871 · Cited by 89 · see citing works

Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma

Bilello, M., Akbari, H., Da, X., Pisapia, J. M., Mohan, S., Wolf, R. L., O’Rourke, D. M., Martinez‐Lage, M., & Davatzikos, C. A. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.03.007 · Cited by 68 · see citing works

Highlights from the 14th St Gallen International Breast Cancer Conference 2015 in Vienna: Dealing with classification, prognostication, and prediction refinement to personalize the treatment of patients with early breast cancer

Espósito, A. (2015) · ecancermedicalscience · doi:10.3332/ecancer.2015.518 · Cited by 66 · see citing works

Breast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response

Ashraf, A. B., Gaonkar, B., Mies, C., DeMichele, A. M., Rosen, M. A., Davatzikos, C. A., & Despina Kontos, B. (2015) · Translational Oncology · doi:10.1016/j.tranon.2015.03.005 · Cited by 59 · see citing works

Brain–Tumor Interaction Biophysical Models for Medical Image Registration

Hogea, C. S., Davatzikos, C. A., & Biros, G. (2008) · SIAM Journal on Scientific Computing · doi:10.1137/07069208x · Cited by 53 · see citing works

Brain Lesions, Introduction

Crimi, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30858-6_1 · Cited by 44 · see citing works

Individualized Map of White Matter Pathways

Tunç, B., Ingalhalikar, M., Parker, D., Lecoeur, J., Singh, N., Wolf, R. L., Macyszyn, L. J., Brem, S. S., & Verma, R. (2015) · Neurosurgery · doi:10.1227/neu.0000000000001183 · Cited by 39 · see citing works

Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy

Arlinghaus, L. R., Li, X., Levy, M. A., Smith, D., Welch, E. B., Gore, J. C., & Yankeelov, T. E. (2010) · Journal of Oncology · doi:10.1155/2010/919620 · Cited by 37 · see citing works

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

Zeng, K., Bakas, S., Sotiras, A., Akbari, H., Rozycki, M., Rathore, S., Pati, S., & Davatzikos, C. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-55524-9_18 · Cited by 37 · see citing works

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Ou, Y., Weinstein, S. P., Conant, E. F., Englander, S. A., Da, X., Gaonkar, B., Hsieh, M., Rosen, M. A., DeMichele, A. M., Davatzikos, C. A., et al. (2014) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25368 · Cited by 36 · see citing works

Automated tract extraction via atlas based Adaptive Clustering

Tunç, B., Parker, W. A., Ingalhalikar, M., & Verma, R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.08.021 · Cited by 36 · see citing works

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

Keller, B. M., Oustimov, A., Wang, Y., Chen, J., Acciavatti, R. J., Zheng, Y., Ray, S., Gee, J. C., Maidment, A. D. A., & Despina Kontos, B. (2015) · Journal of medical imaging · doi:10.1117/1.jmi.2.2.024501 · Cited by 26 · see citing works

Assessing connectivity related injury burden in diffuse traumatic brain injury

Solmaz, B., Tunç, B., Parker, D., Whyte, J., Hart, T., Rabinowitz, A. R., Rohrbach, M., Kim, J., & Verma, R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23561 · Cited by 24 · see citing works

Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model

Bakas, S., Chatzimichail, K., Hunter, G., Labbé, B., Sidhu, P. S., & Makris, D. (2015) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2015.1029642 · Cited by 22 · see citing works

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Heinz Handels, A., Winzeck, S., Reyes, M., Maier, O., Menze, B. H., & Crimi, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-55524-9 · Cited by 21 · see citing works

Unsupervised automatic white matter fiber clustering using a Gaussian mixture model

Liu, M., Vemuri, B. C., & Deriche, R. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235600 · Cited by 15 · see citing works

NIMG-20. IMAGING PATTERN ANALYSIS REVEALS THREE DISTINCT PHENOTYPIC SUBTYPES OF GBM WITH DIFFERENT SURVIVAL RATES

Rathore, S., Akbari, H., Rozycki, M., Bakas, S., & Davatzikos, C. A. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now212.532 · Cited by 13 · see citing works

A Multichannel Markov Random Field Approach for Automated Segmentation of Breast Cancer Tumor in DCE-MRI Data Using Kinetic Observation Model

Ashraf, A. B., Gavenonis, S. C., Daye, D., Mies, C., Feldman, M. D., Rosen, M. A., & Despina Kontos, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_67 · Cited by 11 · see citing works

Addressing the Challenge of Edema in Fiber Tracking

Lecoeur, J., Caruyer, E., Elliott, M. A., Brem, S. S., Macyszyn, L. J., & Verma, R. (2014) · HAL · Cited by 9 · see citing works

MPTH-02. EXTRACELLULAR EGFR289 ACTIVATING MUTATIONS CONFER POORER SURVIVAL AND SUGGEST ENHANCED MOTILITY IN PRIMARY GBMs

Binder, Z. A., Bakas, S., Wileyto, E. P., Akbari, H., Rathore, S., Rozycki, M., Morrissette, J. J. D., Martinez‐Lage, M., Dahmane, N., Davatzikos, C. A., et al. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now212.441 · Cited by 8 · see citing works

Hierarchical Fiber Clustering Based on Multi-Scale Neuroanatomical Features

Wang, Q., Yap, P., Jia, H., Guorong Wu, A., & Shen, D. G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15699-1_47 · Cited by 7 · see citing works

Neoadjuvant Therapy for Breast Cancer: Controversies in Clinical Trial Design and Standard of Care

White, J. R., & DeMichele, A. M. (2015) · American Society of Clinical Oncology Educational Book · doi:10.14694/edbook_am.2015.35.e17 · Cited by 7 · see citing works

Improving White Matter Tractography by Resolving the Challenges of Edema

Lecoeur, J., Caruyer, E., Macyszyn, L. J., & Verma, R. (2013) · HAL · Cited by 6 · see citing works

NIMG-05IDENTIFICATION OF IMAGING SIGNATURES OF THE EPIDERMAL GROWTH FACTOR RECEPTOR VARIANT III (EGFRvIII) IN GLIOBLASTOMA

Bakas, S., Akbari, H., Pisapia, J. M., Rozycki, M., O’Rourke, D. M., & Davatzikos, C. A. (2015) · Neuro-Oncology · doi:10.1093/neuonc/nov225.05 · Cited by 6 · see citing works

NIMG-59. RADIOLOGIC SUBTYPES OF GLIOBLASTOMA CALCULATED VIA MULTI-PARAMETRIC IMAGING SIGNATURES REVEAL COMPLEMENTARY INFORMATION TO CURRENT WHO CLASSIFICATION

Rathore, S., Akbari, H., Rozycki, M., Abdullah, K. G., Nasrallah, M. P., Binder, Z. A., Lustig, R. A., Dahmane, N., Bilello, M., O’Rourke, D. M., et al. (2017) · Neuro-Oncology · doi:10.1093/neuonc/nox168.633 · Cited by 6 · see citing works

NIMG-11. HIGHLY-EXPRESSED WILD-TYPE EGFR AND EGFRvIII MUTANT GLIOBLASTOMAS HAVE SIMILAR MRI SIGNATURE, CONSISTENT WITH DEEP PERITUMORAL INFILTRATION

Bakas, S., Binder, Z. A., Akbari, H., Martinez‐Lage, M., Rozycki, M., Morrissette, J. J. D., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now212.523 · Cited by 5 · see citing works

Unsupervised Machine Learning of Radiomic Features for Predicting Treatment Response and Survival of Early-Stage Nonsmall Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy

Li, H., Galperin-Aizenberg, M., Pryma, D. A., Simone, C. B., & Yong Fan, A. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.06.092 · Cited by 5 · see citing works

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic ClassificationPreprint

Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy–Cramer, J., Kitamura, F., Pati, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02314 · Cited by 431 · see citing works

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works

Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis

Zwanenburg, A. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04391-8 · Cited by 306 · see citing works

Emerging Applications of Artificial Intelligence in Neuro-Oncology

Rudie, J. D., Rauschecker, A. M., Bryan, R. N., Davatzikos, C. A., & Mohan, S. (2019) · Radiology · doi:10.1148/radiol.2018181928 · Cited by 242 · see citing works

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Santamaría, N. A. F., Kazerooni, A. F., Pati, S., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01560-7 · Cited by 199 · see citing works

The Role of Epigenetics in Placental Development and the Etiology of Preeclampsia

Apicella, C., Ruano, C. S. M., Méhats, C., Miralles, F., & Vaiman, D. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112837 · Cited by 194 · see citing works

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Moradmand, H., Aghamiri, S. M. R., & Ghaderi, R. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12795 · Cited by 173 · see citing works

Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development

Binder, Z. A., Thorne, A. H., Bakas, S., Wileyto, E. P., Bilello, M., Akbari, H., Rathore, S., Ha, S. M., Zhang, L., Ferguson, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.06.006 · Cited by 166 · see citing works

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma

Beig, N. G., Bera, K., Prasanna, P., Antunes, J. T., Correa, R., Singh, S., Bamashmos, A. S., Ismail, M., Braman, N. M., Verma, R., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2556 · Cited by 145 · see citing works

Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information

Xie, Y., Jiang, B., Gong, E., Li, Y., Zhu, G., Michel, P., Wintermark, M., & Zaharchuk, G. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20260 · Cited by 142 · see citing works

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning

Rathore, S., Akbari, H., Doshi, J. J., Shukla, G., Rozycki, M., Bilello, M., Lustig, R. A., & Davatzikos, C. A. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.021219 · Cited by 141 · see citing works

Blueprint for cancer research: Critical gaps and opportunities

Elmore, L. W., Greer, S. F., Daniels, E. C., Saxe, C. C., Melner, M. H., Krawiec, G. M., Cance, W. G., & Phelps, W. C. (2020) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21652 · Cited by 121 · see citing works

In vivoevaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature

Akbari, H., Bakas, S., Pisapia, J. M., Nasrallah, M. P., Rozycki, M., Martinez‐Lage, M., Morrissette, J. J. D., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy033 · Cited by 113 · see citing works

Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects

Horng, H., Singh, A., Yousefi, B., Cohen, E. A., Haghighi, B., Katz, S. I., Noël, P. B., Shinohara, R. T., & Despina Kontos, B. (2022) · Scientific Reports · doi:10.1038/s41598-022-08412-9 · Cited by 110 · see citing works

Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI

Le, N. Q. K., Hung, T. N. K., Do, D. T., Lam, L. H. T., Đăng, L. H., & Huynh, T.-T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104320 · Cited by 108 · see citing works

Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review

Kazerooni, A. F., Bakas, S., Rad, H. S., & Davatzikos, C. A. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26907 · Cited by 104 · see citing works

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence

Chitalia, R., Rowland, J., McDonald, E. S., Pantalone, L., Cohen, E. A., Gastounioti, A., Feldman, M. D., Schnall, M. D., Conant, E. F., & Despina Kontos, B. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-4067 · Cited by 101 · see citing works

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

Li, H., Galperin-Aizenberg, M., Pryma, D. A., Simone, C. B., & Yong Fan, A. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.06.025 · Cited by 99 · see citing works

Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma

Akbari, H., Rathore, S., Bakas, S., Nasrallah, M. P., Shukla, G., Mamourian, E., Rozycki, M., Bagley, S. J., Rudie, J. D., Flanders, A. E., et al. (2020) · Cancer · doi:10.1002/cncr.32790 · Cited by 103 · see citing works

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Pati, S., Singh, A., Rathore, S., Gastounioti, A., Bergman, M., Ngo, P., Ha, S. M., Bounias, D., Minock, J., Murphy, G., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-46643-5_38 · Cited by 94 · see citing works

Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans

Qureshi, S. A., Hussain, L., Ibrar, U., Alabdulkreem, E. A., Nour, M. K., Alqahtani, M. S., Nafie, F. M., Mohamed, A., Mohammed, G. P., & Duong, T. Q. (2023) · Scientific Reports · doi:10.1038/s41598-023-30309-4 · Cited by 87 · see citing works

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Kazerooni, A. F., Saxena, S., Toorens, E., Tu, D., Bashyam, V., Akbari, H., Mamourian, E., Sako, C., Koumenis, C., Verginadis, I. I., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-12699-z · Cited by 83 · see citing works

RaCaT: An open source and easy to use radiomics calculator tool

Pfaehler, E. A. G., Zwanenburg, A., de Jong, J. R., & Boellaard, R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212223 · Cited by 82 · see citing works

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

McNitt‐Gray, M. F., Napel, S. A., Jaggi, A., Mattonen, S. A., Hadjiiski, L. M., Muzi, M., Goldgof, D. B., Balagurunathan, Y., Pierce, L. A., Kinahan, P. E., et al. (2020) · Tomography · doi:10.18383/j.tom.2019.00031 · Cited by 76 · see citing works

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test–retest and image registration analyses

Shiri, I., Hajianfar, G., Sohrabi, A., Abdollahi, H., Shayesteh, S. P., Geramifar, P., Zaidi, H., Oveisi, M., & Rahmim, A. (2020) · Medical Physics · doi:10.1002/mp.14368 · Cited by 73 · see citing works

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Beig, N. G., Bera, K., & Tiwari, P. (2020) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaa148 · Cited by 67 · see citing works

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Mang, A., Bakas, S., Subramanian, S., Davatzikos, C. A., & Biros, G. (2020) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-121105 · Cited by 63 · see citing works

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results

Mehta, R., Filos, A., Baid, U., Sako, C., McKinley, R., Rebsamen, M., Dätwyler, K., Meier, R., Radojewski, P., Murugesan, G. K., et al. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-354b · Cited by 56 · see citing works

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation

Cox, J., Liu, P., Stolte, S. E., Yang, Y., Liu, K., See, K. B., Ju, H., & Fang, R. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103301 · Cited by 56 · see citing works

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study

Fathi Kazerooni, A., Arif, S., Madhogarhia, R., Khalili, N., Haldar, D., Bagheri, S., Familiar, A. M., Anderson, H., Haldar, S., Tu, W., et al. (2023) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdad027 · Cited by 55 · see citing works

Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans

Rezaeijo, S. M., Chegeni, N., Naeini, F. B., Makris, D., & Bakas, S. (2023) · Cancers · doi:10.3390/cancers15143565 · Cited by 55 · see citing works

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma

Lu, Y., Patel, M. D., Natarajan, K., Ughratdar, I., Sanghera, P., Jena, R., Watts, C., & Sawlani, V. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.09.017 · Cited by 53 · see citing works

The Federated Tumor Segmentation (FeTS) ChallengePreprint

Pati, S., Baid, U., Zenk, M., Edwards, B., Sheller, M., Reina, G. A., Foley, P., Gruzdev, A., Martin, J., Albarqouni, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05874 · Cited by 102 · see citing works

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities

Bakas, S., Shukla, G., Akbari, H., Erus, G., Sotiras, A., Rathore, S., Sako, C., Ha, S. M., Rozycki, M., Shinohara, R. T., et al. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.3.031505 · Cited by 52 · see citing works

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51 · see citing works

Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review

Walls, G., Osman, S. O. S., Brown, K. H., Butterworth, K. T., Hanna, G. G., Hounsell, A. R., McGarry, C. K., Leijenaar, R. T. H., Lambin, P., Cole, A., et al. (2021) · Clinical Oncology · doi:10.1016/j.clon.2021.10.006 · Cited by 50 · see citing works

The New Era of Salivaomics in Dentistry: Frontiers and Facts in the Early Diagnosis and Prevention of Oral Diseases and Cancer

Papale, F., Santonocito, S., Polizzi, A., Giudice, A. L., Capodiferro, S., Favia, G., & Isola, G. (2022) · Metabolites · doi:10.3390/metabo12070638 · Cited by 51 · see citing works

Integrating deep and radiomics features in cancer bioimaging

Bizzego, A., Bussola, N., Salvalai, D., Chierici, M., Maggio, V., Jurman, G., & Furlanello, C. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791473 · Cited by 50 · see citing works

Proteomics and its applications in breast cancer.

Neagu, A.-N., Whitham, D., Buonanno, E., Jenkins, A., Alexa‐Stratulat, T., Tamba, B.-I., & Darie, C. C. (2021) · PubMed Central · Cited by 48 · see citing works

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

Pati, S., Thakur, S. P., Hamamcı, İ. E., Baid, U., Baheti, B., Bhalerao, M., Güley, O., Mouchtaris, S., Lang, D., Thermos, S., et al. (2023) · Communications Engineering · doi:10.1038/s44172-023-00066-3 · Cited by 54 · see citing works

Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Pati, S., Verma, R., Akbari, H., Bilello, M., Hill, V. B., Sako, C., Correa, R., Beig, N. G., Venet, L., Thakur, S. P., et al. (2020) · Medical Physics · doi:10.1002/mp.14556 · Cited by 45 · see citing works

Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI

Cepeda, S., Luppino, L. T., Pérez‐Núñez, Á., Solheim, O. S., García-García, S., Velasco-Casares, M., Karlberg, A. M., Eikenes, L., Sarabia, R., Arrese, I., et al. (2023) · Cancers · doi:10.3390/cancers15061894 · Cited by 45 · see citing works

MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models

Saeed, N., Ridzuan, M., Alasmawi, H., Sobirov, I., & Yaqub, M. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102989 · Cited by 45 · see citing works

Improved generalized ComBat methods for harmonization of radiomic features

Horng, H., Singh, A., Yousefi, B., Cohen, E. A., Haghighi, B., Katz, S. I., Noël, P. B., Despina Kontos, B., & Shinohara, R. T. (2022) · Scientific Reports · doi:10.1038/s41598-022-23328-0 · Cited by 43 · see citing works

Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas

Khanna, O., Kazerooni, A. F., Farrell, C. J., Baldassari, M. P., Alexander, T. D., Karsy, M. J., Greenberger, B. A., García, J., Sako, C., Evans, J. J., et al. (2021) · Neurosurgery · doi:10.1093/neuros/nyab307 · Cited by 42 · see citing works

Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study

Tang, T. T., Zawaski, J. A., Francis, K. N., Qutub, A. A., & Gaber, M. W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48738-5 · Cited by 42 · see citing works

Standardization of imaging methods for machine learning in neuro-oncology

Li, X. T., & Huang, R. Y. (2020) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaa054 · Cited by 42 · see citing works

Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis

Lei, M.-X., Varghese, B., Hwang, D., Cen, S., Lei, X., Desai, B., Azadikhah, A., Oberai, A., & Duddalwar, V. (2021) · Journal of digital imaging · doi:10.1007/s10278-021-00506-6 · Cited by 38 · see citing works

Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network

Rudie, J. D., Weiss, D. A., Saluja, R., Rauschecker, A. M., Wang, J., Sugrue, L. P., Bakas, S., & Colby, J. B. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00084 · Cited by 41 · see citing works

Peri-implant diseases diagnosis, prognosis and dental implant monitoring: a narrative review of novel strategies and clinical impact

Bornes, R., Montero, J., Correia, A., Marques, T., & Rosa, N. D. N. (2023) · BMC Oral Health · doi:10.1186/s12903-023-02896-1 · Cited by 40 · see citing works

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

Fathi Kazerooni, A., Akbari, H., Shukla, G., Badve, C., Rudie, J. D., Sako, C., Rathore, S., Bakas, S., Pati, S., Singh, A., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00121 · Cited by 39 · see citing works

Radiomics and radiogenomics in pediatric neuro-oncology: A review

Madhogarhia, R., Haldar, D., Bagheri, S., Familiar, A., Anderson, H., Arif, S., Vossough, A., Storm, P. B., Resnick, A. C., Davatzikos, C. A., et al. (2022) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdac083 · Cited by 39 · see citing works

The Río Hortega University Hospital Glioblastoma dataset: A comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)

Cepeda, S., García-García, S., Arrese, I., Herrero, F., Escudero, T., Zamora, T., & Sarabia, R. (2023) · Data in Brief · doi:10.1016/j.dib.2023.109617 · Cited by 38 · see citing works

Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers

Haldar, D., Kazerooni, A. F., Arif, S., Familiar, A., Madhogarhia, R., Khalili, N., Bagheri, S., Anderson, H., Shaikh, I. S., Mahtabfar, A., et al. (2022) · Neoplasia · doi:10.1016/j.neo.2022.100869 · Cited by 37 · see citing works

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts

Linguraru, M. G., Bakas, S., Aboian, M., Chang, P., Flanders, A. E., Kalpathy–Cramer, J., Kitamura, F., Lungren, M. P., Mongan, J., Prevedello, L. M. S., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240225 · Cited by 39 · see citing works

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies

Booth, T. C., Grzeda, M. T., Chelliah, A., Roman, A., Busaidi, A. A., Dragos, C., Shuaib, H., Luis, A., Mirchandani, A., Alparslan, B., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.799662 · Cited by 37 · see citing works

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Chakrabarty, S., LaMontagne, P., Shimony, J. S., Marcus, D. S., & Sotiras, A. (2023) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdad023 · Cited by 37 · see citing works

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice

Bakas, S., Vollmuth, P., Galldiks, N., Booth, T. C., Aerts, H. J. W. L., Bi, W. L., Wiestler, B., Tiwari, P., Pati, S., Baid, U., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00315-2 · Cited by 36 · see citing works

Combining MRI and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma

Rathore, S., Chaddad, A., Iftikhar, M. A., Bilello, M., & Abdulkadir, A. (2021) · Radiology Imaging Cancer · doi:10.1148/rycan.2021200108 · Cited by 33 · see citing works

Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment

Bianconi, A., Rossi, L. F., Bonada, M., Zeppa, P., Nico, E., Marco, R. D., Lacroce, P., Cofano, F., Bruno, F., Morana, G., et al. (2023) · Brain Informatics · doi:10.1186/s40708-023-00207-6 · Cited by 32 · see citing works

Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Cepeda, S., Pérez‐Núñez, Á., García-García, S., García‐Pérez, D., Arrese, I., Roldan, L. J., García-Galindo, M., González, P., Velasco-Casares, M., Zamora, T., et al. (2021) · Cancers · doi:10.3390/cancers13205047 · Cited by 29 · see citing works

Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning

Thakur, S. P., Doshi, J. J., Pati, S., Ha, S. M., Sako, C., Talbar, S. N., Kulkarni, U., Davatzikos, C. A., Erus, G., & Bakas, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_6 · Cited by 28 · see citing works

Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: a multi-case multi-reader study

Novak, A., Ather, S., Gill, A., Aylward, P., Maskell, G., Cowell, G. W., Morgado, A. T. E., Duggan, T., Keevill, M., Gamble, O., et al. (2024) · Emergency Medicine Journal · doi:10.1136/emermed-2023-213620 · Cited by 30 · see citing works

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

Villanueva-Meyer, J. E., Bakas, S., Tiwari, P., Lupo, J., Calabrese, E., Davatzikos, C. A., Bi, W. L., Ismail, M., Akbari, H., Lohmann, P., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00316-4 · Cited by 27 · see citing works

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Venet, L., Pati, S., Feldman, M. D., Nasrallah, M. P., Yushkevich, P. A., & Bakas, S. (2021) · Applied Sciences · doi:10.3390/app11041892 · Cited by 27 · see citing works

Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma

Ismail, M., Prasanna, P., Bera, K., Statsevych, V., Hill, V. B., Singh, G., Partovi, S., Beig, N. G., McGarry, S. D., LaViolette, P. S., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3148780 · Cited by 26 · see citing works

Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging

Akbari, H., Kazerooni, A. F., Ware, J. B., Mamourian, E., Anderson, H., Guiry, S., Sako, C., Raymond, C., Yao, J., Brem, S. S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94560-3 · Cited by 26 · see citing works

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach

Guo, J. F., Kazerooni, A. F., Toorens, E., Akbari, H., Yu, F., Sako, C., Mamourian, E., Shinohara, R. T., Koumenis, C., Bagley, S. J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-55072-y · Cited by 26 · see citing works

The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

Kazerooni, A. F., Akbari, H., Hu, X., Bommineni, V. L., Grigoriadis, D., Toorens, E., Sako, C., Mamourian, E., Ballinger, D. G., Sussman, R. T., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-00767-0 · Cited by 25 · see citing works

Integrative Radiogenomics Approach for Risk Assessment of Post-Operative Metastasis in Pathological T1 Renal Cell Carcinoma: A Pilot Retrospective Cohort Study

Lee, H. W., Cho, H.-H., Joung, J., Jeon, H. G., Jeong, B. C., Jeon, S. S., Lee, H. M., Nam, D., Park, W., Kim, C. K., et al. (2020) · Cancers · doi:10.3390/cancers12040866 · Cited by 25 · see citing works

Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction

Turrisi, R., Pati, S., Pioggia, G., & Tartarisco, G. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121016 · Cited by 24 · see citing works

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

d’Este, S. H., Nielsen, M. B., & Hansen, A. E. (2021) · Diagnostics · doi:10.3390/diagnostics11040592 · Cited by 25 · see citing works

Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme

Duman, A., Sun, X., Thomas, S., Powell, J. R., & Spezi, E. (2024) · Cancers · doi:10.3390/cancers16193351 · Cited by 22 · see citing works

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs

Varghese, B., Shin, H., Desai, B., Gholamrezanezhad, A., Lei, X., Perkins, M., Oberai, A., Nanda, N., Cen, S., & Duddalwar, V. (2021) · British Journal of Radiology · doi:10.1259/bjr.20210221 · Cited by 23 · see citing works

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study

Zhou, Z., Qian, X., Hu, J., Chen, G., Zhang, C., Zhu, J., & Dai, Y. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-20 · Cited by 21 · see citing works

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics

Li, Q., Dong, F., Jiang, B., & Zhang, M. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.646267 · Cited by 19 · see citing works

iGLASS: imaging integration into the Glioma Longitudinal Analysis Consortium

Bakas, S., Ormond, D. R., Alfaro-Munoz, K. D., Smits, M., Cooper, L. A. D., Verhaak, R. G. W., & Poisson, L. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa160 · Cited by 19 · see citing works

Federated Learning for Brain Tumor Segmentation Using MRI and Transformers

Nalawade, S. S., Yogananda, C. G. B., Wagner, B. C., Reddy, D. D., Das, Y., Yu, F. F., Fei, B., Madhuranthakam, A. J., & Maldjian, J. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09002-8_39 · Cited by 18 · see citing works

MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation

Robinet, L., Siegfried, A., Roques, M., Berjaoui, A., & Moyal, E. C. (2023) · Cancers · doi:10.3390/cancers15082253 · Cited by 18 · see citing works

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1

Chitalia, R., Pati, S., Bhalerao, M., Thakur, S. P., Jahani, N., Belenky, V., McDonald, E. S., Gibbs, J., Newitt, D. C., Hylton, N. M., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01555-4 · Cited by 18 · see citing works

Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis

Morrison, G. J., Buckley, J. D., Ostrow, D. G., Varghese, B. A., Cen, S., Werbin, J. L., Ericson, N. G., Cunha, A. T., Lu, Y., George, T. C., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052571 · Cited by 17 · see citing works

Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features

Sayah, A., Bencheqroun, C., Bhuvaneshwar, K., Belouali, A., Bakas, S., Sako, C., Davatzikos, C. A., Alaoui, A., Madhavan, S., & Gusev, Y. (2022) · Scientific Data · doi:10.1038/s41597-022-01415-1 · Cited by 17 · see citing works

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizábal, A., Zimmerer, D., Gruzdev, A. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60466-1 · Cited by 19 · see citing works

Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI

Zhao, W., Hu, Z., Kazerooni, A. F., Koerzdoerfer, G., Nittka, M., Davatzikos, C. A., Viswanath, S. E., Wang, X., Badve, C. A., & Ma, D. (2023) · Investigative Radiology · doi:10.1097/rli.0000000000001026 · Cited by 16 · see citing works

Towards fully automated deep-learning-based brain tumor segmentation: Is brain extraction still necessary?

Pacheco, B. M., Cassia, G. D. S. E., & Silva, D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104514 · Cited by 16 · see citing works

Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale

Upadhyay, V. R., Ramesh, V., Kumar, H., Somagond, Y. M., Priyadarsini, S., Kuniyal, A., Prakash, V., & SAHOO, A. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0109 · Cited by 15 · see citing works

Brain tumour genetic network signatures of survival

Ruffle, J. K., Mohinta, S., Pombo, G., Gray, R., Kopanitsa, V., Lee, F., Brandner, S., Hyare, H., & Nachev, P. (2023) · Brain · doi:10.1093/brain/awad199 · Cited by 14 · see citing works

Investigating the role of imaging factors in the variability of CT‐based texture analysis metrics

Varghese, B. A., Cen, S., Jensen, K., Levy, J. R., Andersen, H. K., Schulz, A., Lei, X., Duddalwar, V. A., & Goodenough, D. J. (2023) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14192 · Cited by 15 · see citing works

Comprehensive benchmarking of CNN-based tumor segmentation methods using multimodal MRI data

Kundal, K., Rao, K. V., Majumdar, A., Kumar, N., & Kumar, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108799 · Cited by 15 · see citing works

Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions

Dong, J., Li, S., Li, L., Liang, S., Zhang, B., Meng, Y., Zhang, X., Zhang, Y., & Zhao, S. (2021) · British Journal of Radiology · doi:10.1259/bjr.20201302 · Cited by 15 · see citing works

Cerebrovascular Reactivity Mapping Using Resting‐State Functional MRI in Patients With Gliomas

Yeh, M., Chen, H., Hou, P., Kumar, V. A., Johnson, J. M., Noll, K. R., Prabhu, S. S., Ferguson, S. D., Schomer, D. F., Peng, H., et al. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28194 · Cited by 15 · see citing works

Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample

Pertuz, S. D., Sassi, A., Holli-Helenius, K., Kämäräinen, J., Rinta-Kiikka, I. C., Lääperi, A.-L., & Arponen, O. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108710 · Cited by 15 · see citing works

ModelHub.AI: Dissemination Platform for Deep Learning ModelsPreprint

Hosny, A., Schwier, M., Berger, C., Örnek, E. P., Turan, M., Tran, P. V., Weninger, L., Isensee, F., Maier-Hein, K. H., McKinley, R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.13218 · Cited by 15 · see citing works

Diagnosing Progression in Glioblastoma—Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma

Belue, M. J., Harmon, S. A., Chappidi, S., Zhuge, Y., Taşçı, E., Jagasia, S., Joyce, T., Camphausen, K. A., Türkbey, B., & Krauze, A. V. (2024) · Diagnostics · doi:10.3390/diagnostics14131374 · Cited by 14 · see citing works

Radiomic Features of the Edema Region May Contribute to Grading Meningiomas With Peritumoral Edema

Guo, Z., Tian, Z., Shi, F., Xu, P., Zhang, J., Ling, C., & Zeng, Q. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28494 · Cited by 14 · see citing works

Arterial Spin Labeling and Dynamic Susceptibility Contrast-enhanced MR Imaging for evaluation of arteriovenous shunting and tumor hypoxia in glioblastoma

Nabavizadeh, S. A., Akbari, H., Ware, J. B., Nasrallah, M. P., Guiry, S., Bagley, S. J., Desai, A. S., Levy, S. C., Sarchiapone, W., Prior, T., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-45312-x · Cited by 13 · see citing works

Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns

Elsheikh, S. S. M., Bakas, S., Mulder, N., Chimusa, E. R., Davatzikos, C. A., & Crimi, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11723-8_24 · Cited by 13 · see citing works

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation

Bathla, G., Durjoy, D., Liu, Y., Le, N. H., Soni, N., Zhang, H., Mohan, S., Roberts-Wolfe, D., Rathore, S., Milan Sonka, A., et al. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.10.044 · Cited by 12 · see citing works

Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study

Novak, A., Hollowday, M., Morgado, A. T. E., Oke, J. L., Shelmerdine, S. C., Woznitza, N., Metcalfe, D., Costa, M. L., Wilson, S., Kiam, J. S., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-086061 · Cited by 12 · see citing works

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap

Raunig, D. L., Pennello, G. A., Delfino, J. G., Buckler, A. J., Hall, T. J., Guimarães, A. R., Wang, X., Huang, E. P., Barnhart, H. X., deSouza, N. M., et al. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.10.026 · Cited by 12 · see citing works

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

Rathore, S., Bakas, S., Pati, S., Akbari, H., Kalarot, R., Sridharan, P., Rozycki, M., Bergman, M., Tunc, B., Verma, R., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-75238-9_12 · Cited by 57 · see citing works

Making Cancer Imaging Smarter: Emerging Techniques and Computational Outlook to Guide Precision Diagnostics

Singh, P. K., Gupta, P. K., Gupta, V. K., Singh, S. K., & Akhtar, S. (2024) · The Open Bioinformatics Journal · doi:10.2174/0118750362344593241111114723 · Cited by 11 · see citing works

Precision diagnostics based on machine learning-derived imaging signatures

Davatzikos, C. A., Sotiras, A., Yong Fan, A., Habes, M., Erus, G., Rathore, S., Bakas, S., Chitalia, R., Gastounioti, A., & Despina Kontos, B. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.012 · Cited by 42 · see citing works

Radiomics: a quantitative imaging biomarker in precision oncology

Jha, A. K., Mithun, S., Purandare, N. C., Kumar, R., Rangarajan, V., Wee, L., & Dekker, A. L. A. J. (2022) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000001543 · Cited by 39 · see citing works

Developing global image feature analysis models to predict cancer risk and prognosis

Zheng, B., Qiu, Y., Aghaei, F., Mirniaharikandehei, S., Heidari, M., & Danala, G. (2019) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-019-0026-5 · Cited by 6 · see citing works