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

Greedy function approximation: A gradient boosting machine

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Textural Features for Image Classification

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

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

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Stochastic gradient boosting

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

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SUSAN—a new approach to low level image processing

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

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

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Texture analysis using gray level run lengths

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

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Federated learning enables big data for rare cancer boundary detection

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

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Emerging Applications of Artificial Intelligence in Neuro-Oncology

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

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

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

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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

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 146

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 147

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 144

Blueprint for cancer research: Critical gaps and opportunities

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

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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 117

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

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

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 102

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

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 105

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

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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 91

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

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

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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

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

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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

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

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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

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation

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

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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

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

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The Federated Tumor Segmentation (FeTS) ChallengePreprint

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

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

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

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

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Integrating deep and radiomics features in cancer bioimaging

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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

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 68

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

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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

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 51

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

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

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

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

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

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

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

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

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

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

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

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

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 43

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

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

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

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 37

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

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

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 31

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

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

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

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

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