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Quantitative Magnetic Resonance Imaging of the Pancreas of Individuals With Diabetes

Virostko, J. M. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.592349 · Cited by 27 · see citing works

Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction

Liu, R., Hall, L., Bowyer, K. W., Goldgof, D. B., Gatenby, R. A., & Ahmed, K. B. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122802 · Cited by 27 · see citing works

Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival

Li, C., Yan, J., Torheim, T., McLean, M. A., Boonzaier, N. R., Zou, J., Huang, Y., Yuan, J., van Dijken, B. R. J., Matys, T., et al. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.01.008 · Cited by 27 · see citing works

Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution

Kim, J. Y., & Gatenby, R. A. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6539-7_6 · Cited by 26 · see citing works

Radiogenomics in Interventional Oncology

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Texture Feature Analysis to Predict Metastatic and Necrotic Soft Tissue Sarcomas

Farhidzadeh, H., Goldgof, D. B., Hall, L., Gatenby, R. A., Gillies, R. J., & Raghavan, M. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.488 · Cited by 25 · see citing works

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

Arthur, A., Johnston, E. W., Winfield, J. M., Blackledge, M., Jones, R. L., Huang, P. H., & Messiou, C. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.892620 · Cited by 20 · see citing works

Diffusion‐weighted MRI for early detection and characterization of prostate cancer in the transgenic adenocarcinoma of the mouse prostate model

Hill, D. K., Kim, E., Teruel, J. R., Jamin, Y., Widerøe, M., Søgaard, C. D., Størkersen, Ø., Rodrigues, D. N., Heindl, A., Yuan, Y., et al. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25087 · Cited by 20 · see citing works

Habitat Analysis of Breast Cancer‐Enhanced MRI Reflects BRCA1 Mutation Determined by Immunohistochemistry

Du, T., & Zhao, H. (2022) · BioMed Research International · doi:10.1155/2022/9623173 · Cited by 18 · see citing works

Radiomics++: Review of Habitat Imaging Analysis for Decoding Tumor Heterogeneity

Wu, J., Xia, Y., Wang, X., Shi, F., & Shen, D. G. (2026) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-031825-040442 · Cited by 16 · see citing works

RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment

Antunes, J. T., Ismail, M., Hossain, I., Wang, Z., Prasanna, P., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3146778 · Cited by 15 · see citing works

Comparison of Radiomic Features in a Diverse Cohort of Patients With Pancreatic Ductal Adenocarcinomas

Permuth, J. B., Vyas, S., Li, J., Chen, D., Jeong, D. K., & Choi, J. W. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.712950 · Cited by 15 · see citing works

The Future of Radiation Oncology in Soft Tissue Sarcoma

Naghavi, A. O., Yang, G. Q., Latifi, K., Gillies, R. J., McLeod, H. L., & Harrison, L. B. (2018) · Cancer Control · doi:10.1177/1073274818815504 · Cited by 14 · see citing works

Investigating intracranial tumour growth patterns with multiparametric MRI incorporating Gd‐DTPA and USPIO‐enhanced imaging

Boult, J. K. R., Borri, M., Jury, A., Popov, S., Box, G., Perryman, L., Eccles, S. A., Jones, C., & Robinson, S. P. (2016) · NMR in Biomedicine · doi:10.1002/nbm.3594 · Cited by 12 · see citing works

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

Wei, R., Lu, S., Lai, S., Liang, F., Zhang, W., Jiang, X., Zhen, X., & Yang, R. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-05603-3 · Cited by 12 · see citing works

Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma

Mahmoudi, K., Kim, D., Tavakkol, E., Kihira, S., Bauer, A., Tsankova, N. M., Khan, F., Hormigo, A., Yedavalli, V. S., & Nael, K. (2024) · Cancers · doi:10.3390/cancers16030589 · Cited by 11 · see citing works

Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses

McLeod, G. A., Stanley, E. A. M., Rosenal, T., & Forkert, N. D. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02226-5 · Cited by 9 · see citing works

Decoding brain cancer dynamics: a quantitative histogram-based approach using temporal MRI

Zhou, M., Hall, L., Goldgof, D. B., Russo, R. J., Gillies, R. J., & Gatenby, R. A. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2075545 · Cited by 9 · see citing works

Using Radiomics to Differentiate Brain Metastases From Lung Cancer Versus Breast Cancer, Including Predicting Epidermal Growth Factor Receptor and human Epidermal Growth Factor Receptor 2 Status

Shi, J., Chen, H., Wang, X., Cao, R., Chen, Y., Cheng, Y., Pang, Z., & Huang, C. (2023) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001499 · Cited by 9 · see citing works

Subregion Radiomics Analysis to Display Necrosis After Hepatic Microwave Ablation—A Proof of Concept Study

Bressem, K. K., Adams, L. C., Vahldiek, J. L., Erxleben, C., Poch, F. G. M., Lehmann, K. S., Hamm, B., & Niehues, S. M. (2020) · Investigative Radiology · doi:10.1097/rli.0000000000000653 · Cited by 9 · see citing works

Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study

Wang, B., Guo, H., Zhang, M., Huang, Y., Duan, L., Huang, C., Xu, J., & Wang, H. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1433196 · Cited by 8 · see citing works

Differential effect of vascularity between long‐ and short‐term survivors with IDH1/2 wild‐type glioblastoma

Álvarez‐Torres, M. D. M., Fuster‐García, E., Reynés, G., Juan–Albarracín, J., Chelebian, E., Oleaga, L., Pineda, J., Auger, C., Rovira, À., Emblem, K. E., et al. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4462 · Cited by 8 · see citing works

Fuzzy volumetric delineation of brain tumor and survival prediction

Bhadani, S., Mitra, S., & Banerjee, S. (2020) · Soft Computing · doi:10.1007/s00500-020-04728-8 · Cited by 6 · see citing works

MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses

Pinho, M. C., Bera, K., Beig, N. G., & Tiwari, P. (2020) · Neuromethods · doi:10.1007/978-1-0716-0856-2_14 · Cited by 6 · see citing works

Genomics and radiomics: Tools to see the unseen to personalize radiation therapy

Grass, G. D., Mills, M. N., Scott, J. G., Eschrich, S. A., & Torres–Roca, J. F. (2019) · Applied Radiation Oncology · doi:10.37549/aro1213 · Cited by 6 · see citing works

A novel multimodal framework combining habitat radiomics, deep learning, and conventional radiomics for predicting MGMT gene promoter methylation in Glioma: Superior performance of integrated models

Zhu, F.-Y., Chen, W., Chen, H., Ren, S.-Y., Zhuo, L.-Y., Wang, T.-D., Ren, C., Yin, X., & Wang, J.-N. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112406 · Cited by 5 · see citing works

RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction

Antunes, J. T., Prasanna, P., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_53 · Cited by 5 · see citing works

A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours

Pérez-Beteta, J., Martínez‐González, A., & Pérez‐García, V. M. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0503 · Cited by 5 · see citing works

Radiomic Features Associated with Extent of Resection in Glioma Surgery

Muscas, G., Orlandini, S., Becattini, E., Battista, F., Staartjes, V. E., Serra, C., & Puppa, A. D. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_38 · Cited by 5 · see citing works

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma

Johnston, S. K., Whitmire, P., Massey, S. C., Kumthekar, P., Porter, A. B., Raghunand, N., Gonzalez‐Cuyar, L. F., Mrugała, M. M., Hawkins-Daarud, A. J., Jackson, P., et al. (2019) · American Journal of Clinical Oncology · doi:10.1097/coc.0000000000000564 · Cited by 5 · see citing works

Radio-iBAG: Radiomics-based integrative Bayesian analysis of multiplatform genomic data

Zhang, Y., Morris, J. S., Narang, S., Rao, A. U. K., & Baladandayuthapani, V. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1238 · Cited by 4 · see citing works

Association of graph-based spatial features with overall survival status of glioblastoma patients

Lee, J., Narang, S., Martinez, J. J., Rao, G., & Rao, A. U. K. (2023) · Scientific Reports · doi:10.1038/s41598-023-44353-7 · Cited by 3 · see citing works

Radiologically Defined Tumor-habitat Adjacency as a Prognostic Biomarker in Glioblastoma

Xu, X., Samaras, D., & Prasanna, P. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629779 · Cited by 3 · see citing works

Signal intensity analysis of ecological defined habitat in soft tissue sarcomas to predict metastasis development

Farhidzadeh, H., Chaudhury, B., Scott, J. G., Goldgof, D. B., Hall, L., Gatenby, R. A., Gillies, R. J., & Raghavan, M. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216961 · Cited by 3 · see citing works

Imbalanced learning for clinical survival group prediction of brain tumor patients

Zhou, M., Hall, L., Goldgof, D. B., Gillies, R. J., & Gatenby, R. A. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2075606 · Cited by 2 · see citing works

Kurtosis is An MRI Radiomics Feature Predictor of Poor Prognosis in Patients with GBM

Borges, P. H. D. M., Lizar, J. C., Faustino, A. C., Arruda, G. V., & Pavoni, J. F. (2021) · Brazilian Journal of Physics · doi:10.1007/s13538-021-00912-9 · Cited by 2 · see citing works

Imaging Biomarker Measurements

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Application and constraints of AI in radiomics and radiogenomics (R-n-R) studies of neuro-oncology

Panda, S., Padhi, S., Gupta, V., Suri, J. S., & Saxena, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18508-3.00009-7 · Cited by 1 · see citing works

Bridging Spatial Scales From Radiographic Images to Cellular and Molecular Properties in Cancers

Raghunand, N., & Gatenby, R. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816386-3.00053-3 · Cited by 1 · see citing works

The Use of Textural Kinetic Habitats to Mine Diagnostic Information from DCE MR Images of Breast Tumors

Chaudhury, B. (2015)

BAYESIAN INTEGRATIVE ANALYSIS OF OMICS DATA

Zhang, Y. (2018) · Digital Commons-TMC (Texas Medical Center)

Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

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Análise de textura em pacientes com lesão de glioblastoma multiforme para correlação com o prognóstico da doença

Borges, P. H. D. M. (2020) · doi:10.11606/d.59.2020.tde-28052020-162040

Characterization of vascular heterogeneity of astrocytomas grade 4 for supporting patient prognosis estimation, and treatment response assessment

Torres, M. D. M. Á. (2022) · doi:10.4995/thesis/10251/188957

Classification and Evolution of Tumor Ecosystem

Saw, P. E., & Song, E. (2023) · Springer eBooks · doi:10.1007/978-981-99-1183-7_29

MRI-based severity classification of central canal stenosis using radiomics features and machine learning

Dehbaghi, H. A., Khoshgard, K., & Jafari, S. (2026) · Neuroradiology · doi:10.1007/s00234-026-04238-8

Habitat imaging based on dual-energy computed tomography for predicting axillary lymph node metastasis in breast cancer

Tang, S., Li, L., Wang, X., Huang, Y., Cao, Y. J., Gong, X., Cheng, Y., & Zhang, J. (2025) · Acta Radiologica · doi:10.1177/02841851251333291

New radiomics approaches for hepatic tumor characterization through imaging analysis

Ouhmich, F. (2021) · doi:10.70675/b5d050c9zbcc8z4ba2z8e59z8b953b2cebea

HINTS: Hierarchically Disentangling Subregional Heterogeneity With Structural Priors for Multi-Modal Survival Analysis

Li, W., Chen, Y. Z., Chen, B., Huang, G., T, J., Yuan, X., Li, Y., Pun, C., Lei, B., Wang, L., et al. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3711281

Radiomics: Transforming Medical Imaging into Quantitative Insights for Precision Medicine

Sri, G., Rouf, A., Kumar, A., & Gupta, P. (2026) · Journal of Postgraduate Medicine Education and Research · doi:10.5005/jp-journals-10028-1758

Ultrasound-Based Habitat Radiomics for Differentiating Phyllodes Tumor from Breast Fibroadenoma

Xue, Q., Ding, W., Lu, Z., Chen, F., Jia, M. X., Liu, J., Sun, J., Li, S., Zhou, J., Zhao, C., et al. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.09.010

Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme

Stringfield, O., Arrington, J. A., Johnston, S. K., Rognin, N. G., Peeri, N. C., Balagurunathan, Y., Jackson, P., Clark-Swanson, K. R., Swanson, K. R., Egan, K. M., et al. (2019) · Tomography · doi:10.18383/j.tom.2018.00052 · Cited by 44 · see citing works

Survival time prediction of patients with glioblastoma multiforme tumors using spatial distance measurement

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Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation

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Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction

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The Effect of Heterogenous Subregions in Glioblastomas on Survival Stratification: A Radiomics Analysis Using the Multimodality MRI

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