Machine Learning and Radiomics

Recent advances in radiomics have enabled the prediction of clinical outcomes based on radiologic imaging. Aiming to achieve early prediction of treatment responses, patient stratification, and prognosis, we have developed novel tools for early predicting treatment outcomes of cancer patients, facilitating early identification of patients that would benefit from adjuvant treatment, thus ultimately improving treatment outcomes.

  • Li H, Galperin-Aizenberg M, Pryma D, Simone CB II, Fan Y, 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. Radiother Oncol. 2018 Nov;129(2):218-226. doi: 10.1016/j.radonc.2018.06.025. PMID: 30473058
  • Li H, Boimel P, Janopaul-Naylor J, Zhong H, Xiao Y, Ben-Josef E, Fan Y. Deep convolutional neural networks for imaging data based survival analysis of rectal cancer. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:846-849. doi: 10.1109/ISBI.2019.8759301. Epub 2019 Jul 11. PMID: 31929858; PMCID: PMC6955095.
  • Martin-Carreras T, Li H, Cooper K, Fan Y, Sebro R. Radiomic features from MRI distinguish myxomas from myxofibrosarcomas. BMC Med Imaging. 2019 Aug 15;19(1):67. doi: 10.1186/s12880-019-0366-9. PMID: 31416421; PMCID: PMC6694512.
  • Liu H, Li H, Habes M, Li Y, Boimel P, Janopaul-Naylor J, Xiao Y, Ben-Josef E, Fan Y, Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis. IEEE Trans Biomed Eng. 2020 Jan 27; doi: 10.1109/TBME.2020.2969839. PMID: 31995474.
  • Jiao Z, Li H, Xiao Y, Aggarwal C, Galperin-Aizenberg M, Pryma D, Simone CB II, Feigenberg SJ, Kao GD, Fan Y, Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy, Int J Radiat Oncol Biol Phys. 2021 Apr 1;109(5):1647-1656. doi: 10.1016/j.ijrobp.2020.12.014. Epub 2021 Jan 19. PMID: 33333202
  • Jiao Z, Li H, Xiao Y, Dorsey J, Simone CB II, Feigenberg SJ, Kao GD, Fan Y, Integration of deep learning radiomics and counts of circulating tumor cells improves prediction of outcomes of early stage NSCLC patients treated with SBRT. Int J Radiat Oncol Biol Phys. 2022 Mar 15; 112(4):1045-1054. doi: 10.1016/j.ijrobp.2021.11.006. PMID: 34775000