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Sanjay Aneja, MD

Assistant Professor of Therapeutic Radiology; Director of Clinical Informatics, Therapeutic Radiology; Co-Director Medical School Clerkship, Therapeutic Radiology; Medical School Thesis Oversight, Therapeutic Radiology; Radiation Safety, Therapeutic Radiology

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Sanjay Aneja, MD
Aneja Lab Website

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Yale Medicine

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Research Summary

Sanjay Aneja, MD is an Assistant Professor within the Department of Therapeutic Radiology at Yale School of Medicine. Dr. Aneja is a physician scientist whose research group is focused on the application of machine learning techniques on clinical oncology. He received his medical degree from Yale School of Medicine and served as class president. During medical school he completed a research fellowship at the Department of Health and Human Services in large scale data analysis. He later completed his medicine internship at Memorial Sloan Kettering Cancer Center followed by his residency in radiation oncology at Yale-New Haven Hospital. During his residency he completed his post-doc in machine learning at the Center for Outcomes Research and Evaluation (CORE) receiving research grant from IBM Computing. He is currently a recipient of an NIH Career Development award, an NSF research grant, and an American Cancer Society Research Award.

The Aneja Labs on-going efforts include:

1) Deep Learning to Derive Imaging Based Biomarkers of Cancer Outcomes: We have previously shown the ability for deep learning to derive imaging-based biomarkers for lung cancer and are currently applying our deep learning platform to brain metastases. We have developed a national consortium of 7 institutions whom have contributed data to our effort. This project is funded by the NIH and Radiation Society of North America (RSNA)

2) AI-Driven Collection of Patient Reported Outcomes: Our group is developing deep learning algorithms which use patient audio diaries to predict validated patient reported outcome metrics. Through a collaboration with Amazon, we hope to integrate our algorithm into virtual assistants and pilot them in a clinical setting.

3) Machine Learning Methods for Clinical Trial Classification: Our group, through a collaboration with SWOG and an industry partner, is studying the ability of machine learning to classify cancer clinical trials and match clinicians to relevant randomized clinical trials. This project is currently funded by the NSF and SWOG Hope Grant.

Extensive Research Description

CNS Malignancies

Coauthors

Research Interests

Artificial Intelligence; Classification; Health Services; Mathematics; Radiology; Radiation Oncology; Informatics; Machine Learning; Data Science

Selected Publications

  • Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.Kann BH, Hicks DF, Payabvash S, Mahajan A, Du J, Gupta V, Park HS, Yu JB, Yarbrough WG, Burtness BA, Husain ZA, Aneja S. Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma. Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology 2020, 38: 1304-1311. PMID: 31815574, DOI: 10.1200/JCO.19.02031.
  • Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology.Joel MZ, Umrao S, Chang E, Choi R, Yang DX, Duncan JS, Omuro A, Herbst R, Krumholz HM, Aneja S. Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology. JCO Clinical Cancer Informatics 2022, 6: e2100170. PMID: 35271304, PMCID: PMC8932490, DOI: 10.1200/CCI.21.00170.
  • Perspectives of Patients About Artificial Intelligence in Health Care.Khullar D, Casalino LP, Qian Y, Lu Y, Krumholz HM, Aneja S. Perspectives of Patients About Artificial Intelligence in Health Care. JAMA Network Open 2022, 5: e2210309. PMID: 35507346, PMCID: PMC9069257, DOI: 10.1001/jamanetworkopen.2022.10309.
  • Artificial Intelligence in Oncology: Current Applications and Future Directions.Kann BH, Thompson R, Thomas CR, Dicker A, Aneja S. Artificial Intelligence in Oncology: Current Applications and Future Directions. Oncology (Williston Park, N.Y.) 2019, 33: 46-53. PMID: 30784028.
  • Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival.Yang DX, Khera R, Miccio JA, Jairam V, Chang E, Yu JB, Park HS, Krumholz HM, Aneja S. Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival. JAMA Network Open 2021, 4: e211793. PMID: 33755165, PMCID: PMC7988369, DOI: 10.1001/jamanetworkopen.2021.1793.
  • Comparison of radiomic feature aggregation methods for patients with multiple tumors.Chang E, Joel MZ, Chang HY, Du J, Khanna O, Omuro A, Chiang V, Aneja S. Comparison of radiomic feature aggregation methods for patients with multiple tumors. Scientific Reports 2021, 11: 9758. PMID: 33963236, PMCID: PMC8105371, DOI: 10.1038/s41598-021-89114-6.
  • Public vs physician views of liability for artificial intelligence in health care.Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. Public vs physician views of liability for artificial intelligence in health care. Journal Of The American Medical Informatics Association : JAMIA 2021, 28: 1574-1577. PMID: 33871009, PMCID: PMC8279784, DOI: 10.1093/jamia/ocab055.
  • Imaging biomarkers for brain metastases: more than meets the eye.Aneja S, Omuro A. Imaging biomarkers for brain metastases: more than meets the eye. Neuro-oncology 2019, 21: 1493-1494. PMID: 31777936, PMCID: PMC6917408, DOI: 10.1093/neuonc/noz193.
  • Impact of tissue heterogeneity correction on Gamma Knife stereotactic radiosurgery of acoustic neuromas.Peters GW, Tien CJ, Chiang V, Yu J, Hansen JE, Aneja S. Impact of tissue heterogeneity correction on Gamma Knife stereotactic radiosurgery of acoustic neuromas. Journal Of Radiosurgery And SBRT 2021, 7: 207-212. PMID: 33898084, PMCID: PMC8055239.
  • Applications of artificial intelligence in neuro-oncology.Aneja S, Chang E, Omuro A. Applications of artificial intelligence in neuro-oncology. Current Opinion In Neurology 2019, 32: 850-856. PMID: 31609739, DOI: 10.1097/WCO.0000000000000761.
  • Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks.Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, Yu JB, Park HS, Yarbrough WG, Malhotra A, Burtness BA, Husain ZA. Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks. Scientific Reports 2018, 8: 14036. PMID: 30232350, PMCID: PMC6145900, DOI: 10.1038/s41598-018-32441-y.
  • Artificial Intelligence in Radiation Oncology Imaging.Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. Artificial Intelligence in Radiation Oncology Imaging. International Journal Of Radiation Oncology, Biology, Physics 2018, 102: 1159-1161. PMID: 30353870, DOI: 10.1016/j.ijrobp.2018.05.070.
  • The Future of Artificial Intelligence in Radiation Oncology.Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. The Future of Artificial Intelligence in Radiation Oncology. International Journal Of Radiation Oncology, Biology, Physics 2018, 102: 247-248. PMID: 30191856, DOI: 10.1016/j.ijrobp.2018.05.072.
  • Career Enrichment Opportunities at the Scientific Frontier in Radiation Oncology.Thompson RF, Fuller CD, Berman AT, Aneja S, Thomas CR. Career Enrichment Opportunities at the Scientific Frontier in Radiation Oncology. JCO Clinical Cancer Informatics 2019, 3: 1-4. PMID: 30817170, PMCID: PMC6582958, DOI: 10.1200/CCI.18.00126.
  • Risk of Clinically Significant Prostate Cancer Associated With Prostate Imaging Reporting and Data System Category 3 (Equivocal) Lesions Identified on Multiparametric Prostate MRI.Sheridan AD, Nath SK, Syed JS, Aneja S, Sprenkle PC, Weinreb JC, Spektor M. Risk of Clinically Significant Prostate Cancer Associated With Prostate Imaging Reporting and Data System Category 3 (Equivocal) Lesions Identified on Multiparametric Prostate MRI. AJR. American Journal Of Roentgenology 2018, 210: 347-357. PMID: 29112469, DOI: 10.2214/AJR.17.18516.
  • Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiotherapy And Oncology : Journal Of The European Society For Therapeutic Radiology And Oncology 2018, 129: 421-426. PMID: 29907338, DOI: 10.1016/j.radonc.2018.05.030.

Clinical Trials

ConditionsStudy Title
Brain and Nervous SystemPhase III Trial of Post-Surgical Single Fraction Stereotactic Radiosurgery (SRS) Compared With Fractionated SRS for Resected Metastatic Brain Disease
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