Sanjay Aneja, MD
Assistant Professor of Therapeutic RadiologyCards
Appointments
Additional Titles
Director of Clinical Informatics, Therapeutic Radiology
Director Medical School Clerkship, Therapeutic Radiology
Medical School Thesis Oversight, Therapeutic Radiology
Radiation Safety, Therapeutic Radiology
Assistant Cancer Center Director, Bioinformatics
Contact Info
Appointments
Additional Titles
Director of Clinical Informatics, Therapeutic Radiology
Director Medical School Clerkship, Therapeutic Radiology
Medical School Thesis Oversight, Therapeutic Radiology
Radiation Safety, Therapeutic Radiology
Assistant Cancer Center Director, Bioinformatics
Contact Info
Appointments
Additional Titles
Director of Clinical Informatics, Therapeutic Radiology
Director Medical School Clerkship, Therapeutic Radiology
Medical School Thesis Oversight, Therapeutic Radiology
Radiation Safety, Therapeutic Radiology
Assistant Cancer Center Director, Bioinformatics
Contact Info
About
Titles
Assistant Professor of Therapeutic Radiology
Director of Clinical Informatics, Therapeutic Radiology; Director Medical School Clerkship, Therapeutic Radiology; Medical School Thesis Oversight, Therapeutic Radiology; Radiation Safety, Therapeutic Radiology; Assistant Cancer Center Director, Bioinformatics
Biography
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, AHRQ, Radiation Society of North America (RSNA), and the American Cancer Society.
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.
Appointments
Therapeutic Radiology
Assistant ProfessorPrimaryBiomedical Informatics & Data Science
Assistant ProfessorSecondary
Other Departments & Organizations
Education & Training
- Radiation Oncology Resident
- Yale University School of Medicine (2018)
- Postdoctoral Research Fellow
- Center for Outcomes Research (CORE) (2017)
- Transitional Year Resident
- Memorial-Sloan Kettering Cancer Center (2014)
- Research Fellow
- Center for Medicare Medicaid Innovation (CMMI) (2013)
- MD
- Yale School of Medicine (2013)
- BA
- Columbia University, Applied Mathematics (2009)
Research
Overview
CNS Malignancies
Medical Subject Headings (MeSH)
ORCID
0000-0001-5681-7528- View Lab Website
Aneja Lab Website
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
James B. Yu, MD, MHS, FASTRO
MingDe Lin, PhD
Antonio Omuro, MD
Harlan Krumholz, MD, SM
Henry S. Park, MD, MPH
Kenneth B. Roberts, MD
Artificial Intelligence
Radiation Oncology
Machine Learning
Radiology
Informatics
Data Science
Publications
Featured Publications
Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation
Avesta A, Hossain S, Lin M, Aboian M, Krumholz H, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering 2023, 10: 181. PMID: 36829675, PMCID: PMC9952534, DOI: 10.3390/bioengineering10020181.Peer-Reviewed Original ResearchCitationsAltmetricConceptsLimited training dataDice scoreComputational memoryTraining dataBrain imagesDeep-learning methodsHigher Dice scoresSegmentation accuracyAuto-segmentation modelComputational speedPerformance metricsOne-sliceAuto-SegmentationBetter performanceConsecutive slicesImagesDeploymentLowest Dice scoresMemoryPerformanceTrainingMetricsModelAccuracyDataArtificial Intelligence in Breast Cancer Screening
Potnis K, Ross J, Aneja S, Gross C, Richman I. Artificial Intelligence in Breast Cancer Screening. JAMA Internal Medicine 2022, 182: 1306-1312. PMID: 36342705, PMCID: PMC10623674, DOI: 10.1001/jamainternmed.2022.4969.Peer-Reviewed Original ResearchCitationsAltmetricPerspectives 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.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsPrevalence 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.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsNational Cancer DatabaseNon-small cell lung cancerOverall survivalCell lung cancerCancer DatabaseMedical recordsLung cancerProstate cancerBreast cancerPatient recordsComplete dataRetrospective cohort studyCohort studyCancer RegistryCommon cancerVariables of interestHigh prevalenceMAIN OUTCOMEPatientsClinical advancementReal-world data sourcesCancerPrevalenceSurvivalHeterogeneous differencesPublic 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 2021, 28: 1574-1577. PMID: 33871009, PMCID: PMC8279784, DOI: 10.1093/jamia/ocab055.Peer-Reviewed Original ResearchCitationsAltmetricComparison 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.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCox proportional hazards modelCox proportional hazardsProportional hazards modelBrain metastasesRadiomic featuresHazards modelProportional hazardsStandard Cox proportional hazards modelMultifocal brain metastasesMultiple brain metastasesNumber of patientsPatient-level outcomesHigher concordance indexRadiomic feature analysisRandom survival forest modelSurvival modelsDifferent tumor volumesMultifocal tumorsCancer outcomesMultiple tumorsMetastatic cancerConcordance indexTumor volumePatientsTumor typesUsing 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.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsPremetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma
Fisk JN, Mahal AR, Dornburg A, Gaffney SG, Aneja S, Contessa JN, Rimm D, Yu JB, Townsend JP. Premetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma. Cancer Letters 2021, 526: 346-351. PMID: 34780851, PMCID: PMC8702484, DOI: 10.1016/j.canlet.2021.11.011.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMutational processesSingle ancestral lineageAncestral lineageProgression of cancerMetastatic lineagesPhylogenetic analysisGenetic resistanceEvolutionary processesExogenous mutational processesCancer evolutionConsolidative therapyMutational signature analysisEGFR-positive non-small cell lung cancerNon-small cell lung cancerKey eventsLineagesCell populationsTherapeutic resistanceLocal consolidative therapyClinical time courseCell lung cancerDisease etiologyTherapeutic decision makingCisplatin therapyLung cancerComparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
Joel M, Avesta A, Yang D, Zhou J, Omuro A, Herbst R, Krumholz H, Aneja S. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging. Cancers 2023, 15: 1548. PMID: 36900339, PMCID: PMC10000732, DOI: 10.3390/cancers15051548.Peer-Reviewed Original ResearchCitationsAltmetricConceptsAdversarial imagesDeep learning modelsDL modelsDetection modelLearning modelConvolutional neural networkDetection schemeAdversarial detectionDefense techniquesMachine learningMedical imagesAdversarial perturbationsInput imageAdversarial trainingNeural networkArt performanceMagnetic resonance imagingGradient descentPixel valuesHigh accuracyImagesBrain magnetic resonance imagingAbsence of malignancyClassificationScheme3D Capsule Networks for Brain Image Segmentation
Avesta A, Hui Y, Aboian M, Duncan J, Krumholz H, Aneja S. 3D Capsule Networks for Brain Image Segmentation. American Journal Of Neuroradiology 2023, 44: 562-568. PMID: 37080721, PMCID: PMC10171390, DOI: 10.3174/ajnr.a7845.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and Concepts
Clinical Trials
Current Trials
Long Covid: Understanding Immune, Symptom, and Treatment Experiences Nationwide (LISTEN Study)
HIC ID2000032207RoleSub InvestigatorPrimary Completion Date01/31/2025Recruiting ParticipantsGenderBothAge18+ years
Clinical Care
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Clinical Specialties
Fact Sheets
Metastatic Brain Tumors
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Board Certifications
Radiation Oncology
- Certification Organization
- AB of Radiology
- Original Certification Date
- 2021
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- May 02, 2024
18 Faculty Selected for Second Longitudinal Coach Cohort
- April 01, 2024
Yale Faculty Present Groundbreaking Clinical Research at the 2024 American College of Cardiology Scientific Sessions
- December 17, 2022Source: PBN
Five Questions With: Dr. Joseph Renzulli, Dr. Sanjay Aneja and Dr. Matthew Austin
- May 13, 2022Source: Advisory Board
Charted: How Patients Really Feel about AI in Health Care
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