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Sanjay Aneja, MD

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

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Sanjay Aneja, MD
Aneja Lab Website
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

  • 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.
  • Artificial Intelligence in Breast Cancer ScreeningPotnis K, Ross J, Aneja S, Gross C, Richman I. Artificial Intelligence in Breast Cancer Screening JAMA Internal Medicine 2022, 182: 1306-1312. PMID: 36342705, DOI: 10.1001/jamainternmed.2022.4969.
  • Perspectives of Patients About Artificial Intelligence in Health CareKhullar 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.
  • Prevalence of Missing Data in the National Cancer Database and Association With Overall SurvivalYang 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.
  • 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 2021, 28: 1574-1577. PMID: 33871009, PMCID: PMC8279784, DOI: 10.1093/jamia/ocab055.
  • Comparison of radiomic feature aggregation methods for patients with multiple tumorsChang 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.
  • Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in OncologyJoel 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.
  • Premetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinomaFisk 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.
  • Comparing 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.
  • Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial IntelligenceMoore N, McWilliam A, Aneja S. Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence Seminars In Radiation Oncology 2023, 33: 70-75. PMID: 36517196, DOI: 10.1016/j.semradonc.2022.10.009.
  • NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NETJekel L, Bousabarah K, Lin M, Merkaj S, Kaur M, Avesta A, Aneja S, Omuro A, Chiang V, Scheffler B, Aboian M. NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET Neuro-Oncology 2022, 24: vii162-vii162. PMCID: PMC9661012, DOI: 10.1093/neuonc/noac209.622.
  • NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASETLost J, Tillmans N, Merkaj S, von Reppert M, Lin M, Bousabarah K, Huttner A, Aneja S, Omuro A, Aboian M, Avesta A. NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET Neuro-Oncology 2022, 24: vii165-vii166. DOI: 10.1093/neuonc/noac209.638.
  • Screening for Extranodal Extension with Deep Learning: Evaluation in ECOG-ACRIN E3311, a Randomized De-Escalation Trial for HPV-Associated Oropharyngeal CarcinomaKann B, Likitlersuang J, Ye Z, Aneja S, Bakst R, Kelly H, Juliano A, Payabvash S, Guenette J, Uppaluri R, Margalit D, Schoenfeld J, Tishler R, Haddad R, Aerts H, Garcia J, Flamand Y, Subramaniam R, Burtness B, Ferris R. Screening for Extranodal Extension with Deep Learning: Evaluation in ECOG-ACRIN E3311, a Randomized De-Escalation Trial for HPV-Associated Oropharyngeal Carcinoma International Journal Of Radiation Oncology • Biology • Physics 2022, 114: s26-s27. DOI: 10.1016/j.ijrobp.2022.07.379.
  • Comparison of Diagnostic PET and 4D CT-Based Tumor Delineation for Oligometastatic Lung TumorsWallington D, Verma N, Laird J, Aneja S, Park H, Yang D. Comparison of Diagnostic PET and 4D CT-Based Tumor Delineation for Oligometastatic Lung Tumors International Journal Of Radiation Oncology • Biology • Physics 2022, 114: e407-e408. DOI: 10.1016/j.ijrobp.2022.07.1584.
  • Predictors of Early Polymetastasis after Comprehensive Local Therapy for Synchronous Oligometastatic NSCLCLaird J, Verma N, Moore N, Park H, Aneja S. Predictors of Early Polymetastasis after Comprehensive Local Therapy for Synchronous Oligometastatic NSCLC International Journal Of Radiation Oncology • Biology • Physics 2022, 114: s32. DOI: 10.1016/j.ijrobp.2022.07.388.
  • Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment.Choi R, Joel M, Hui M, Aneja S. Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment. Journal Of Clinical Oncology 2022, 40: 600-600. DOI: 10.1200/jco.2022.40.16_suppl.600.
  • Comparison of Machine Learning Approaches for Missing Data Imputation Among Non-Small Cell Lung Cancer PatientsYang D, Khera R, Chang E, Joel M, Janda G, Park H, Aneja S. Comparison of Machine Learning Approaches for Missing Data Imputation Among Non-Small Cell Lung Cancer Patients International Journal Of Radiation Oncology • Biology • Physics 2021, 111: s115. DOI: 10.1016/j.ijrobp.2021.07.264.
  • Opportunities for integration of artificial intelligence into stereotactic radiosurgery practice.Kotecha R, Aneja S. Opportunities for integration of artificial intelligence into stereotactic radiosurgery practice. Neuro-Oncology 2021, 23: 1629-1630. PMID: 34244803, PMCID: PMC8485447, DOI: 10.1093/neuonc/noab169.
  • Abstract 184: The utility of deep metric learning for breast cancer identification on mammographic imagesDu J, Umrao S, Chang E, Joel M, Gilson A, Janda G, Choi R, Hui Y, Aneja S. Abstract 184: The utility of deep metric learning for breast cancer identification on mammographic images Cancer Research 2021, 81: 184-184. DOI: 10.1158/1538-7445.am2021-184.
  • Abstract PO-074: The impact of phenotypic bias in the generalizability of deep learning models in non-small cell lung cancerGilson A, Du J, Janda G, Umrao S, Joel M, Choi R, Herbst R, Krumholz H, Aneja S. Abstract PO-074: The impact of phenotypic bias in the generalizability of deep learning models in non-small cell lung cancer Clinical Cancer Research 2021, 27: po-074-po-074. DOI: 10.1158/1557-3265.adi21-po-074.
  • Abstract PO-078: Exploring adversarial image attacks on deep learning models in oncologyJoel M, Umrao S, Chang E, Choi R, Yang D, Gilson A, Herbst R, Krumholz H, Aneja S. Abstract PO-078: Exploring adversarial image attacks on deep learning models in oncology Clinical Cancer Research 2021, 27: po-078-po-078. DOI: 10.1158/1557-3265.adi21-po-078.
  • Machine Learning Analysis of Local Recurrence of Meningioma Treated with Stereotactic RadiotherapyGreenberger B, Piper K, Chang E, Khanna O, Collopy S, Huttler E, Aneja S, Liu H, Werner-Wasik M, Evans J, Farrell C, Shi W. Machine Learning Analysis of Local Recurrence of Meningioma Treated with Stereotactic Radiotherapy Journal Of Neurological Surgery Part B Skull Base 2021, 82: s65-s270. DOI: 10.1055/s-0041-1725247.
  • 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.
  • Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors.Chang E, Joel M, Chang HY, Du J, Khanna O, Omuro A, Chiang V, Aneja S. Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors. MedRxiv : The Preprint Server For Health Sciences 2020 PMID: 33173902, PMCID: PMC7654896, DOI: 10.1101/2020.11.04.20226159.
  • Provider Engagement in Radiation Oncology Data Science: Workshop Report.Jain AK, Aneja S, Fuller CD, Dicker AP, Chung C, Kim E, Kirby JS, Quon H, Lam CJK, Louv WC, Ahern C, Xiao Y, McNutt TR, Housri N, Ennis RD, Kang J, Tang Y, Higley H, Berny-Lang MA, Camphausen KA. Provider Engagement in Radiation Oncology Data Science: Workshop Report. JCO Clinical Cancer Informatics 2020, 4: 700-710. PMID: 32755458, PMCID: PMC7469584, DOI: 10.1200/cci.20.00051.
  • Stereotactic Radiosurgery for Pediatric Arteriovenous Malformations: A Case Series Reflecting One Institution’s Experience With TreatmentChoi R, An Y, Aneja S, Bindra R, Bond J, Chiang V, Hansen J, Hebert R, Matouk C, Yu J. Stereotactic Radiosurgery for Pediatric Arteriovenous Malformations: A Case Series Reflecting One Institution’s Experience With Treatment International Journal Of Radiation Oncology • Biology • Physics 2020, 108: e244. DOI: 10.1016/j.ijrobp.2020.07.589.
  • The Impact of Missing/Incomplete Data in Real-World Data StudiesYang D, Miccio J, Jairam V, Chang E, Yu J, Park H, Aneja S. The Impact of Missing/Incomplete Data in Real-World Data Studies International Journal Of Radiation Oncology • Biology • Physics 2020, 108: e394. DOI: 10.1016/j.ijrobp.2020.07.2432.
  • A Multi-Institutional External Validation of a Deep-Learning Based Platform for Prediction of Outcomes following SBRT Treatment for Early-Stage Non-Small Cell Lung CancerGreenberger B, Chang E, Mistro M, Taylor J, Harrison A, Decker R, Werner-Wasik M, Dicker A, Aneja S. A Multi-Institutional External Validation of a Deep-Learning Based Platform for Prediction of Outcomes following SBRT Treatment for Early-Stage Non-Small Cell Lung Cancer International Journal Of Radiation Oncology • Biology • Physics 2020, 108: s165. DOI: 10.1016/j.ijrobp.2020.07.933.
  • Deep Learning Survival Analysis for Brain Metastases Treated with Stereotactic RadiosurgeryChang E, Joel M, Chang H, Du J, Yu J, An Y, Hansen J, Omuro A, Chiang V, Aneja S. Deep Learning Survival Analysis for Brain Metastases Treated with Stereotactic Radiosurgery International Journal Of Radiation Oncology • Biology • Physics 2020, 108: s164-s165. DOI: 10.1016/j.ijrobp.2020.07.932.
  • Development and Validation of a Deep Learning Algorithm to Predict Radiation Pneumonitis and Esophagitis Following Lung Stereotactic Body Radiation TherapyChang E, Du J, Decker R, Yu J, Park H, Aneja S. Development and Validation of a Deep Learning Algorithm to Predict Radiation Pneumonitis and Esophagitis Following Lung Stereotactic Body Radiation Therapy International Journal Of Radiation Oncology • Biology • Physics 2020, 108: e14. DOI: 10.1016/j.ijrobp.2020.02.496.
  • Randomized phase II study of rituximab, methotrexate (MTX), procarbazine, vincristine, and cytarabine (R-MPV-A) with and without low-dose whole-brain radiotherapy (LD-WBRT) for newly diagnosed primary CNS lymphoma (PCNSL).Omuro A, DeAngelis L, Karrison T, Bovi J, Rosenblum M, Corn B, Correa D, Wefel J, Aneja S, Grommes C, Schaff L, Waggoner S, Lallana E, Werner-Wasik M, Iwamoto F, Robinson T, Donnelly E, Struve T, Won M, Mehta M. Randomized phase II study of rituximab, methotrexate (MTX), procarbazine, vincristine, and cytarabine (R-MPV-A) with and without low-dose whole-brain radiotherapy (LD-WBRT) for newly diagnosed primary CNS lymphoma (PCNSL). Journal Of Clinical Oncology 2020, 38: 2501-2501. DOI: 10.1200/jco.2020.38.15_suppl.2501.
  • 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 2019, 38: 1304-1311. PMID: 31815574, DOI: 10.1200/jco.19.02031.
  • 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.
  • 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.
  • Upfront Stereotactic Radiosurgery in the Treatment of Brain Metastasis from Small Cell Lung CancerMiccio J, Miccio J, Johnson S, Johnson S, Jairam V, Yu J, Yu J, Hansen J, Aneja S, An Y, Decker R, Omay S, Chiang V, Park H, Park H. Upfront Stereotactic Radiosurgery in the Treatment of Brain Metastasis from Small Cell Lung Cancer International Journal Of Radiation Oncology • Biology • Physics 2019, 105: e81-e82. DOI: 10.1016/j.ijrobp.2019.06.2348.
  • External Validation and Radiologist Comparison of a Deep Learning Model (DLM) to Identify Extranodal Extension (ENE) in Head and Neck Squamous Cell Carcinoma (HNSCC) with Pretreatment Computed Tomography (CT) ImagingKann B, Hicks D, Payabvash S, Mahajan A, Gupta V, Burtness B, Husain Z, Aneja S. External Validation and Radiologist Comparison of a Deep Learning Model (DLM) to Identify Extranodal Extension (ENE) in Head and Neck Squamous Cell Carcinoma (HNSCC) with Pretreatment Computed Tomography (CT) Imaging International Journal Of Radiation Oncology • Biology • Physics 2019, 105: s71. DOI: 10.1016/j.ijrobp.2019.06.525.
  • Impact of Tissue Heterogeneity Correction on Stereotactic Radiosurgery of Acoustic NeuromasWelch G, Tien C, Chiang V, Yu J, Hansen J, Aneja S. Impact of Tissue Heterogeneity Correction on Stereotactic Radiosurgery of Acoustic Neuromas International Journal Of Radiation Oncology • Biology • Physics 2019, 105: e785. DOI: 10.1016/j.ijrobp.2019.06.749.
  • Deep Neural Networks for Radiographic Risk Stratification of GliomasGao S, Kann B, Bindra R, Contessa J, Yu J, Aneja S. Deep Neural Networks for Radiographic Risk Stratification of Gliomas International Journal Of Radiation Oncology • Biology • Physics 2019, 105: s121. DOI: 10.1016/j.ijrobp.2019.06.092.
  • Comparative Effectiveness of SBRTAneja S, Kumar R, Yu J. Comparative Effectiveness of SBRT 2019, 415-424. DOI: 10.1007/978-3-030-16924-4_34.
  • Career Enrichment Opportunities at the Scientific Frontier in Radiation OncologyThompson 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.
  • 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 2019, 33: 46-53. PMID: 30784028.
  • Successful Identification of Head and Neck Cancer (HNC) Nodal Metastasis (NM) and Extranodal Extension (ENE) Using Deep Learning Neural NetworksKann B, Aneja S, Loganadane G, Kelly J, Smith S, Decker R, Yarbrough W, Malhotra A, Burtness B, Husain Z. Successful Identification of Head and Neck Cancer (HNC) Nodal Metastasis (NM) and Extranodal Extension (ENE) Using Deep Learning Neural Networks International Journal Of Radiation Oncology • Biology • Physics 2018, 102: s60. DOI: 10.1016/j.ijrobp.2018.06.169.
  • Artificial Intelligence in Radiation Oncology ImagingThompson 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.
  • Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural NetworksKann 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: 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 2018, 129: 421-426. PMID: 29907338, PMCID: PMC9620952, DOI: 10.1016/j.radonc.2018.05.030.
  • The Future of Artificial Intelligence in Radiation OncologyThompson 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.
  • MRI-Ultrasound Fusion Targeted Biopsy of Prostate Imaging Reporting and Data System Version 2 Category 5 Lesions Found False-Positive at Multiparametric Prostate MRI.Sheridan AD, Nath SK, Aneja S, Syed JS, Pahade J, Mathur M, Sprenkle P, Weinreb JC, Spektor M. MRI-Ultrasound Fusion Targeted Biopsy of Prostate Imaging Reporting and Data System Version 2 Category 5 Lesions Found False-Positive at Multiparametric Prostate MRI. American Journal Of Roentgenology 2018, 210: w218-w225. PMID: 29489409, DOI: 10.2214/ajr.17.18680.
  • 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. American Journal Of Roentgenology 2017, 210: 347-357. PMID: 29112469, DOI: 10.2214/ajr.17.18516.
  • Deep Neural Network to Predict Local Failure Following Stereotactic Body Radiation Therapy: Integrating Imaging and Clinical Data to Predict OutcomesAneja S, Shaham U, Kumar R, Pirakitikulr N, Nath S, Yu J, Carlson D, Decker R. Deep Neural Network to Predict Local Failure Following Stereotactic Body Radiation Therapy: Integrating Imaging and Clinical Data to Predict Outcomes International Journal Of Radiation Oncology • Biology • Physics 2017, 99: s47. DOI: 10.1016/j.ijrobp.2017.06.120.
  • Annual Facility Treatment Volume and Patient Survival for Mycosis Fungoides and Sézary SyndromeKann B, Park H, Yeboa D, Aneja S, Girardi M, Foss F, Roberts K, Wilson L. Annual Facility Treatment Volume and Patient Survival for Mycosis Fungoides and Sézary Syndrome International Journal Of Radiation Oncology • Biology • Physics 2017, 99: e430. DOI: 10.1016/j.ijrobp.2017.06.1632.
  • Patterns of Care and Outcomes of Patients With Breast Cancer Who Refuse Recommended TherapyJohnson S, An Y, Kelly J, Aneja S, Mougalian S, Young M, Moran M, Evans S. Patterns of Care and Outcomes of Patients With Breast Cancer Who Refuse Recommended Therapy International Journal Of Radiation Oncology • Biology • Physics 2017, 99: e20-e21. DOI: 10.1016/j.ijrobp.2017.06.639.
  • Health-Related Quality of Life in Elderly Breast Cancer Patients Undergoing Breast ReconstructionAneja S, Yu J. Health-Related Quality of Life in Elderly Breast Cancer Patients Undergoing Breast Reconstruction International Journal Of Radiation Oncology • Biology • Physics 2016, 96: s206. DOI: 10.1016/j.ijrobp.2016.06.513.
  • The Effect of Marital Status on Health-Related Quality of Life in Elderly Patients Undergoing Radiation TherapyAneja S, Yu J. The Effect of Marital Status on Health-Related Quality of Life in Elderly Patients Undergoing Radiation Therapy International Journal Of Radiation Oncology • Biology • Physics 2016, 96: s161. DOI: 10.1016/j.ijrobp.2016.06.389.
  • Minimally Invasive Surgery and External Beam Radiation Therapy Selection for Prostate Cancer Varies Significantly by Health Insurance StatusBledsoe T, Park H, Rutter C, Aneja S, Yu J. Minimally Invasive Surgery and External Beam Radiation Therapy Selection for Prostate Cancer Varies Significantly by Health Insurance Status International Journal Of Radiation Oncology • Biology • Physics 2015, 93: e358-e359. DOI: 10.1016/j.ijrobp.2015.07.1461.
  • The Effect of Margin Status and Radiation Therapy on Survival in Adult Retroperitoneal Soft Tissue SarcomasStahl J, Corso C, An Y, Aneja S, Rutter C, Park H, Mancini B, Yeboa D, Lester-Coll N, Han D, Roberts K. The Effect of Margin Status and Radiation Therapy on Survival in Adult Retroperitoneal Soft Tissue Sarcomas International Journal Of Radiation Oncology • Biology • Physics 2015, 93: e638. DOI: 10.1016/j.ijrobp.2015.07.2175.
  • Historical trends of radiotherapy use in prevalent malignancies over 38 years in SEERYeboa D, Aneja S, Montana G, Roberts K, Yu J. Historical trends of radiotherapy use in prevalent malignancies over 38 years in SEER Journal Of Radiation Oncology 2015, 4: 11-17. DOI: 10.1007/s13566-015-0182-y.
  • Historical Trends of Radiation Therapy Use in Prevalent MalignanciesYeboa D, Roberts K, Aneja S, Yu J. Historical Trends of Radiation Therapy Use in Prevalent Malignancies International Journal Of Radiation Oncology • Biology • Physics 2014, 90: s599-s600. DOI: 10.1016/j.ijrobp.2014.05.1796.
  • PET Predicts Survival Following Stereotactic Body Radiation Therapy for Non-Small Cell Lung CancerMancini B, Giacalone N, Rutter C, Aneja S, Decker R, Husain Z. PET Predicts Survival Following Stereotactic Body Radiation Therapy for Non-Small Cell Lung Cancer International Journal Of Radiation Oncology • Biology • Physics 2013, 87: s11-s12. DOI: 10.1016/j.ijrobp.2013.06.036.
  • A Minimum Tumor to Spinal Cord Distance of 3-4 mm Is Needed for Optimal Planning of Spine SBRTPicone J, Deng J, Aneja S, Kim J, Husain Z. A Minimum Tumor to Spinal Cord Distance of 3-4 mm Is Needed for Optimal Planning of Spine SBRT International Journal Of Radiation Oncology • Biology • Physics 2013, 87: s733. DOI: 10.1016/j.ijrobp.2013.06.1943.
  • Long-term Survivors After Treatment of Intracranial Metastases With RadiosurgeryDosoretz A, Aneja S, Contessa J, Bindra R, Chiang V, Yu J. Long-term Survivors After Treatment of Intracranial Metastases With Radiosurgery International Journal Of Radiation Oncology • Biology • Physics 2013, 87: s276. DOI: 10.1016/j.ijrobp.2013.06.721.
  • Using Skin Dose Parameters Predicts Low Skin Toxicity in a Phase 2 Trial of Multiple Dwell Position Balloon-Based Brachytherapy for Partial Breast IrradiationNath S, Chen Z, Rowe B, Blitzblau R, Aneja S, Grube B, Horowitz N, Weidhaas J. Using Skin Dose Parameters Predicts Low Skin Toxicity in a Phase 2 Trial of Multiple Dwell Position Balloon-Based Brachytherapy for Partial Breast Irradiation International Journal Of Radiation Oncology • Biology • Physics 2013, 87: s213. DOI: 10.1016/j.ijrobp.2013.06.551.
  • A 35-year History of Radiation Therapy Utilization Across the United StatesAneja S, Roberts K, Wilson L, Haffty B, Montana G, Yu J. A 35-year History of Radiation Therapy Utilization Across the United States International Journal Of Radiation Oncology • Biology • Physics 2012, 84: s546-s547. DOI: 10.1016/j.ijrobp.2012.07.1457.
  • The Funding of Phase III Clinical Trials Examining Radiation Compared With That of Other ModalitiesLloyd S, Buscariollo D, Gross C, Makarov D, Yu J, Aneja S. The Funding of Phase III Clinical Trials Examining Radiation Compared With That of Other Modalities International Journal Of Radiation Oncology • Biology • Physics 2012, 84: s44-s45. DOI: 10.1016/j.ijrobp.2012.07.326.
  • The Influence of Regional Health System Resources on Receipt of Postoperative Radiation Therapy for Glioblastoma MultiformeAneja S, Yu J. The Influence of Regional Health System Resources on Receipt of Postoperative Radiation Therapy for Glioblastoma Multiforme International Journal Of Radiation Oncology • Biology • Physics 2012, 84: s265. DOI: 10.1016/j.ijrobp.2012.07.691.
  • Radiation therapy in the management of unilesional primary cutaneous T‐cell lymphomasChan D, Aneja S, Honda K, Carlson S, Yao M, Katcher J, Cooper K. Radiation therapy in the management of unilesional primary cutaneous T‐cell lymphomas British Journal Of Dermatology 2012, 166: 1134-1137. PMID: 22059744, DOI: 10.1111/j.1365-2133.2011.10728.x.
  • The Influence of Regional Radiation Oncologist and Urologist Capacities on Treatment Choice for Prostate CancerAneja S, Gross C, Makarov D, Roberts K, Yu J. The Influence of Regional Radiation Oncologist and Urologist Capacities on Treatment Choice for Prostate Cancer International Journal Of Radiation Oncology • Biology • Physics 2011, 81: s552. DOI: 10.1016/j.ijrobp.2011.06.869.
  • TRENDS IN THE GEOGRAPHIC DISTRIBUTION OF THE CARDIOVASCULAR WORKFORCEAneja S, Ross J, Rodgers G, Matsumoto M, Wang Y, Bernheim S, Krumholz H. TRENDS IN THE GEOGRAPHIC DISTRIBUTION OF THE CARDIOVASCULAR WORKFORCE Journal Of The American College Of Cardiology 2011, 57: e1284. DOI: 10.1016/s0735-1097(11)61284-9.
  • Radiation oncologist density and prostate cancer mortality.Aneja S, Yu J. Radiation oncologist density and prostate cancer mortality. Journal Of Clinical Oncology 2011, 29: 72-72. DOI: 10.1200/jco.2011.29.7_suppl.72.
  • Radiation oncologist density and esophageal cancer mortality.Aneja S, Yu J. Radiation oncologist density and esophageal cancer mortality. Journal Of Clinical Oncology 2011, 29: 116-116. DOI: 10.1200/jco.2011.29.4_suppl.116.
  • Radiation oncologist density and pancreatic cancer mortality.Aneja S, Yu J. Radiation oncologist density and pancreatic cancer mortality. Journal Of Clinical Oncology 2011, 29: 350-350. DOI: 10.1200/jco.2011.29.4_suppl.350.

Clinical Trials

ConditionsStudy Title
COVID-19 Inpatient; COVID-19 OutpatientLong Covid: Understanding Immune, Symptom, and Treatment Experiences Nationwide (LISTEN Study)
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