We strive to share our work with the scientific and academic communities. Check out some of our recent research below.

An AI Model to Predict Acute Hospitalization after Radiation Therapy

November 1, 2022

W.D. Lindsay, R. Wilder, M. Botyrius, C. Harrill, C.G. Berlind, L. Pugliese, J. Pinsky, A.C. Riegel, S. Garza, J.M. Herman, L. Potters

In cancer patients undergoing RT, a machine learning model identified patients at risk of 30-day hospitalization. Predictive analytics may be a key tool to help providers identify high-risk patients and optimize interventions, while improving quality and value of care.

Impact of the COVID-19 Pandemic on Radiotherapy Treatment Volume and Treatment Intent in Multicenter New York Area Institution

October 24, 2021

William Lindsay, PhD; Chris Ahern, PhD; Rajiv Sharma, MD; Beatrice Bloom, MD; Louis Potters, MD; Bhupesh Parashar MD

Oncora and Northwell abstract accepted to ASTRO 2021, looking retrospectively to measure and characterize the impact of Covid-19 pandemic on radiation therapy treatments.

A methodology for the extraction and analysis of real-world radiographic and electronic medical record data to reconstruct treatment arms of historical prostate cancer clinical trials.

June 5, 2021

William D. Lindsay, Christopher A. Ahern, Aaron Kamauu, Robert Wilder, Karen Chagin, Timmothy J. Taps

This project demonstrates the ability to extract and analyze RWD to identify patients matching eligibility criteria to four historical clinical trials in metastatic castration-resistant prostate cancer (mCRPC), and calculate outcome measures.

A Machine Learning Model Approach to Risk-Stratify Gastrointestinal (GI) Cancer Patients for Hospitalization and Mortality Outcomes

April 30, 2021

Kaitlin Christopherson, Prajnan Das, Chris Berlind, William Lindsay, Chris Ahern, Ben Smith, Ishwaria Subbiah, Eugene Koay, Albert Koong, Emma Holliday, Ethan Ludmir, Bruce Minsky, Cullen Taniguchi, Grace Smith

Oncora and MD Anderson teamed up to create a predictive model for unplanned hospitalization. Results were accepted for publication in the International Journal of Radiation Oncology. Together the team developed a ML model to identify GI patients at high risk of unplanned hospitalizations.

Gender-based Disparities in Receipt of Care and Survival in Malignant Pleural Mesothelioma

November 1, 2020

AR Barsky, CA Ahern, S Venigalla, V Verma, EJ Anstadt, CM Wright, EB Ludmir, CG Berlind, WD Lindsay, S Grover, KA Cengel, CB Simone II

Abstract accepted for publication in Clinical Lung Cancer Journal, looking into the National Cancer Database (NCDB) to assess patterns-of-care and overall survival (OS) among patients with MPM by gender.

Predicting Survival after Radiotherapy for Brain Metastases

October 26, 2020

W. D. Lindsay, A. Malik, C. A. Ahern, R. Wilder, C. G. Berlind, A. Goenka, L. Potters, and B. Parashar; Oncora Medical, Philadelphia, PA, Carnegie Mellon, Pittsburgh, PA, Northwell Health, Lake Success, NY.

Oncora and Northwell Health machine learning research selected for a special session poster presentation during ASTRO 2020.

Provider Engagement in Radiation Oncology Data Science: Workshop Report

August 5, 2020

Anshu K. Jain, Sanjay Aneja, Clifton D. Fuller, Adam P. Dicker, Caroline Chung, Erika Kim, Justin S. Kirby, Harry Quon, Clara J. K. Lam, William C. Louv, Chris Ahern

Oncology providers and Oncora Medical discuss big data and informatics playing an increasingly important role in radiation oncology clinical practice and research. Highlighted at the NCI cancer workshop.

Applying a machine learning approach to predict acute radiation toxicities for head and neck cancer patients

September 17, 2019

Reddy J, Lindsay W, Berlind C, Ahern C, Holmes A, Smith B, Phan J, Frank S, Gunn G, Rosenthal D, Morrison W, Garden A, Chronowski G, May L, Fuller C

Oncora and MD Anderson abstract was accepted as both press and oral presentation during the 2019 American Society of Radiation Oncology conference (ASTRO). The abstract focused on applying a machine learning techniques to predict acute radiation toxicities for head and neck cancer patients.

Improving quality through AI: Applying machine learning to predict unplanned hospitalizations after radiation

August 1, 2019

Christopherson K, Berlind C, Ahern C, Holmes A, Lindsay A, Smith B, Das P, Smith G

The department of Radiation Oncology at MD Anderson and Oncora abstract accepted as oral presentation at the 2019 ASCO Quality of Care Symposium in San Diego, CA. In addition to being one of the twelve abstracts accepted, this research also received a Merit Award from Conquer Cancer ASCO Foundation!

Oncora, MD Anderson poster presentation highlighted at the NCI Childhood Cancer Data Initiative Symposium

July 29, 2019

Holmes A, Ahern C, Berlind C, Smith B, Edwards T, Lindsay W, McGovern S

Oncora traveled to Washington DC to highlight the success of building a pediatric oncology research database with Oncora's point-of-care electronic data capture and automated data extraction software for the department of radiation oncology at MD Anderson. The 3 day symposium will bring together leaders to discuss the current issues and opportunities in childhood cancer research that can be alleviated through data initiatives.

Applications of AI to Real World Data in Radiation Oncology

July 25, 2019

Ahern C

Oncora data science team traveled to the NIH campus in Bethesda, MD to discuss current applications of AI to real world data in radiation oncology. The NCI State of Data Science in Radiation Oncology workshop brought together leaders from MD Anderson, Penn Medicine, Johns Hopkins, Yale Medicine, Sidney Kimmel Cancer Center and more

Oncora and radiation oncology leaders review the future impact of AI in radiation oncology

October 2, 2018

Thompson RF, Valdes G, Fuller C, Carpente C, Morin O, Aneja S, William L, Aerts H, Agrimson B, Deville C, Rosenthal S, Yu JB, Thomas CJ

Industry leaders from Yale, MD Anderson, OHSU, UCSF and Oncora consider implications of AI in radiation oncology for patients, providers, and the entire healthcare system.

Implementing an electronic data capture system to improve clinical workflow in a large academic radiation oncology practice

October 1, 2018

Pasalic D, Reddy J, Edwards T, Pan H, Smith B

Published in the Journal of Clinical Oncology, Brocade [before Oncora Patient Care] proves to be a reliable Web-based EDC tool that improves clinical documentation without disrupting clinical workflow. Moreover, Brocade has the advantage of capturing data in a structured manner that facilitates real-time analytics and outcome reporting.

Applying a machine learning approach to predict acute toxicities during radiation for breast cancer patients

October 1, 2018

Reddy J, Lindsay W, Berlind C, Ahern C, Smith B

Oncora heads to San Antonio, TX showcasing a successful application of a machine learning-based, precision oncology approach to accurately predict acute RT toxicities. Presentation will be delivered by Dr. Jay Reddy, a radiation oncologists at MD Anderson Department of Radiation Oncology.

Electronic data capture system, built by Oncora and MD Anderson, to be highlighted at ASTRO 2018

June 30, 2018

Pasalic D, Edwards T, Pan H, Herman J, Smith B

An abstract authored by MD Anderson was accepted for publication and presentation at ASTRO's annual meeting. The study emphasizes a prospective electronic data capture tool built in collaboration between Oncora and MD Anderson to improve workflow and research.

Evaluating linearity of risk functions across radiotherapy outcomes using deep learning

May 2, 2018

Ahern C, Pheiffer T, Berlind C, Lindsay W, Xiao Y, Simone CB

Oncora will present a second abstract at the 2018 annual AAPM conference, exploring radiotherapy outcomes using deep learning. This research was done in collaboration with the University of Pennsylvania and the University or Maryland School of Medicine

Identifying predictors of unplanned hospitalizations after radiation therapy using regularized survival models

May 1, 2018

Ahern C, Pheiffer T, Berlind C, Lindsay W, Xiao Y, Simone CB

Oncora will travel to Nashville, TN to present two accepted abstracts at the annual AAPM conference! The first abstract explored predictors of unplanned hospitalizations after radiotherapy.

Facility volume and postoperative outcomes for malignant pleural mesothelioma: A national cancer data base analysis

March 20, 2018

Verma V, Ahern C, Berlind C, Lindsay W, Grover S, Culligan J, Friedberg J, Simone CB

Accepted in Lung Cancer journal, Oncora's research discusses the postoperative outcomes and overall survival for malignant pleural mesothelioma by facility volume.

National cancer database report on pneumonectomy versus lung-sparing surgery for malignant pleural mesothelioma

November 1, 2017

Vivek V, Ahern C, Berlind C, Lindsay W, Sharma S, Shabason J, Culligan M, Grover S, Friedberg J, Simone CB

Oncora manuscript accepted in Journal of Thoracic Oncology focusing on Pneumonectomy versus Lung-Sparing Surgery for Malignant Pleural Mesothelioma.

Quantifying the potential impact of predictive analytics in radiation oncology

September 24, 2017

Lindsay W, Berlind C, Ahern C, Gabriel P, Simone CB

Oncora Medical recently showcased new research at the annual ASTRO conference in San Diego, CA discussing the potential impact of predictive analytics in radiation oncology.

The relative impact of clinical variables on radiotherapy outcome predictions

July 31, 2017

Ahern C, Berlind C, Lindsay W, Gabriel P, Simone CB

Oncora Medical traveled to Denver, CO to present a second accepted abstract at the annual American Association of Physicists in Medicine, exploring which clinical variables contribute the most to the prediction of different radiotherapy outcomes and practical uses for radiation oncologists.

Cross-decomposition methods for learning clinically relevant measures of patient similarity in oncology

July 30, 2017

Berlind C, Ahern C, Lindsay W, Gabriel P, Simone CB

Oncora Medical traveled to Denver, CO to present one of two accepted abstracts at the annual American Association of Physicists in Medicine, exploring which clinical variables contribute the most to the prediction of different radiotherapy outcomes and practical uses for radiation oncologists.

Radiation Oncology Health Information Technology: Is It Working For or Against Us?

June 30, 2017

Pan H, Mazur L, Martin N, Mayo C, Santanem L, Pawlicki T, Marks L, Smith B

Radiation oncology software, Brocade (now Oncora Patient Care), improves workflow at MD Anderson Cancer Center by reducing documentation time for breast radiation oncologists by 68%. Leaders in the radiation oncology field further address the need for improvements in HIT to improve clinical efficiency, reliability, cost effectiveness, provider satisfaction and patient safety to ultimately lead to better patient outcomes.

A single treatment site agnostic model to predict adverse events after radiation therapy

September 28, 2016

Lindsay W, Berlind C, Simone CB

Oncora Medical was recently in Boston to present new research comparing the predictive power of treatment site-specific and treatment site-agnostic models at the 58th Annual Meeting of American Society for Radiation Oncology (ASTRO). The abstract is published in ASTRO's official journal.

Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy

June 16, 2016

Lindsay W, Tobias J, Berlind C, Chidambaram C, Gabriel P, Gee J, Simone C

Oncora traveled to the NIH campus in Bethesda, MD to present research evaluating predictive models built from automatically extracted EMR data. With collaborators from Penn Medicine, Oncora compared the reliability of models employing ensemble methods against those using logistic regression.

Predictive modeling of mortality, tumor control, and normal tissue complications after sterotactic body radiotherapy for stage I non-small cell lung cancer

May 15, 2015

W. D. Lindsay, C. Berlind, J.C. Gee, and C. Simone;

An abstract authored by Oncora and Penn Medicine was accepted for publication at the 2015 Annual Meeting of the American Association of Physicists in Medicine.