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. 2025 Apr;32(4):1895-1905.
doi: 10.1016/j.acra.2025.01.012. Epub 2025 Jan 26.

Prediction of Radiation Therapy Induced Cardiovascular Toxicity from Pretreatment CT Images in Patients with Thoracic Malignancy via an Optimal Biomarker Approach

Mujun Long  1 , Mostafa Alnoury  2 , Jayaram K Udupa  2 , Yubing Tong  2 , Caiyun Wu  2 , Nicholas Poole  2 , Sutirth Mannikeri  2 , Bonnie Ky  3 , Steven J Feigenberg  4 , Jennifer W Zou  4 , Shannon O'Reilly  4 , Drew A Torigian  5
Affiliations

Prediction of Radiation Therapy Induced Cardiovascular Toxicity from Pretreatment CT Images in Patients with Thoracic Malignancy via an Optimal Biomarker Approach

Mujun Long et al. Acad Radiol. 2025 Apr.

Abstract

Rationale and objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.

Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT. Manual segmentations of 10 cardiovascular objects of interest were performed on pre-treatment non-contrast-enhanced CT simulation images in 125 patients with thoracic malignancy (41 who developed cardiotoxicity and 84 who did not after RT). 1078 features describing morphology, image intensity, and texture for each of these objects were extracted and the top 5 features among them that were most uncorrelated and showed the best ability to discriminate between cardiotoxicity/ no cardiotoxicity were determined. The best combination among all possible combinations among these 5 features that yielded the highest accuracy of prediction on a training data set was selected, an SVM classifier was then trained on this combination, and tested for prediction accuracy on an independent data set. Prediction accuracy was quantified for the optimal features derived from each object.

Results: The best feature combination based on 5 CT-based features derived from the left ventricle had the highest testing prediction accuracy of 0.88 among all objects. Prediction accuracies over all objects ranged from 0.76-0.88. Heart, Left Atrium, Aortic Arch, Thoracic Aorta, and Descending Thoracic Aorta showed the next best accuracy of 0.84. Most optimal features were texture properties based on the co-occurrence matrix.

Conclusion: It is feasible to predict future cardiotoxicity following RT with high accuracy in individual patients with thoracic malignancy from available pre-treatment CT images via machine learning techniques.

Keywords: Cardio-oncology; Cardiotoxicity; Computed tomography (CT); Optimal biomarker (OBM) method; Radiation oncology; Radiomics; Thoracic cancer.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bonnie Ky reports financial support was provided by National Heart Lung and Blood Institute. Drew A. Torigian reports a relationship with University of Pennsylvania that includes: employment and funding grants. Conflict of interest statement for all authors: BK reports support for the present manuscript from NHLBI R01 HL 118018, Thalheimer Center for Cardio-Oncology, and Abramson Cancer Center Radiation Oncology Translational Center of Excellence; other grants or contracts in past 36 months from NHLBI, American Heart Association, and Pfizer; consulting fees from Pfizer, Bristol Myers Squibb, and Roche; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Uptodate; support for attending meetings and/or travel from AACR; leadership or fiduciary role in American College of Cardiology. JWZ reports other grants or contracts in past 36 months from 1-R01-HL-148272–01A1, 1R01HL152707–01, 1-P01-CA-257904–01A1, 2U24CA180803–09, 5U10CA180868–09, and Varian grant. DAT reports support for the present manuscript from the Penn Cardio-Oncology Center of Excellence pilot grant; co-founder of Quantitative Radiology Solutions LLC. ML, MA, JU, YT, CW, NP, SM, SJF, and SO report no conflicts of interest. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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