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This is a Observational study (also classified as: Literature Review).

August 21, 2025Open Access

Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics

SDSefika DincerVan Yüzüncü Yıl Üniversitesi MAMüge AkmansuGazi University OAOya AkyolGazi University

Key Points

The predictive composite model achieved a high AUC of 0.96 for identifying treatment-related cardiac events.
Heart-specific Troponin T levels were measured post-radiotherapy, with 14 ng/L as the critical cutoff for patient grouping.
This retrospective study utilized machine learning techniques, including gradient-boosted classification and feature selection methods.
Findings imply that integrating radiomics and dosiomics may enhance patient surveillance and treatment strategies in breast cancer.

Abstract

Background Personalized medicine has transformed disease management by focusing on individual characteristics, driven by advancements in genome mapping and biomarker discoveries. Objectives: This study aims to develop a predictive model for the early detection of treatment-related cardiac side effects in breast cancer patients by integrating clinical data, high-sensitivity Troponin-T (hs-TropT), radiomics, and dosiomics. The ultimate goal is to identify subclinical cardiotoxicity before clinical symptoms manifest, enabling personalized surveillance strategies. It is the first study to utilize heart-segmented dosiomics in breast cancer patients. Methods and Materials This retrospective study included clinical, dosimetric, radiomic, and dosiomic data from 42 women with localized breast cancer. Heart-specific Troponin T levels were measured 2–3 weeks post-radiotherapy, with 14 ng/L as the cutoff. Patients were grouped on this threshold to identify potential treatment-related cardiac events. Radiomics and dosiomics were extracted using PyRadiomics. Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. Feature selection was conducted using gradient-boosted recursive feature elimination. Model performance is assessed by the area under the curve (AUC). Results A total of 111 dosiomic and 119 radiomic features were extracted per patient. The highest predictive accuracy was achieved using clinical, dosiomic, and radiomic parameters (validation cohort-AUC = 0.96), outperforming the clinical + dosimetric model (validation cohort-AUC = 0.67). Permutation tests confirmed the non-randomness of these two models results (p 0.05). Cross-validation indicated that the clinical + dosiomic + radiomic model had a fair-to-good generalizable performance (mean AUC = 80.33 ± 21%). Discussion This study may demonstrate that radiomics and dosiomics provide superior predictive capabilities for cardiac events in breast cancer patients compared to traditional parameters.

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