Development of a Predictive Model for Heart Dose in Left Breast Radiotherapy Using Geometric Analysis

Sanat Sharma

Development of a Predictive Model for Heart Dose in Left Breast Radiotherapy Using Geometric Analysis

Authors: Sanat Sharma

Published: 2025-04-28 17:22:49.223

Abstract

This paper presents the development of a predictive model to correlate the mean heart dose (MHD) in left breast radiotherapy with treatment geometry, specifically aiming to minimize cardiac complications due to heart proximity. Given the importance of constraining MHD, we propose a methodology to quantify geometric arrangements using the Expansion Intersection Histogram (EIH) method. This approach involves progressive isotropic expansions of the target volume and mapping the overlap with the heart. From the resulting EIH graph, two key parameters—separation (S) and wrapping (W)—are derived, alongside the omolateral breast volume (OBV), which serve as inputs for a multivariate linear regression model. The study includes data from 19 breast cancer patients who received a treatment course of 15 fractions, with a breast dose of 40.5 Gy and a 48 Gy simultaneous integrated boost (SIB) planned via volumetric modulated arc therapy (VMAT). Descriptive statistics for the model parameters showed mean values of 1.21±0.41 cm (S), 8.25±3.33 %/cm (W), and 708.13±388.64 cc (OBV), while the MHD averaged 3.25±0.78 Gy. The regression model demonstrated a high R² value of 0.9, with significant coefficients for S, W, and OBV. The results suggest that the developed model provides a reliable method for predicting MHD based on treatment geometry, offering potential for better heart dose management in radiotherapy.

Keywords

Radiotherapy, Left breast cancer, Mean heart dose (MHD), Treatment geometry, Expansion Intersection Histogram (EIH), Volumetric modulated arc therapy (VMAT), Wrapping (W), Omolateral breast volume (OBV).

References

Darby SC, Ewertz M, McGale P (2013) Risk of ischemic heart disease in women after radiotherapy for breast cancer. N Engl J Med 368: 987-98.
Hong JC, Rahimy E, Gross CP (2018) Radiation dose and cardiac risk in breast cancer treatment: An analysis of modern radiation therapy including community settings Practical Radiation Oncology 8: e79-e86
Berg M, Lorenzen EL, Jensen I (2018) The potential benefits from respiratory gating for breast cancer patients regarding target coverage and dose to organs at risk when applying strict dose limits to the heart: results from the DBCG HYPO trial. 2018, Acta Oncologica 57: 113-9.
Drost L, Yee C, Lam H (2018) Systematic Review of Heart Dose in Breast Radiotherapy. 2018. Clinical Breast Cancer, 5: e819-24
Pawełoszek, I., Kumar, N., & Solanki, U. (2022). Artificial intelligence, digital technologies and the future of law. Futurity Economics & Law, 2(2), 24–33. https://doi.org/10.57125/FEL.2022.06.25.03
Vinay Singh, Alok Agggarwal and Narendra Kumar: “A Rapid Transition from Subversion to Git: Time, Space, Branching, Merging, Offline commits & Offline builds and Repository aspects, Recent Advances in computers Sciences and communications, Recent Advances in Computer Science and Communications, Bentham Science, vol 15 (5) 2022 pp 0-8, (DOI : 10.2174/2666255814666210621121914)June 2021
Eldredge-Hindy H, Lockamy V, Crawford A (2015) Active Breathing Coordinator reduces radiation dose to the heart and preserves local control in patients with left breast cancer: Report of a prospective trial. 2015, Practical Radiation Oncology 5: 4-10.

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How to Cite

Sanat Sharma, (2025-04-28 17:22:49.223). Development of a Predictive Model for Heart Dose in Left Breast Radiotherapy Using Geometric Analysis. JANOLI International Journal of Artificial Intelligence and its Applications, Volume hJAEiqNzZqWjtpPaXKzr, Issue 2.

ISSN: 3048-6815

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Artificial Intelligence and its Applications ( JIJAIA )