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Abstract <p>This paper presents a neural network-based algorithm for Clinical Target Distribution (CTD) identification integrated within a proton therapy treatment planning system, with validation against simulated MRI-derived ground truth data. The proposed CTD methodology demonstrates significant improvements in organ-at-risk (OAR) preservation, achieving a mean dose reduction of 29.3% to critical structures while maintaining robust target coverage (98.2%).</p>

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Neural-Network Identification of Clinical Target Distribution

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • S. N. Dima

摘要

Abstract

This paper presents a neural network-based algorithm for Clinical Target Distribution (CTD) identification integrated within a proton therapy treatment planning system, with validation against simulated MRI-derived ground truth data. The proposed CTD methodology demonstrates significant improvements in organ-at-risk (OAR) preservation, achieving a mean dose reduction of 29.3% to critical structures while maintaining robust target coverage (98.2%).