Paper Proffered Program Therapy Physics

Artificial Intelligence-Driven One-Click Automatic Treatment Planning for Functional Lung Avoidance Radiation Therapy: Clinical Implementation and Comprehensive Evaluation

Abstract
Purpose

To advance the clinical application of a fully automatic planning system for functional lung avoidance radiotherapy (AP-FLART) through clinical implementation and comprehensive evaluation.

Methods

AP-FLART integrates dosimetric score-based beam angle selection, multi-modality-guided dose prediction (MMDP), and function-guided dose mimicking. The system was implemented within the clinical treatment planning system RayStation. The MMDP model was pre-trained with 1,971 conventional lung radiotherapy (ConvRT) and FLART plans and then fine-tuned with 50 pairs of high-quality manual ConvRT/FLART plans. Clinical performance and potential benefits were assessed using a test dataset comprising 33 lung cancer patients who underwent SPECT ventilation or perfusion imaging and lung radiotherapy. Dosimetric metrics and normal tissue complication probabilities of automatic FLART plans generated by AP-FLART were compared with those of manual ConvRT and FLART plans created by an experienced planner. Additionally, three senior clinicians conducted blind reviews and comparisons of the automatic and manual FLART plans.

Results

Compared to manual ConvRT plans, automatic FLART plans significantly reduced the functionally weighted mean lung dose and high-function lung mean dose by 11.8% and 15.1%, respectively. Among FLART-benefiting patients, automatic FLART plans reduced the probability of grade ≥2 radiation pneumonitis by 6.25 percentage points (27%), while maintaining comparable probabilities for other side effects (all increases within 0.25 percentage points). Blinded reviews indicated that 87.9% of automatic FLART plans were clinically acceptable without modification, and 68.7% were rated as comparable (38.4%) or superior (30.3%) to manual FLART plans. Furthermore, AP-FLART reduced planning time from 2–3 hours for manual FLART planning to approximately 8 minutes.

Conclusion

A clinically viable, one-click auto-planning system for FLART (AP-FLART) has been successfully implemented and comprehensively evaluated. AP-FLART demonstrates substantial potential to enhance the efficiency, consistency, and quality of FLART planning, while reducing radiation-induced lung toxicity compared to the current clinical standard of lung radiotherapy.

People
Chunyu He, MDAuthors · Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital Chengcheng Fan, MDAuthors · Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital Bing Li, PhDAuthors · Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital Guangping Zeng, MScAuthors · Department of Radiation Oncology, Jinshazhou Hospital of Guangzhou University of Chinese Medicine Hong Ge, MDAuthors · Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital Qingrong Jackie Wu, PhDAuthors · Duke University Medical Center Yang Sheng, PhDAuthors · Duke University Medical Center Tianyu Xiong, MScPresenting Author · Department of Health Technology and Informatics, The Hong Kong Polytechnic University Zhi Chen, PhDAuthors · Department of Health Technology and Informatics, The Hong Kong Polytechnic University Yu-Hua Huang, PhDAuthors · Department of Health Technology and Informatics, The Hong Kong Polytechnic University Zongrui Ma, PhDAuthors · Department of Health Technology and Informatics, The Hong Kong Polytechnic University Vincent W. S. Leung, PhDAuthors · Department of Health Technology and Informatics, The Hong Kong Polytechnic University Ge RenAuthors · Department of Health Technology and Informatics, The Hong Kong Polytechnic University Jing Cai, PhDCorrespondings · Department of Health Technology and Informatics, The Hong Kong Polytechnic University

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