AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions

Yanhua Duan; Jiyong Wang; Puyu Wu; Yan Shao; Hua Chen; Hao Wang; Hongbin Cao; Hengle Gu; Aihui Feng; Ying Huang; Zhenjiong Shen; Yang Lin; Qing Kong; Jun Liu; Hongxuan Li; Xiaolong Fu; Zhangru Yang; Xuwei Cai; Zhiyong Xu

doi:10.1016/j.ijrobp.2023.12.001

AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions

Int J Radiat Oncol Biol Phys. 2023 Dec 29:S0360-3016(23)08239-1. doi: 10.1016/j.ijrobp.2023.12.001. Online ahead of print.

Authors

Yanhua Duan¹, Jiyong Wang², Puyu Wu³, Yan Shao⁴, Hua Chen⁴, Hao Wang⁴, Hongbin Cao⁵, Hengle Gu⁴, Aihui Feng¹, Ying Huang¹, Zhenjiong Shen⁴, Yang Lin⁴, Qing Kong⁶, Jun Liu⁴, Hongxuan Li⁴, Xiaolong Fu⁴, Zhangru Yang⁷, Xuwei Cai⁸, Zhiyong Xu⁹

Affiliations

¹ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China.
² Shanghai Pulse Medical Technology Inc, Shanghai, China.
³ Verisk Information Technology Ltd, Shanghai, China.
⁴ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Department of Radiation Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶ Institute of Modern Physics, Fudan University, Shanghai, China.
⁷ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: aries8703@163.com.
⁸ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: birdhome2000@163.com.
⁹ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: xzyong12vip@sina.com.

PMID: 38159780
DOI: 10.1016/j.ijrobp.2023.12.001

Abstract

Purpose: Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy.

Methods and materials: We retrospectively collected data from 530 patients with esophageal cancer, including single-target and simultaneous integrated boost prescriptions, for model building. The proposed Asymmetric ResNeSt (AS-NeSt) model features novel 3-dimensional (3D) ResNeSt blocks and an asymmetrical architecture. We constructed a loss function targeting global and local doses and validated the model's performance against existing alternatives. Model-assisted experiments were used to validate its clinical benefits.

Results: The AS-NeSt model maintained an absolute prediction error below 5% for each dosimetric metric. The average Dice similarity coefficient for isodose volumes was 0.93. The model achieved an average relative prediction error of 2.02%, statistically lower than Hierarchically Densely Connected U-net (4.17%), DoseNet (2.35%), and Densely Connected Network (3.65%). It also demonstrated significantly fewer parameters and shorter prediction times. Clinically, the AS-NeSt model raised physicians' ability to accurately preassess appropriate treatment methods before planning from 95.24% to 100%, reduced planning time by over 61% for junior dosimetrists and 52% for senior dosimetrists, and decreased both inter- and intra-dosimetrist discrepancies by more than 50%.

Conclusions: The AS-NeSt model, developed with innovative 3D ResNeSt blocks and an asymmetrical encoder-decoder structure, has been validated using clinical esophageal cancer patient data. It accurately predicts 3D dose distributions for various prescriptions, including simultaneous integrated boost, showing potential to improve the management of esophageal cancer treatment in a clinical setting.