CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network

Jiaqi Cui; Zhengyang Jiao; Zhigong Wei; Xiaolin Hu; Yan Wang; Jianghong Xiao; Xingchen Peng

doi:10.3389/fonc.2022.875661

CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network

Front Oncol. 2022 Jul 18:12:875661. doi: 10.3389/fonc.2022.875661. eCollection 2022.

Authors

Jiaqi Cui¹, Zhengyang Jiao¹, Zhigong Wei², Xiaolin Hu³, Yan Wang¹, Jianghong Xiao⁴, Xingchen Peng²

Affiliations

¹ School of Computer Science, Sichuan University, Chengdu, China.
² Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
³ West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China.
⁴ Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

Abstract

Purpose: Current deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically.

Materials and methods: We developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients.

Results: Experimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas).

Conclusion: The proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT.

Keywords: GAN structure; deep learning; dose prediction; radiotherapy planning CT-scan; rectal cancer.