A fully automated machine-learning-based workflow for radiation treatment planning in prostate cancer

The integration of artificial intelligence into radiotherapy planning for prostate cancer has demonstrated promise in enhancing efficiency and consistency. In this study, we assess the clinical feasibility of a fully automated machine learning (ML)-based "one-click" workflow that combines ML-based segmentation and treatment planning. The proposed workflow was designed to create a clinically acceptable radiotherapy plan within the inter-observer variation of conventional plans.

We evaluated the fully-automated workflow on five low-risk prostate cancer patients treated with external beam radiotherapy and compared the results with conventional optimized and inverse planned radiotherapy plans based on the contours of six different experienced radiation oncologists. Both qualitative and quantitative metrics were analyzed. Additionally, we evaluated the dose distribution of the ML-based and conventional radiation treatment plans on the different segmentations (manual vs. manual and manual vs. automation).

The automatic deep-learning segmentation of the target volume revealed a close agreement between the deep-learning based and expert contours referring to Dice Similarity- and Hausdorff index. However, the deep-learning based CTVs had a significantly smaller volume than the expert CTVs (47.1 cm3 vs. 62.6 cm³). The fully automated ML-based plans provide clinically acceptable dose coverage within the range of inter-observer variability observed in the manual plans. Due to the smaller segmentation of the CTV the dose coverage of the CTV and PTV (expert contours) were significantly lower than that of the manual plans.

Our study indicates that the tested fully automated ML-based workflow is clinically feasible and leads to comparable results to conventional radiation treatment plans. This represents a promising step towards efficient and standardized prostate cancer treatment. Nevertheless, in the evaluated cohort, auto segmentation was associated with smaller target volumes compared to manual contours, highlighting the necessity of improving segmentation models and prospective testing of automation in radiation therapy.