Development and validation of an AI-based lung lobe auto-contouring tool using radiation therapy planning free-breathing images.

[BACKGROUND] Pulmonary toxicity can occur during radiation therapy of the lungs. Dose metrics evaluated at the lobar level can improve the ability to predict toxicity. Contouring lung lobes is challenging and time consuming. Currently there are limited dosimetry studies evaluating the dose to lung lobes. The purpose of this work was to develop and validate an artificial intelligence (AI) lung lobe auto-contouring algorithm using radiation therapy planning images.

[MATERIALS AND METHODS] Fifty lung cancer patients from two institutions were analyzed, and a clinician contoured all five lung lobes [left upper lobe (LUL), left lower lobe (LLL), right upper lobe (RUL), right middle lobe (RML), and right lower lobe (RLL)] on the free-breathing computed tomography data set. The AI model used a residual 3D U-Net and trained using the expert lobe contours for forty patients. Validation was carried out by comparing expert lobe contours on ten patients against AI-based lobe contours using dice similarity coefficients (DSC).

[RESULTS] The AI-based model showed good agreement with expert contours with overall DSC of 0.93 (range of 0.78-0.97). The DSC were 0.95 (0.97-0.91), 0.92 (0.96-0.85), 0.94 (0.97-0.87), 0.88 (0.93-0.78), and 0.94 (0.96-0.91), for the LUL, LLL, RUL, RML, and RLL, respectively.

[CONCLUSIONS] This work presents a validation of AI-based lung lobe contours on free-breathing data and shows good agreement with expert contours.