Automatic lung dose painting for functional lung avoidance radiotherapy through multi-modality-guided dose prediction

Abstract

Objective. This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images. Approach. The proposed auto-planning algorithm consists of a novel MMDP model and a function-guided dose mimicking algorithm. The MMDP model features extracting complementary features from multi-modality images for predicting dose distributions close to FLART plans. An instance-weighting anatomy-to-function training strategy is tailored to enhance prediction accuracy. A function-guided voxel-wise dose mimicking algorithm is developed to convert predicted dose into FLART (MMDP-FLART) plans. We retrospectively collected data from 163 lung cancer patients across three institutions, comprising 114/28 cases for training/validation and 21 cases with SPECT ventilation (V) images for testing. Furthermore, we prospectively collected 33 cases with SPECT perfusion (Q) images for evaluation. MMDP-FLART plans were compared against conventional radiotherapy (ConvRT) and FLART plans manually created by senior clinicians. Main results. MMDP achieved accurate dose predictions, with median prediction errors for all assessed dose-volume histogram (DVH) metrics within ±1 Gy/±1%. The MMDP model reduced prediction absolute errors for functionally weighted mean lung dose (fMLD) by 12.77% compared to an anatomy-guided dose prediction model and the instance-weighting anatomy-to-function training strategy reduced prediction absolute errors for fMLD by 22.64%. Compared to manual ConvRT plans, MMDP-FLART plans effectively reduced fMLD by 0.80 Gy (11.9%, p < 0.01) and 0.46 Gy (6.0%, p < 0.01) on SPECT V and Q datasets respectively. Compared to manual FLART plans, MMDP-FLART plans exhibited lower and comparable fMLD on SPECT V and Q datasets respectively with lower dose to heart and esophagus. Significance. The MMDP model with instance-weighting anatomy-to-function training can achieve accurate dose prediction for FLART. The MMDP-based auto-planning algorithm can produce FLART plans leveraging voxel-wise lung function information from V/Q images. It shows promise in promoting FLART planning efficiency, consistency, and quality.