Improving Radiotherapy Workflow Through Implementation of Delineation Guidelines & AI-Based Annotation

Abstract

Incidence of breast cancer is increasing as the number of radiotherapy treatments. Delineation of target volumes and organs at risk (OARs) is time-consuming and suffers from heterogeneity. New guidelines have emerged, towards standardizing the process and improving treatment outcomes. The objective of this effort is twofold: assess the benefit of these guidelines and the added value of AI-driven delineation methods in terms of quality and resources gain. A CE-marked solution for automatic delineation of 80+ organs at risk harnessing a unique combination of anatomically preserving and deep learning delineation concept was developed. Using transfer learning the models were re-trained according to the latest ESTRO guidelines, through the integration of 256 cases randomly selected from HYPOG-01 trial. One hundred unseen cases were selected for evaluation: half were delineated based on the ESTRO guidelines (C1) and 50 cases were delineated before guidelines implementation (C2). For each case, automatic delineations (AD) were generated and blended with the ones corresponding to the experts for qualitative and independent evaluation. Overall, 33% of AD structures, 33% manual structures from C1 and 33% manual structures from C2 were scored by 4 radiation oncologist breast experts as A for “No correction required”, B for “Minor correction required” and C for “Major corrections required”. Correction effort towards moving AD to clinically-acceptable target volumes and OARs (heart, lungs, spinal cord, esophagus and thyroid) were measured in C2 by one expert. Assessing benefit of guidelines, significant gain was observed on expert delineations between C1 & C2. Some OARs were not delineated before guidelines implementation such as thyroid in 95% of C2 cases. The delineations of experts were assessed clinically acceptable (A+B) for 93% of C1 cases, while the percentage ramped down to 75% in C2. In terms of expert versus AI, 93% of the automated delineations in C1 & C2 were considered as clinically acceptable (A: 49%; B: 44%), reaching human expertise. All target volumes were better scored with AD (92% and 94% of A+B for breast and nodes for AD vs. 86% and 87% respectively for manual delineations). Spinal cord and lungs were better scored using AD (94% and 96% respectively of A) than manual delineation (76% and 91%). On the contrary, 35% of the AD brachial plexus required major corrections (13% for the manual ones). The mean time to correct an AD case was 2.6±1.9 min (4.3±1.6 min for cases with nodal treatment; 1.8±1.0 min without). This systematic, blinded, random evaluation suggests that using AD in breast cancer has high potential for delineation guidelines propagation, homogenization of practices and time saving. Only minor corrections were required, showing the clinical relevance of the developed software. Evaluation of dosimetric impact of AD is on-going on C2 cohort to validate its major interest in clinical practice.