Abstract
METHODS
In this retrospective, multicentre study, we included 10 926 CT scans from 2080 patients from 14 cohorts. A subset totalling 1176 CT scans from routine care (Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital) and trial cohorts (INITIATE, NivoMes, PEMMELA, LUME-MESO, NVALT19, and MiST1 trials) was annotated by 12 radiologists and 1 pulmonologist, supplemented by 100 negative CT scans, to train a deep-learning segmentation model. Internal testing included 98 CT scans from independent international hospitals in LUME-MESO. External testing included data from the MEDUSA cohort (101 CT scans with radiologist-corrected segmentations) and two fully independent manual segmentation datasets from SAKK17/18 (22 CT scans) and the University of Chicago (15 CT scans). AI segmentations were evaluated through dice similarity coefficient (DSC) and normalised surface distance (NSD) at 3 mm. Progressive disease thresholds were derived using data from patients with multiple CT scans before first-line therapy or receiving only supportive care after first-line treatment (611 CT scans), and partial response thresholds from inter-reader variability (derived from 451 CT scans). ARTIMES was validated using data from eight clinical trials (4674 CT scans; 943 patients) and compared with modified Response Evaluation Criteria in Solid Tumors (mRECIST) using time-varying Cox proportional hazards models and trial-level surrogate endpoint analysis against overall survival using R2 and surrogate threshold effect.
BACKGROUND
Response evaluation in pleural mesothelioma is challenging because its crescent growth pattern is poorly captured by diameter-based criteria. We aimed to develop and validate artificial intelligence (AI)-assisted volumetric response criteria (ARTIMES) based on automated tumour segmentation and biologically derived thresholds.
FINDINGS
DSC was 94-95% in internal testing and 71-80% with manual segmentations. NSD was 98% and 81-93%, respectively. ARTIMES demonstrated superior patient-level prognostic performance compared with mRECIST (concordance index 0·83 [95% CI 0·79-0·87] vs 0·73 [0·66-0·80]; p=0·023) and detected progression a median of 5 weeks earlier (124 days [95% CI 115-126] vs 162 days [138-167]; p<0·0001). At the trial level, ARTIMES-based progression-free survival showed stronger correlation with overall survival (R2 88% [95% CI 42-100]) than did mRECIST-based progression-free survival (R2 6% [0-97]) and demonstrated a surrogate threshold effect at a progression-free survival hazard ratio of less than 0·82; no threshold was observed for mRECIST. Baseline AI-derived tumour volume independently predicted overall survival and outperformed T stage and WHO performance status.
INTERPRETATION
ARTIMES-based progression-free survival improves prognostic stratification and shows better trial-level surrogacy for overall survival compared with mRECIST-based progression-free survival. Pending prospective validation, ARTIMES could potentially facilitate a more reliable response evaluation in pleural mesothelioma.
FUNDING
Asbestos-Related Disease Section (SAGA) of the Dutch Society of Pulmonology and Tuberculosis (NVALT), Dutch Cancer Society, and Dutch Ministry of Health, Welfare and Sport.