Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study

The Lancet, Oncology 2026 July [Link]

Kevin B W Groot Lipman, Rianne Wittenberg, Mateus de Oliveira Taveira, Illaa Smesseim, Alexander M Th Schmitz , Thierry N Boellaard 2, Kalina Chupetlovska, Mohamed A Abdelatty, Liliana Petrychenko, Neeraj Jain, Francesco M Arico, Valerio Pugliese, Robin Cornelissen, Ties A Mulders, Fenneke Zwierenga, Birgitta I Hiddinga, Mena Shenouda, Samuel G Armato 3rd, Sanjay Popat, Solange Peters, Susanne Roux, Caroline Zellweger, Alessandra Curioni Fontecedro, Thomas Frauenfelder, Thi Dan Linh Nguyen-Kim, Regina G H Beets-Tan, Paul Baas, Maurice Dungey, Amrita Bajaj, Dean Fennell, Renaud Tissier, Jacobus A Burgers, Cornedine J de Gooijer, Stefano Trebeschi

Abstract

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.

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.

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.