Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study.
Authors
Affiliations (22)
Affiliations (22)
- Cluster Imaging, Netherlands Cancer Institute, Amsterdam, Netherlands; Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands. Electronic address: [email protected].
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands.
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands.
- Department of Radiology, Kasr Al-Ainy Hospitals, Cairo University, Cairo, Egypt.
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands; Department of Radiodiagnosis, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands.
- Department of Pulmonary Medicine, Erasmus MC Cancer Institute, Rotterdam, Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.
- Department of Pulmonary Medicine, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Radiology, University of Chicago, Chicago, IL, USA.
- Lung Unit, The Royal Marsden Hospital NHS Foundation Trust and Institute of Cancer Research, London and Surrey, UK.
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
- ETOP IBCSG Partners Foundation, Bern, Switzerland.
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland.
- Medical Oncology, Cantonal Hospital Fribourg, University of Fribourg, Fribourg, Switzerland.
- Department of Radiology, Stadtspital Zurich Triemli, Zurich, Switzerland.
- The Netherlands Cancer Institute, Amsterdam, Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands; Maastricht Radiation Oncology Institute, Maastricht, Netherlands.
- Genetics, Genomics and Cancer Sciences, University of Leicester, Leicester, UK.
- Department of Radiology, University Hospitals of Leicester, Leicester, UK.
- Department of Biostatistics, Netherlands Cancer Institute, Amsterdam, Netherlands.
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands.
Abstract
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. 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 R<sup>2</sup> and surrogate threshold effect. 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 (R<sup>2</sup> 88% [95% CI 42-100]) than did mRECIST-based progression-free survival (R<sup>2</sup> 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. 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. 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.