S32 Clinical readiness of AI tumour volume quantification in mesothelioma: results of the meso-Q study

Abstract

<h3>Objectives</h3> We report on the clinical readiness of an AI tool for volumetric tumour quantification in pleural mesothelioma (PM) developed in the multicentre Mesothelioma detection and Quantification study, part of the PREDICT-Meso International Accelerator network. <h3>Methods</h3> We pre-specified a primary endpoint of ‘clinical acceptability’, requiring ≥90% of AI segmentations to be scored as ‘Excellent’ or ‘Satisfactory’ on a 5-point Likert scale and assembled a retrospective cohort of CT scans and clinical data, describing 1851 patients from 9 UK hospitals. 706/1851 had full metadata; 414/1851 had follow-up CT scans following therapy. 3 sites were held out for validation and to assess generalisation. Correlation and agreement (human edited vs. AI) were assessed using Dice overlap, Intra-Class Correlation (ICC) and Bland-Altman limits. AI- and human-segmented tumour volume (TV) were used to stratify patient response and compared using concordance index. Baseline AI-generated TVs were used to stratify overall survival using Cox proportional hazards models. <h3>Results</h3> Following full annotation of 353 scans (~422 CT slices per scan; &gt;174,000 slices total), the primary objective was met. 90.8% of scans were deemed clinically acceptable in an external validation set of 98 scans from 9 different hospitals, with no performance difference in held-out scans, indicating good generalisation. 98% were considered ‘acceptable starting points’, needing no more than minor modifications to become clinically acceptable. The optimised network achieved Dice=0.95 and ICC=0.98, with non-significant mean bias of 28 cm3, suggesting edits did not meaningfully change the nature of the segmentation. Using mRECIST response cutoffs ([-30%,20%]), AI-derived TV outperformed human-segmented TV in 36 baseline-response CT pairs (concordance index=0.56 vs. 0.52). AI-derived TV stratified survival (414 paired CT scans) when dichotomised around the median volume (HR=0.68,p&lt;0.001) in Cox proportional hazards models. Agreement on segmentation quality was perfect (κ=1). Clinical acceptability appeared independent of CT manufacturer and age (p=0.1844,p=0.1335). <h3>Conclusion</h3> These data demonstrate the clinical readiness of a fully-automated AI tool for PM segmentation. The optimised network generalises well to CT scans acquired on different scanners across multiple centres. AI-generated TV outperforms human-generated TV for response assessment and has important prognostic implications. Ongoing work includes tuning of optimal volumetric thresholds for response assessment.

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