Near-infrared spectroscopy combined with machine learning for plasma-based discriminant diagnosis of malignant mesothelioma: a retrospective study.

This study developed a novel, non-invasive platform integrating near-infrared spectroscopy (NIRS) with machine learning (ML) to address the critical clinical challenge of misdiagnosing malignant mesothelioma (MM). We analyzed plasma samples from 99 individuals (29 MM, 41 lung cancer (LC), and 29 healthy controls). A support vector machine () model perfectly discriminated MM from LC (area under the curve (AUC) = 0.827), while a partial least squares () model differentiated MM from healthy control (HC) with high accuracy (AUC = 1.0). Despite the highly promising results, this single-center study is however limited by a small sample size, inherent to the rarity of MM and the associated difficulties in patient recruitment. Our findings demonstrate the potential of the NIRS-ML platform as a highly accurate tool for improving MM diagnosis and discriminant diagnosis, meriting further validation in larger cohorts.