Abstract 2507: Comparative assessment of data analysis methods to enable robust 10-second diagnosis of tissue pathologies with picosecond infrared laser mass spectrometry

Abstract A survey of clinical outcome data suggests surgical approaches of the central nervous system cancers must vary based on type of cancer, its aggressiveness, chance of recurrence and associated risks. This is usually informed by feedback from a neuropathologist on call wherein the accuracy of diagnosis depends highly on the experience and training level of the pathologist. As such, to deliver robust personalized resections, the subjectivity of intraoperative diagnosis must be reduced.Picosecond infrared laser mass spectrometry (PIRL-MS) generates a fingerprint of cancer-specific tissue lipids that can form the basis for non-subjective diagnosis in only 10 seconds. Here, a differential diagnosis model must be incorporated with finer data analytics methods to help tease out cancer-specific spectral features in complex datasets.A hierarchical differential diagnosis (decision tree) model optimizing evidence-based adjustment of the extent of resection for n=62 lymphoma (no resection), n=96 metastatic carcinomas (aggressive resection) and n=216 gliomas (maximum safe margin resection), the latter comprised of n=93 isocitrate dehydrogenase-1 (IDH1) positive and n=132 negative mutational status was created. This tree flows in a logical order based on what a neuropathologist would use for diagnosis, albeit utilized for automated non-subjective diagnosis based on 10-second PIRL-MS lipid analysis. The goal was to optimize the best machine learning method at each discrimination point from a list of principal component analysis-linear discriminant analysis (PCA-LDA), partial-least squares discriminant analysis, lasso regression, random forest, XGBoost, and support vector machine.The hierarchical approach enabled simplification of class comparisons to only those in differential diagnosis, as such, eliminating the misclassification between classes at finer branches of tree. This allowed performance metrics to be generated at each discrimination point not distinguished in simultaneous prediction of all classes using a single multiclass model.Within all nodes, PCA-LDA (a linear method) showed best performance, especially compared to ensemble approaches. Using this approach, the hierarchical method delivered diagnosis of lymphoma, glioma and metastatic tumours in node 1 with sensitivity and specificity of 87% and 93%, respectively and was able to differentiate between IDH1 mutant and WT gliomas with the sensitivity and specificity of >93%, the latter not being possible in current intraoperative diagnosis workflows due to long analysis times.The utility of this optimized hierarchical approach with PIRL-MS data is manifested in the users’ ability to adjust the diagnosis depth commensurate with desired performance criteria, enabling incorporation of the clinical ‘cost’ of total resection of one tumour class compared to another. Citation Format: Darah Vlaminck, Alexa Fiorante, Lan Anna Ye, David Munoz, Sunit Das, Gelareh Zadeh, Howard Ginsberg, Arash Zarrine-Afsar. Comparative assessment of data analysis methods to enable robust 10-second diagnosis of tissue pathologies with picosecond infrared laser mass spectrometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2507.

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