Topological Machine Learning for Raman Spectroscopy: Perspectives for Pancreatic Diseases

The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced topological data analysis (TDA) techniques, known as topological machine learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classification (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.

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Published
2025-09-12
DOI
https://doi.org/10.3390/proceedings2025129061
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
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article
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article

Topological Machine Learning for Raman Spectroscopy: Perspectives for Pancreatic Diseases

Raffaele Gaeta, Niccolò Furbetta, Francesco Conti, Luca Morelli et al.
Spectroscopy Techniques in Biomedical and Chemical Research
article

Topological Machine Learning for Raman Spectroscopy: Perspectives for Pancreatic Diseases

Raffaele Gaeta, Niccolò Furbetta, Francesco Conti, Luca Morelli, Gianmarco Lazzini, Davide Moroni, Luca Emanuele Pollina, Maria Antonietta Pascali, Mario D’Acunto, Annalisa Comandatore
article en

Abstract

The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced topological data analysis (TDA) techniques, known as topological machine learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classification (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.

University of Pisa (IT), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT), Institut national de recherche en sciences et technologies du numérique (FR), National Research Council (IT)
Openalex Percentile: Top 83%
Spectroscopy Techniques in Biomedical and Chemical Research
DOIOpen Access PDFOpenAlex
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Topological Machine Learning for Raman Spectroscopy: Perspectives for Pancreatic Diseases — Raffaele Gaeta, Niccolò Furbetta, et al. · (2025) | TGRS Research Map | TGRS