Lazzini Gianmarco, Gaeta Raffele, Pollina Luca Emanuele, Comandatore Annalisa, Furbetta Niccolò, Morelli Luca, D'Acunto Mario
CNR-IBF, Istituto di Biofisica Consiglio Nazionale delle Ricerche, via Moruzzi 1, 56124, Pisa, Italy.
Second Division of Surgical Pathology, University Hospital of Pisa, Pisa, Italy.
Sci Rep. 2025 Apr 17;15(1):13240. doi: 10.1038/s41598-025-98122-9.
Pancreatic ductal adenocarcinoma is currently the 12th most frequent form of cancer worldwide, characterized by a very low 5-year survival rate. Although several therapeutic approaches have been proposed to treat this form of pancreatic cancer, surgical resection is still commonly recognized as the most effective technique to slow down the disease progression and maximize the 5-year survival rate. Analogously, one critical issue is the ability of current diagnostic methodologies to distinguish between irregular growth of the tumor mass and surrounding inflammatory tissues. In this pilot study, we apply Raman spectroscopy, supported by a series of machine learning techniques, to distinguish among healthy, pancreatitis and ductal adenocarcinoma tissues, respectively, for a total of 15 cases. Raman spectroscopy is a label-free, non-destructive spectral technique exploiting Raman scattering. In turn, by applying a combination of principal component analysis and random forest classifier on the Raman spectral dataset, we achieved a maximum accuracy of up to 96%. Our findings clearly indicate that Raman spectroscopy could become a powerful spectral technique to support pathologists in improving pancreatic cancer diagnosis.
胰腺导管腺癌是目前全球第12大常见癌症，其5年生存率极低。尽管已经提出了几种治疗方法来治疗这种胰腺癌，但手术切除仍然是普遍公认的减缓疾病进展并最大化5年生存率的最有效技术。同样，一个关键问题是当前诊断方法区分肿瘤块不规则生长与周围炎症组织的能力。在这项初步研究中，我们应用拉曼光谱，并辅以一系列机器学习技术，分别对总共15例健康、胰腺炎和导管腺癌组织进行区分。拉曼光谱是一种利用拉曼散射的无标记、非破坏性光谱技术。反过来，通过对拉曼光谱数据集应用主成分分析和随机森林分类器的组合，我们实现了高达96%的最大准确率。我们的研究结果清楚地表明，拉曼光谱可能成为一种强大的光谱技术，以支持病理学家改善胰腺癌诊断。
