Boroji Maede, Danesh Vahid, Barrera David, Lee Elizabeth, Arauz Paul G, Farrell Renee F, Boyce Brendan F, Khan Fazel A, Kao Imin
Department of Mechanical Engineering, Stony Brook University, Stony Brook, New York, United States of America.
Department of Pathology and Laboratory Medicine, Stony Brook University Hospital, Stony Brook, New York, United States of America.
PLoS One. 2025 Sep 2;20(9):e0330618. doi: 10.1371/journal.pone.0330618. eCollection 2025.
Soft tissue sarcomas (STS) are a diverse and rare group of malignant tumors arising from the connective tissues of the body, including fibrous tissue, muscles, fat, nerves, and blood vessels. The heterogeneity and infrequency of these tumors pose significant challenges in both diagnosis and treatment. Surgical resection remains the primary treatment strategy, often complemented by radiation or chemotherapy, contingent upon the tumor's size, location, and stage. However, current methods for assessing intraoperative margins are limited, underscoring the need for improved approaches that enhance both efficiency and accuracy. This study investigates the potential of microscopic Raman spectroscopy for distinguishing between different subtypes of soft tissue sarcomas, benign tumors, and normal tissue. Ex-vivo Raman measurements were conducted using a 633 nm excitation wavelength on samples obtained from surgical resections of seven patients (286,672 spectra). After pre-processing of the data, a custom ResNet architecture was developed to accurately classify the different tissue types, achieving an overall weighted accuracy of 97.1% and a clinical alert rate of 1.46%, a critical metric for quantifying the misclassification of malignant tissues. These findings suggest that single Raman spectra could serve as a rapid, non-invasive tool for surgical guidance, aiding in the precise identification of abnormal tissue types and margins.
软组织肉瘤（STS）是一类源自身体结缔组织的多样且罕见的恶性肿瘤，包括纤维组织、肌肉、脂肪、神经和血管。这些肿瘤的异质性和罕见性在诊断和治疗方面都带来了重大挑战。手术切除仍然是主要的治疗策略，通常会根据肿瘤的大小、位置和分期辅以放疗或化疗。然而，目前评估术中切缘的方法有限，这凸显了需要改进方法以提高效率和准确性。本研究调查了显微拉曼光谱法区分软组织肉瘤不同亚型、良性肿瘤和正常组织的潜力。使用633nm激发波长对7例患者手术切除获得的样本进行了离体拉曼测量（286,672个光谱）。在对数据进行预处理后，开发了一种定制的ResNet架构来准确分类不同的组织类型，总体加权准确率达到97.1%，临床警报率为1.46%，这是量化恶性组织误分类的关键指标。这些发现表明，单个拉曼光谱可作为一种快速、非侵入性的手术指导工具，有助于精确识别异常组织类型和切缘。
