软组织肉瘤的活体拉曼光谱和基于人工智能的分类
Maede Boroji1, Vahid Danesh1, David Barrera2
1Department of Mechanical Engineering, Stony Brook University, Stony Brook, New York, United States of America.
PloS one
|September 2, 2025
概括
微观拉曼光谱显示有望区分软组织瘤 (STS) 和正常组织. 这种技术可以提供一个快速的,非侵入性的工具来引导外科医生在手术期间识别异常组织和边缘.
科学领域:
- 癌症学
- 生物医学工程
- 光谱学
背景情况:
- 软组织瘤 (STS) 是一种罕见的异质恶性瘤,需要精确的诊断和治疗.
- 目前用于STS的手术内利评估方法缺乏效率和准确性.
- 需要改进的工具来加强软组织瘤的手术指导.
研究的目的:
- 对微观拉曼光谱进行研究,以区分STS亚型,良性瘤和正常组织.
- 评估使用拉曼光谱作为手术中的快速诊断工具的可行性.
- 开发和验证基于拉曼数据的组织类型的计算模型.
主要方法:
- 在7名患者的286,672个组织样本上进行了活体拉曼光谱.
- 使用633nm激发波长进行光谱采集.
- 使用自定义的ResNet架构进行数据预处理和分类.
主要成果:
- 在分类组织类型方面,ResNet模型的整体加权精度高达97.1%.
- 记录的临床警报率为1. 46%,表明恶性组织的错误分类很小.
- 拉曼光谱在区分各种软组织类型方面表现出显著的潜力.
结论:
- 微观拉曼光谱可以准确地区分软组织瘤,良性瘤和正常组织.
- 单个拉曼光谱显示出作为手术指导的快速,非侵入性工具的潜力.
- 这种技术可以提高在肉瘤切除中识别异常组织和外科边缘的精度.
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