卵巢癌
接收机工作特性
子宫内膜癌
多光谱图像
拉曼光谱
医学
金标准(测试)
阶段(地层学)
癌症
病理
放射科
肿瘤科
内科学
生物
人工智能
计算机科学
光学
物理
古生物学
作者
Sandryne David,Arthur Plante,F. Dallaire,Jean-Philippe Tremblay,Guillaume Sheehy,Elizabeth Macdonald,Laura A. Forrest,Manijeh Daneshmand,Dominique Trudel,Brian C. Wilson,Laura Hopkins,Sangeeta Murugkar,Barbara C. Vanderhyden,Frédéric Leblond
标识
DOI:10.1002/jbio.202100198
摘要
Up to 70% of ovarian cancer patients are diagnosed with advanced-stage disease and the degree of cytoreduction is an important survival prognostic factor. The aim of this study was to evaluate if Raman spectroscopy could detect cancer from different organs within the abdominopelvic region, including the ovaries. A Raman spectroscopy probe was used to interrogate specimens from a cohort of nine patients undergoing cytoreductive surgery, including four ovarian cancer patients and three patients with endometrial cancer. A feature-selection algorithm was developed to determine which spectral bands contributed to cancer detection and a machine-learning model was trained. The model could detect cancer using only eight spectral bands. The receiver-operating-characteristic curve had an area-under-the-curve of 0.96, corresponding to an accuracy, a sensitivity and a specificity of 90%, 93% and 88%, respectively. These results provide evidence multispectral Raman spectroscopy could be developed to detect ovarian cancer intraoperatively.
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