Aubertin Kelly, Desroches Joannie, Jermyn Michael, Trinh Vincent Quoc, Saad Fred, Trudel Dominique, Leblond Frédéric
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), 900 rue St-Denis, Montréal, Quebec H2X 0A9, Canada.
Institut du cancer Montréal (ICM), 900 rue St-Denis, Montréal, Quebec H2X 0A9, Canada.
Biomed Opt Express. 2018 Aug 15;9(9):4294-4305. doi: 10.1364/BOE.9.004294. eCollection 2018 Sep 1.
For prostate cancer (PCa) patients, radical prostatectomy (complete removal of the prostate) is the only curative surgical option. To date, there is no clinical technique allowing for real-time assessment of surgical margins to minimize the extent of residual cancer. Here, we present a tissue interrogation technique using a dual excitation wavelength Raman spectroscopy system capable of sequentially acquiring fingerprint (FP) and high wavenumber (HWN) Raman spectra. Results demonstrate the ability of the system to detect PCa in post-prostatectomy specimens. In total, 477 Raman spectra were collected from 18 human prostate slices. Each area measured with Raman spectroscopy was characterized as either normal or cancer based on histopathological analyses, and each spectrum was classified based on supervised learning using support vector machines (SVMs). Based on receiver operating characteristic (ROC) analysis, FP (area under the curve [AUC] = 0.89) had slightly superior cancer detection capabilities compared with HWN (AUC = 0.86). Optimal performance resulted from combining the spectral information from FP and HWN (AUC = 0.91), suggesting that the use of these two spectral regions may provide complementary molecular information for PCa detection. The use of leave-one-(spectrum)-out (LOO) or leave-one-patient-out (LOPO) cross-validation produced similar classification results when combining FP with HWN. Our findings suggest that the application of machine learning using multiple data points from the same patient does not result in biases necessarily impacting the reliability of the classification models.
对于前列腺癌（PCa）患者，根治性前列腺切除术（完全切除前列腺）是唯一的治愈性手术选择。迄今为止，尚无临床技术能够实时评估手术切缘，以尽量减少残留癌的范围。在此，我们展示了一种组织检测技术，该技术使用双激发波长拉曼光谱系统，能够依次获取指纹（FP）和高波数（HWN）拉曼光谱。结果证明了该系统在前列腺切除术后标本中检测PCa的能力。总共从18个人类前列腺切片中收集了477条拉曼光谱。根据组织病理学分析，用拉曼光谱测量的每个区域被表征为正常或癌症，并且使用支持向量机（SVM）基于监督学习对每个光谱进行分类。基于受试者工作特征（ROC）分析，与HWN（曲线下面积[AUC]=0.86）相比，FP（曲线下面积[AUC]=0.89）具有略优的癌症检测能力。将FP和HWN的光谱信息相结合可获得最佳性能（曲线下面积[AUC]=0.91），这表明使用这两个光谱区域可能为PCa检测提供互补的分子信息。当将FP与HWN相结合时，使用留一（光谱）法（LOO）或留一患者法（LOPO）交叉验证产生了相似的分类结果。我们的研究结果表明，使用来自同一患者的多个数据点进行机器学习的应用不一定会导致影响分类模型可靠性的偏差。
