Significance: Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. Aim: To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach: A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l’Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results: Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (−2 % ), +0 % (−3 % ), +2 % (−2 % ), +4 (+3)], the AUC was improved in both testing sets. Conclusions: Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

中文翻译：

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基于拉曼显微光谱数据峰值拟合的降维提高了组织标本中前列腺癌的检测

意义：前列腺癌是男性中最常见的癌症。在检测时准确诊断其严重程度对提高他们的生存率起着重要作用。最近，使用从拉曼显微光谱鉴定的生物标志物的机器学习模型以≥85% 的检测准确率区分了来自癌组织的前列腺导管内癌 (IDC-P) 并区分了来自 IDC-P 的高级别前列腺上皮内瘤变 (HGPIN) ≥97.8 % 的准确度。目的：使用新的降维技术提高识别不同类型前列腺癌组织的机器学习模型的分类性能。方法：径向基函数 (RBF) 核支持向量机 (SVM) 模型在来自 272 名患者队列（蒙特利尔大学中心医院，CHUM) 并在两个独立队列中进行了测试，这些队列包括 76 名患者 [大学健康网络 (UHN)] 和 135 名患者（Centre Hospitalier universitaire de Québec-Université Laval，CHUQc-UL）。使用了两种类型的工程特征。单独的强度特征，即在特定波长测量的拉曼信号强度和新的拉曼光谱拟合由峰高和峰宽组成的峰特征。结果：结合工程特征提高了上述三个分类任务的分类性能。UHN 和 CHUQc-UL 测试集的 IDC-P/癌症分类在准确性、灵敏度、特异性和曲线下面积 (AUC) 方面的改进是（括号中的数字与 CHUQc-UL 测试集相关）：+ 4 % (+8 % )、+7 % (+9 % )、+2 % (6%)、+9 (+9) 相对于当前最佳模型。HGPIN 和 IDC-P 之间的区别在两个测试组中也得到改善：+2.2 % (+1.7 % )、+4.5 % (+3.6 % )、+0 % (+0 % )、+2.3 (+0)。虽然对于正常与癌症分类任务 [+0 % (-2 % )、+0 % (-3 % )、+2 % (-2 % )、+4 (+3)]，没有获得整体改进，但两个测试集中的 AUC 都得到了改进。结论：与仅使用单个强度特征相比，结合个体强度特征和新颖的拉曼拟合峰值特征，提高了两个独立和多中心测试集的分类性能。两个测试集中的 AUC 都得到了改进。结论：与仅使用单个强度特征相比，结合个体强度特征和新颖的拉曼拟合峰值特征，提高了两个独立和多中心测试集的分类性能。两个测试集中的 AUC 都得到了改进。结论：与仅使用单个强度特征相比，结合个体强度特征和新颖的拉曼拟合峰值特征，提高了两个独立和多中心测试集的分类性能。