在本研究中，我们研究了多元曲线分辨率交替最小二乘 (MCR-ALS) 算法用于分析前列腺癌 (PCa) 患者前列腺三维 (3D) 1 H-MRSI 数据的潜力。 MCR-ALS 生成代表来自大量患者训练集的光谱分布的成分的相对强度，从而提供可解释的模型。我们的目标是对磁共振 (MR) 谱进行分类，区分肿瘤病变与良性组织，并评估前列腺癌的侵袭性。我们纳入了来自 8 个中心 106 名 PCa 患者的多中心 3D 1 H-MRSI 数据。患者队列被分为训练集 (N = 63) 和独立测试集 (N = 43)。奇异值分解确定 MR 谱最好由五个分量表示。这些成分的概况是通过 MCR-ALS 从训练集中提取的，并分配给特定的组织类型。使用这些组件，将 MCR-ALS 应用于测试集进行定量分析，以区分肿瘤病变与良性组织并评估肿瘤侵袭性。重建成分的相对强度图并与组织病理学报告进行比较。定量分析表明肿瘤和良性体素之间存在显着分离（t 检验，p < 0.001）。这一结果包括具有低质量 MR 光谱的体素。对肿瘤成分相对强度的接受者操作特征分析显示，可以用 0.88 的曲线下面积区分低风险和高风险肿瘤病变。该组件的图正确地识别了肿瘤病变的范围。我们的研究表明，前列腺 1 H-MRSI 的 MCR-ALS 分析可以可靠地识别肿瘤病变并评估其侵袭性。它以最少的预处理处理多中心数据，并且不使用先验知识或质量控制。这些发现表明，MCR-ALS 可以作为一种自动化工具来评估前列腺肿瘤病变的存在、程度和侵袭性，从而增强 PCa 患者的诊断能力和治疗计划。© 2023 作者。约翰·威利 (John Wiley) 出版的《生物医学中的核磁共振》

In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-MRSI data of the prostate in prostate cancer (PCa) patients. MCR-ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1 H-MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR-ALS and assigned to specific tissue types. Using these components, MCR-ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t-test, p < 0.001). This result was achieved including voxels with low-quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low- and high-risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR-ALS analysis of 1 H-MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR-ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients.© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.