A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness.

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.

QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random Copolymers.

The glass transition temperature (Tg ) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing predefined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275 copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9 K, a standard error of prediction (S) of 6.1 K and a squared correlation coefficient (R(2) ) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting.