Purpose: This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions. Methods: In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%; n = 224) and a validation set (ca. 30%; n = 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan–Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors. Results: Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12–63.86) in the high-risk group and 83.0% (95%CI, 76.31–90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model. Conclusion: The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.

Background: Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions are solid and smooth. Methods: To overcome these problems and to provide an efficient, accurate, robust and concise solution that simplifies the whole segmentation pipeline in AI-assisted applications, we propose the BézierSeg model which outputs Bézier curves encompassing the region of interest. Results: Directly modeling the contour with analytic equations ensures that the segmentation is connected and continuous, and that the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of precision, the Bézier contour can be resampled and overlaid with images of any resolution. Moreover, clinicians can conveniently adjust the curve’s control points to refine the result. Conclusions: Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.

Multilabel pathological tissue segmentation is a vital task in computational pathology that aims to semantically segment different tissues within pathological images. Fully and weakly supervised models have demonstrated impressive performances in this regard. However, weakly supervised models still face challenges, such as the poor performance of nondominant samples and limited effectiveness of aggregation functions in conveying supervisory signals. To address these issues, we propose two key contributions: the introduction of a graph attention network(GAT) module to establish contextual relationships between pixels within patches and generate high-quality pseudo-labels, and the development of a novel global classified max pooling(GCMP) aggregation function that effectively transmits the supervision signal from weakly annotated labels and improves the model's classification accuracy. The experimental results show that our method improved the MIoU scores by 3.3 and 3 for the nondominant samples, necrosis(NEC) and lymphocytes(LYM), respectively, in the LUAD-HistoSeg test set. This led to an overall MIoU of 0.774, which is a 1.8 increase in the state-of-the-art(SOTA) performance. Similarly, our approach improved MIoU scores by 5.7 and 2 on the NEC and LYM samples, respectively, in the Breast Cancer Semantic Segmentation(BCSS) test set, resulting in an overall MIoU of 0.721. This represents a 1.6 increase in SOTA performance. In summary, our work addresses the issues of poor performance on nondominant samples and the suboptimal performance of aggregation functions. We propose a novel approach to achieve a significant performance improvement. This is extremely significant in reducing the workload of manual annotation and promoting the development of computational pathologies.