The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Deciphering tertiary lymphoid structure heterogeneity reveals prognostic signature and therapeutic potentials for colorectal cancer: a multicenter retrospective cohort study.

BACKGROUND: Tertiary lymphoid structures (TLSs) exert a crucial role in the tumor microenvironment (TME), impacting tumor development, immune escape, and drug resistance. Nonetheless, the heterogeneity of TLSs in colorectal cancer (CRC) and their impact on prognosis and treatment response remain unclear. METHODS: We collected genome, transcriptome, clinicopathological information, and digital pathology images from multiple sources. An unsupervised clustering algorithm was implemented to determine diverse TLS patterns in CRC based on the expression levels of 39 TLS signature genes (TSGs). Comprehensive explorations of heterogeneity encompassing mutation landscape, TME, biological characteristics, response to immunotherapy, and drug resistance were conducted using multi-omics data. TLSscore was then developed to quantitatively assess TLS patterns of individuals for further clinical applicability. RESULTS: Three distinct TLS patterns were identified in CRC. Cluster 1 exhibited upregulation of proliferation-related pathways, high metabolic activity, and intermediate prognosis, while Cluster 2 displayed activation of stromal and carcinogenic pathways and a worse prognosis. Both Cluster 1 and Cluster 2 may potentially benefit from adjuvant chemotherapy. Cluster 3, characterized by the activation of immune regulation and activation pathways, demonstrated a favorable prognosis and enhanced responsiveness to immunotherapy. We subsequently employed a regularization algorithm to construct the TLSscore based on 9 core genes. Patients with lower TLSscore trended to prolonged prognosis and a more prominent presence of TLSs, which may benefit from immunotherapy. Conversely, those with higher TLSscore exhibited increased benefits from adjuvant chemotherapy. CONCLUSIONS: We identified distinct TLS patterns in CRC and characterized their heterogeneity through multi-omics analyses. The TLSscore held promise for guiding clinical decision-making and further advancing the field of personalized medicine in CRC.

Clinical, endoscopic, pathological characteristics and management of cap polyposis: experience from a Tertiary Hospital in China.

Background and aims: Cap polyposis (CP) is a rare kind of benign disease, and the majority of previously published relevant articles involve a small number of patients. Hence, we summarized our experience to contribute additional data, hoping to raise awareness of this disease. Methods: From 1 January 2017 to 1 November 2021, consecutive patients diagnosed with CP were retrospectively reviewed. Their medical histories, and laboratory, imaging, endoscopic, and pathology results were analyzed. We made telephone calls to the patients and searched for the information in our electronic medical records to obtain the follow-up results. Results: Forty-one patients were chosen for analysis. The median age of the patients was 20 years old, and 90.24% (37 patients) of the patients were male. The majority of the patients presented with hematochezia. The rectum was the most commonly affected site, and the Helicobacter pylori infection rate was high. There were multiple and combined treatments for these patients. These treatments can be divided into 3 main categories: medical therapy, endotherapy and surgery. Medical therapy helped to diminish the size of but the polyps were difficult to resolve; however, the patients' symptoms could be diminished. Twenty-three patients underwent surgical resection, and 12 patients received endotherapy. We further compared the two methods of polyp resection. Both endotherapy and surgery were safe, and the recurrence risk was not significantly different between the two kinds of therapy (p = 0.321). Conclusion: The clinical improvement of medical treatments was not satisfactory, and endotherapy or surgical resection could remove the polyposis and provide temporary relief, but the recurrence rates were high.

Structure Embedded Nucleus Classification for Histopathology Images.

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Ulcerative colitis complicated with hypermucinous dysplasia.

Patients with ulcerative colitis are at increased risk for colorectal neoplasia compared to the general population. The risk factors include family history of colorectal cancer, wide extent of colitis, disease duration, cumulative inflammatory burden, and primary sclerosing cholangitis. Here, we report a case of colorectal neoplasia developed in a patient with ulcerative colitis.