The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PSGC) that is derived from multiple pathomics features of haematoxylin and eosin-stained slides. We find that the PSGC is an independent predictor of prognosis. A nomogram incorporating the PSGC and TNM staging system shows significantly improved accuracy in predicting the prognosis compared to the TNM staging system alone. Moreover, in stage II and III GC patients with a low PSGC (but not in those with a high PSGC), satisfactory chemotherapy benefits are observed. Therefore, the PSGC could serve as a prognostic predictor in patients with GC and might be a potential predictive indicator for decision-making regarding adjuvant chemotherapy.
Systemic inflammatory response (SIRS) can be used as a potential prognostic marker in patients with colorectal cancer (CRC). The purpose of this study was to examine the predictive role of the C-reactive protein (CRP)-lymphocyte ratio (CLR) in the prognosis of CRC. We retrospectively analyzed the data of CRC patients who underwent surgery from 2004 to 2019. The clinicopathological characteristics and follow-up records were analyzed. According to a cutoff value of CLR, the patients were divided into the high and low groups. Kaplan–Meier curves and Cox proportional hazards regression model were applied to assess the overall survival (OS). Clinicopathological characteristics analysis showed that gender, age, BMI, lymphocyte count, tumor location, left- and right-sided CRC, differentiation, T stage, M stage, TNM stage, carcinoembryonic antigen (CEA), CLR, CRP, and microsatellite status were found to differ significantly between the high and low CLR groups. Kaplan–Meier curves revealed that the high CLR group had a shorter OS, and the elderly or right-sided CRC patients faced a worse prognosis. Multivariate analysis suggested that age (hazard ratio [HR]:1.011, P = 0.003), differentiation (HR:1.331, P = 0.000), TNM stage (HR:2.425, P = 0.000), CEA (HR:1.001, P = 0.025), CLR (HR:1.261, P = 0.014) were significant independent prognostic factors for OS. Subgroup analysis demonstrated that females or patients not receiving postoperative adjuvant chemotherapy with high CLR might suffer a worse prognosis. Overall, CLR can be applied as a promising prognostic marker in CRC patients and has great potential in guiding clinical work.
BackgroundLymph node metastasis (LNM) is a well-established prognostic factor for colon cancer. Preoperative LNM evaluation is relevant for planning colon cancer treatment. The aim of this study was to construct and evaluate a nomogram for predicting LNM in primary colon cancer according to pathological features.Patients and MethodsSix-hundred patients with clinicopathologically confirmed colon cancer (481 cases in the training set and 119 cases in the validation set) were enrolled in the Affiliated Cancer Hospital of Guangxi Medical University from January 2010 to December 2019. The expression of molecular markers (p53 and β-catenin) was determined by immunohistochemistry. Multivariate logistic regression was used to screen out independent risk factors, and a nomogram was established. The accuracy and discriminability of the nomogram were evaluated by consistency index and calibration curve.ResultsUnivariate logistic analysis revealed that LNM in colon cancer is significantly correlated (P <0.05) with tumor size, grading, stage, preoperative carcinoembryonic antigen (CEA) level, and peripheral nerve infiltration (PNI). Multivariate logistic regression analysis confirmed that CEA, grading, and PNI were independent prognostic factors of LNM (P <0.05). The nomogram for predicting LNM risk showed acceptable consistency and calibration capability in the training and validation sets.ConclusionsPreoperative CEA level, grading, and PNI were independent risk factor for LNM. Based on the present parameters, the constructed prediction model of LNM has potential application value.
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