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Objectives: The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC). Methods: A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index. Results: LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96–54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39–25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11–17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01–0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06–0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89. Conclusions: There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection. Key Points: • LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection. [ABSTRACT FROM AUTHOR]

Background: It is common for elderly patients to be underrepresented in clinical trials for cancer, which can result in a lack of efficacy data and unclear criteria to guide treatment decisions for clinical doctors. Therefore, one of the common challenges in oncology treatment is determining the extent to which patients aged 75 and older have benefited from postoperative chemotherapy.Purpose: The study aimed to explore the effect of adjuvant chemotherapy (AC) on 3-year recurrence-free survival (RFS) after curative resection in patients aged 75 years and older with stage II–III colorectal cancer (CRC).Methods: The retrospective cohort analysis was performed on patients with stage II–III CRC who received curative resection at three cancer centers in China between 2008 and 2017. Kaplan-Meier curves and Multivariable Cox regression models were used to analyze the impact of AC on RFS in patients. Finally, propensity-score matching was used to reduce selection bias and confounding factors in patients aged 75 years and older with stage II–III CRC.Results: A total of 2885 patients were included (1729 (59.9%) male; 1312 (61.5%) received AC). The pre-matching cohort was comprised of 151 patients aged 75 years and older (median age (IQR)77.00 (76.00, 79.00); 97 (64.2%) male, 51 (72.9%) received AC). Age (P=0.001), postoperative carcinoembryonic antigen (CEA)(P=0.02) level were associated with prognosis. But AC was not associated with 3-year RFS (HR, 1.27; 95% CI, 0.80– 2.0; log-rank P=0.37). After a predisposition 1: 1 match (with or without AC, n = 42), AC remains uncorrelated with 3-year RFS (HR, 1.39; 95% CI, 0.52– 3.70; log-rank P=0.66).Conclusion: Patients over the age of 75 with stage II–III CRC who receive AC or do not face the same risk of postoperative recurrence. As a result, patients with stage II–III postoperative adjuvant chemotherapy can make an informed decision regarding whether they want to undergo chemotherapy based on their age and reduce the unnecessary side effects of chemotherapy. [ABSTRACT FROM AUTHOR]

Background: Whether elevated postoperative serum carcinoembryonic antigen (CEA) levels are prognostic in patients with stage II colorectal cancer (CRC) remains controversial. Patients and Methods: Primary and sensitivity analysis populations were obtained from a retrospective, multicenter longitudinal cohort including consecutive patients without neoadjuvant treatment undergoing curative resection for stage I–III CRC. Serum CEA levels before (CEApre-m1) and within 1 (CEApost-m1), 2–3 (CEApost-m2–3), and 4–6 months (CEApost-m4–6) after surgery were obtained, and their associations with recurrence-free survival (RFS) and overall survival (OS) were assessed using Cox regression. Sensitivity and subgroup analyses were performed. Results: Primary and sensitivity analysis populations included 710 [415 men; age, 54.8 (11.6) years] and 1556 patients [941 men; age, 56.2 (11.8) years], respectively. Recurrence hazard ratios (HRs) in the elevated CEApre-m1, CEApost-m1, CEApost-m2–3, and CEApost-m4–6 groups were 1.30 (95% CI: 0.91–1.85), 1.53 (95% CI: 0.89–2.62), 1.88 (95% CI: 1.08–3.28), and 1.15 (95% CI: 0.91–1.85), respectively. The HRs of the elevated CEApre-m1, CEApost-m1, CEApost-m2–3, and CEApost-m4–6 groups for OS were 1.09 (95% CI: 0.60–1.97), 2.78 (95% CI: 1.34–5.79), 2.81 (95% CI: 1.25–6.30), and 3.30 (95% CI: 1.67–.536), respectively. Adjusted multivariate analyses showed that both in the primary and sensitivity analysis populations, elevated CEApost-m2–3, rather than CEApre-m1, CEApost-m1, and CEApost-m4–6, was an independent risk factor for recurrence, but not for OS. The RFS in the elevated and normal CEApost-m2–3 groups differed significantly among patients with stage II disease [n = 266; HR, 2.89; 95% CI, 1.02–8.24 (primary analysis); n = 612; HR, 2.69; 95% CI, 1.34–5.38 (sensitivity analysis)]. Conclusions: Elevated postoperative CEA levels are prognostic in patients with stage II CRC, with 2–3 months after surgery being the optimal timing for CEA measurement. [ABSTRACT FROM AUTHOR]

aimed to investigate the association between the postoperative peripheral CD4+ T cells percentage and recurrence in CRC patients. Patients and Methods: Consecutive stage I–III CRC patients without neoadjuvant treatment undergoing curative resection from January 2010 to July 2016 were identified in two Chinese centers. The association between the postoperative CD4+ T cells percentage, measured within 12 weeks after surgery, and recurrence-free survival (RFS) was analyzed. Results: A total of 1028 patients were identified (training set: 913 patients, validation set: 115 patients). In the training set, the 5-year RFS rate of the 441 patients with abnormal postoperative CD4+ T cells percentage was significantly lower than that of those with normal percentage (70.3% [95% CI 65.7– 75.2%] vs 77.6% [95% CI 73.7– 81.7%] and unadjusted hazard ratio [HR] 1.36 [95% CI 1.04– 1.78], P= 0.02). The result was confirmed in the validation set. Multivariable Cox regression analysis demonstrated that the association of postoperative CD4+ T cells percentage with 5-year RFS was independent both in the training and validation sets. In propensity score matching analysis, patients with normal postoperative CD4+ T cells percentage were found to have a favourable response to adjuvant chemotherapy (HR 0.29 [95% CI 0.12– 0.72], P=0.008). Conclusion: Postoperative peripheral CD4+ T cells percentage is a predictive biomarker for RFS in patients with CRC, which can identify those who will benefit from adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Computerized image analysis for whole-slide images has been shown to improve efficiency, accuracy, and consistency in histopathology evaluations. We aimed to assess whether immunohistochemistry (IHC) image quantitative features can reflect the immune status and provide prognostic information for colorectal cancer patients. A fully automated pipeline was designed to extract histogram features from IHC digital images in a training set (N = 243). A Hist-Immune signature was generated with selected features using the LASSO Cox model. The results were validated using internal (N = 147) and external (N = 76) validation sets. The five-feature-based Hist-Immune signature was significantly associated with overall survival in training (HR 2.72, 95% CI 1.68–4.41, P <.001), internal (2.86, 1.28–6.39, 0.010), and external (2.30, 1.02–6.16, 0.044) validation sets. The full model constructed by integrating the Hist-Immune signature and clinicopathological factors had good discrimination ability (C-index 0.727, 95% CI 0.678–0.776), confirmed using internal (0.703, 0.621–0.784) and external (0.756, 0.653–0.859) validation sets. Our findings indicate that the Hist-Immune signature constructed based on the quantitative features could reflect the immune status of patients with colorectal cancer, which might advocate change in risk stratification and consequent precision medicine. [ABSTRACT FROM AUTHOR]

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Image segmentation of histopathology of colorectal cancer is a core task of computer aided medical image diagnosis system. Existing convolutional neural networks generally extract multi-scale information in linear flow structures by inserting multi-branch modules, which is difficult to extract heterogeneous semantic information under multi-level and different receptive field and tough to establish context dependency among different receptive field features. To address these issues, we propose a symmetric spiral progressive feature fusion encoder-decoder network called the Symmetric Conical Network (SC-Net). First, we design a Multi-scale Feature Extraction Block (MFEB) matching with the Symmetric Conical Network to obtain multi-branch heterogeneous semantic information under different receptive fields, so as to enrich the diversity of extracted feature information. The encoder is composed of MFEB through spiral and multi-branch arrangement to enhance context dependence between different information flow. Secondly, the information loss of contour, color and others in high-level semantic information through causally stacking MFEB, the Feature Mapping Layer (FML) is designed to map low-level features to high-level semantic features along the down-sampling branch and solve the problem of insufficient global feature extraction in deep levels. The SC-Net was evaluated on our self-constructed colorectal cancer dataset, a publicly available breast cancer dataset and a polyp dataset. The results revealed that the mDice of segmentation reached 0.8611, 0.7259 and 0.7144. We compare our model with the state-of-art semantic segmentation UNet++, PSPNet, Attention U-Net, R2U-Net and other advanced segmentation networks. The experimental results demonstrate that we achieve the most advanced performance. The results indicate that the proposed SC-Net excels in segmenting H & E stained pathology images, effectively preserving morphological features and spatial information even in scenarios with weak texture, poor contrast, and variations in appearance. • The symmetrical conical network is proposed for the segmentation of colorectal cancer pathological images. • Improve information flow and gradients across the network through parameter sharing. • Improving Segmentation Performance by Incorporating Contextual Information from Multi-Scale Receptive Fields. • Compensating the loss of deep abstract semantic feature information in the form of hierarchical flow • A dataset is established with four semantic categories finely annotated. [ABSTRACT FROM AUTHOR]