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Background: The location of gastrointestinal perforation is essential for severity evaluation and optimizing the treatment approach. We aimed to retrospectively analyze the clinical characteristics, laboratory parameters, and imaging features of patients with gastrointestinal perforation and construct a predictive model to distinguish the location of upper and lower gastrointestinal perforation. Methods: A total of 367 patients with gastrointestinal perforation admitted to the department of emergency surgery in Fujian Medical University Union Hospital between March 2014 and December 2020 were collected. Patients were randomly divided into training set and test set in a ratio of 7:3 to establish and verify the prediction model by logistic regression. The receiver operating characteristic curve, calibration map, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical applicability of the prediction model, respectively. The multiomics model was validated by stratification analysis in the prediction of severity and prognosis of patients with gastrointestinal perforation. Results: The following variables were identified as independent predictors in lower gastrointestinal perforation: monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, free air in peritoneal cavity by univariate logistic regression analysis and stepwise regression analysis. The area under the receiver operating characteristic curve of the prediction model was 0.886 (95% confidence interval, 0.840–0.933). The calibration curve shows that the prediction accuracy and the calibration ability of the prediction model are effective. Meanwhile, the decision curve results show that the net benefits of the training and test sets are greater than those of the two extreme models as the threshold probability is 20–100%. The multiomics model score can be calculated via nomogram. The higher the stratification of risk score array, the higher the number of transferred patients who were admitted to the intensive care unit (P < 0.001). Conclusion: The developed multiomics model including monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, and free air in the peritoneal cavity has good discrimination and calibration. This model can assist surgeons in distinguishing between upper and lower gastrointestinal perforation and to assess the severity of the condition. [ABSTRACT FROM AUTHOR]

Objectives: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1–T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. Methods: We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). Results: In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. Conclusion: The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model. [ABSTRACT FROM AUTHOR]

Owing to the high incidence and mortality rates of colorectal cancer (CRC), novel biomarkers for CRC diagnosis are critically needed. Therefore, this study is aimed at exploring the clinical utility of serum C-X-C motif chemokine 8 (CXCL-8) for CRC diagnosis and progression compared to the routinely used biomarkers, carcinoembryonic antigen (CEA), and carbohydrate antigen-19-9 (CA19-9). This study included 227 patients with CRC, 110 patients with colorectal adenoma (CA), and 123 healthy participants, who were recruited from the Fujian Medical University Union Hospital from July 1, 2019 to October 31, 2020. Serum concentrations of CXCL-8, CEA, and CA19-9 were detected using enzyme-linked immunosorbent assay and chemiluminescent microparticle immunoassay. Clinicopathological features of patients with CRC were collected and analyzed. The diagnostic efficacy of CXCL-8, CEA, and CA19-9 for CRC was evaluated using receiver operating characteristic (ROC) curves. We found that the serum concentrations of CXCL-8, CEA, and CA19-9 were significantly higher in patients with CRC than those in patients with CA and healthy controls. The diagnostic sensitivity of CXCL-8 alone was higher than those of CEA and CA19-9 both and when combined; thus, CXCL-8 may be better at discriminating patients with CRC from healthy controls and patients with CA. Moreover, combining CXCL-8 with CEA or CA19-9 improved their respective diagnostic performances in distinguishing patients with CRC from CA patients and healthy participants. Notably, we also found that serum concentrations of CXCL-8 were positively correlated with metastases and tumor size. Therefore, our study suggests that serum CXCL-8 may serve as an improved biomarker for CRC diagnosis compared to the traditional tumor markers CEA and CA19-9. Moreover, our findings indicate the potential efficacy of serum CXCL-8 levels as a CRC prognostic biomarker. [ABSTRACT FROM AUTHOR]

Purpose. The clinical utility of plasma methylated septin 9 (mSEPT9) DNA in screening and recurrence monitoring for colorectal cancer (CRC) is highly promising. The present study was performed to determine the diagnostic value of mSEPT9 in CRC detection and recurrence monitoring in Chinese patients. Methods. Overall, 616 patients newly diagnosed with CRC and 122 individuals with no evidence of disease were recruited from October 1, 2019, to May 31, 2021, at Fujian Medical University Union Hospital. Plasma and serum samples were collected for analyzing mSEPT9, carcinoembryonic antigen (CEA), and carbohydrate antigen-19-9 (CA19-9). Data on clinicopathological characteristics were collected and analyzed. Sensitivity and specificity were calculated to evaluate the diagnostic potential of each marker; the receiver operating characteristic (ROC) curve was applied for the assessment of diagnostic value, and comparisons among mSEPT9, CEA, CA19-9, and their combination were assessed via ROC curves. Results. mSEPT9 achieved an overall sensitivity and specificity of 72.94% and 81.97%, respectively, with an area under the curve (AUC) value of 0.826, which were higher than those of CEA (sensitivity: 43.96%; specificity: 96.72%; AUC: 0.789) and CA19-9 (sensitivity: 14.99%; specificity: 96.61%; AUC: 0.590). The combination of mSEPT9, CEA, and CA19-9 further improved sensitivity, specificity, and AUC value (sensitivity: 78.43%; specificity: 86.07%; AUC: 0.878), respectively. Notably, the mSEPT9 positivity rate was significantly associated with TNM stage, T stage, N stage, tumor size, vascular invasion, and nerve invasion among patients with CRC. A 100% correlation was observed between the positive results of the mSEPT9 test and recurrence or metastasis in patients after therapeutic intervention. Conclusion. Our findings suggest that mSEPT9 may represent a potential biomarker for the diagnosis and prognosis of CRC compared with CEA and CA19-9. Postoperative mSEPT9 status may represent the first noninvasive marker of CRC recurrence or metastasis. [ABSTRACT FROM AUTHOR]