Colon cancer patients with mismatch repair deficiency are more likely to present as acute surgical cases

Survival Outcomes in Patients with Monobloc-Resected Stage IIC (pT4bN0) Colon Cancer: A Retrospective Observational Cohort Study

BACKGROUND

Stage II colon cancer (CC) exhibits considerable prognostic heterogeneous. Our objective was to assess survival but also the prognosis impact of microsatellite instability (MSI) in patients with stage IIC (T4bN0M0) CC.

PATIENTS AND METHODS

We conducted a retrospective observational study including all patients who had primary stage IIC CC resection between 2010 and 2020 in 2 expert centers. The primary endpoint was overall survival (OS) and disease-free survival (DFS) and time-to-relapse (TTR) were secondary endpoints.

RESULTS

Sixty-six patients, median age of 74 years [30-95], were included, with 37.9% presenting MSI (n = 25). Organ invasion involved the last ileal loop (n = 17), another colonic segment (n = 15), omentum (n = 13), visceral peritoneum (n = 13), and the bladder (n = 4). Surgical quality criteria showed complete monobloc resection in all patients and 93.9% R0 resection. After a median follow-up of 5 years [3.5-6.6], the entire population showed a 5-year OS of 65.2% [53.0-80.3] and 5-year DFS of 53.5% [41.1-69.6], with 18.9% [6.8-29.4] experiencing relapses at 5 years. The MSI phenotype correlated with improved 5-year OS (75.5% [56.5-100] vs. 59.5% [44.9-79.0], HR 0.41 [0.17-0.99]; P = .04), but DFS and TTR did not differ. Adjuvant chemotherapy was administered to 34.9% of patients. Univariate analysis identified age > 65 years, MSI status, and the number of nodes as factors associated with OS.

CONCLUSION

These data underline, in relation to a low rate of relapse, the lack of consensus regarding the appropriate indication for adjuvant chemotherapy in this high-risk stage II population.

Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis

The aim of this research was to investigate the predictive role of texture features in computed tomography (CT) images based on artificial intelligence (AI) algorithms for colorectal liver metastases (CRLM). A total of 150 patients with colorectal cancer who were admitted to the hospital were selected as the research objects and randomly divided into three groups with 50 cases in each group. The patients who were found to suffer from the CRLM in the initial examination were included in group A. Patients who were found with CRLM in the follow-up were assigned to group B (B1: metastasis within 0.5 years, 16 cases; B2: metastasis within 0.5–1.0 years, 17 cases; and B3: metastasis within 1.0–2.0 years, 17 cases). Patients without liver metastases during the initial examination and subsequent follow-up were designated as group C. Image textures were analyzed for patients in each group. The prediction accuracy, sensitivity, and specificity of CRLM in patients with six classifiers were calculated, based on which the receiver operator characteristic (ROC) curves were drawn. The results showed that the logistic regression (LR) classifier had the highest prediction accuracy, sensitivity, and specificity, showing the best prediction effect, followed by the linear discriminant (LD) classifier. The prediction accuracy, sensitivity, and specificity of the LR classifier were higher in group B1 and group B3, and the prediction effect was better than that in group B2. The texture features of CT images based on the AI algorithms showed a good prediction effect on CRLM and had a guiding significance for the early diagnosis and treatment of CRLM. In addition, the LR classifier showed the best prediction effect and high clinical value and can be popularized and applied.