A Randomized Phase III Trial of Complete Mesocolic Excision Compared with Conventional Surgery for Right Colon Cancer: Interim Analysis of a Nationwide Multicenter Study of the Italian Society of Surgical Oncology Colorectal Cancer Network (CoME-in trial).

BACKGROUND: Although complete mesocolic excision (CME) is supposed to be associated with a higher lymph node (LN) yield, decreased local recurrence, and survival improvement, its implementation currently is debated because the evidence level of these data is rather low and still not supported by randomized controlled trials. METHOD: This is a multicenter, randomized, superiority trial (NCT04871399). The 3-year disease-free survival (DFS) was the primary end point of the study. The secondary end points were safety (duration of operation, perioperative complications, hospital length of stay), oncologic outcomes (number of LNs retrieved, 3- and 5-year overall survival, 5-year DFS), and surgery quality (specimen length, area and integrity rate of mesentery, length of ileocolic and middle-colic vessels). The trial design required the LN yield to be higher in the CME group at interim analysis. RESULTS: Interim data analysis is presented in this report. The study enrolled 258 patients in nine referral centers. The number of LNs retrieved was significantly higher after CME (25 vs. 20; p = 0.012). No differences were observed with respect to intra- or post-operative complications, postoperative mortality, or duration of surgery. The hospital stay was even shorter after CME (p = 0.039). Quality of surgery indicators were higher in the CME arm of the study. Survival data still were not available. CONCLUSIONS: Interim data show that CME for right colon cancer in referral centers is safe and feasible and does not increase perioperative complications. The study documented with evidence that quality of surgery and LN yield are higher after CME, and this is essential for continuation of patient recruitment and implementation of an optimal comparison. Trial registration The trial was registered at ClinicalTrials.gov with the code NCT04871399 and with the acronym CoME-In trial.

Radiomics based on machine learning algorithms could predict prognosis and postoperative chemotherapy benefits of patients with gastric cancer: a retrospective cohort study.

Background: Traditional clinical characteristics have certain limitations in evaluating cancer prognosis. The radiomics features provide information on tumor morphology, tissue texture, and hemodynamics, which can accurately reflect personalized predictions. This study investigated the clinical value of radiomics features on contrast-enhanced computed tomography (CT) images in predicting prognosis and postoperative chemotherapy benefits for patients with gastric cancer (GC). Methods: For this study, 171 GC patients who underwent radical gastrectomy and pathology confirmation of the malignancy at the First Affiliated Hospital of Wenzhou Medical University were retrospectively enrolled. The general information, pathological characteristics, and postoperative chemotherapy information were collected. Patients were also monitored through telephone interviews or outpatient treatment. GC patients were randomly divided into the developing cohort (n=120) and validation cohort (n=51). The intra-tumor areas of interest inside the tumors were delineated, and 1,218 radiomics features were extracted. The optimal radiomics risk score (RRS) was constructed using 8 machine learning algorithms and 29 algorithm combinations. Furthermore, a radiomics nomogram that included clinicopathological characteristics was constructed and validated through univariate and multivariate Cox analyses. Results: Eleven prognosis-related features were selected, and an RRS was constructed. Kaplan-Meier curve analysis showed that the RRS had a high prognostic ability in the developing and validation cohorts (log-rank P<0.01). The RRS was higher in patients with a larger tumor size (≥3 cm), higher Charlson score (≥2), and higher clinical stage (Stages III and IV) (all P<0.001). Furthermore, GC patients with a higher RRS significantly benefited from postoperative chemotherapy. The results of univariate and multivariate Cox regression analyses demonstrated that the RRS was an independent risk factor for overall survival (OS) and disease-free survival (DFS) (P<0.001). A visual nomogram was established based on the significant factors in multivariate Cox analysis (P<0.05). The C-index was 0.835 (0.793-0.877) for OS and 0.733 (0.677-0.789) for DFS in the developing cohort. The calibration curve also showed that the nomogram had good agreement. Conclusions: A nomogram that combines the RRS and clinicopathological characteristics could serve as a novel noninvasive preoperative prediction model with the potential to accurately predict the prognosis and chemotherapy benefits of GC patients.