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Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography.

ABSTRACT: Importance:Preoperative TNM stratification of colon cancer on computed tomography (CT) does not identify patients who are at high risk of recurrence that could be selected for preoperative treatment. Objective:To evaluate the utility of CT findings for prognosis of sigmoid colon cancer. Design, Setting, and Participants:This prognostic study used retrospective data from patients who underwent bowel resection for sigmoid colon cancer between January 1, 2006, and January 1, 2015, at a tertiary care center receiving international and national referrals for colorectal cancer. Statistical analysis was performed in April 2019. Main Outcomes and Measures:Cox proportional hazards regression analysis was performed to investigate CT findings associated with disease recurrence. Kaplan-Meier survival plots were calculated for disease-free survival using CT staging systems. Results:Of the 414 patients who had sigmoid colon cancer (248 [60.0%] men; mean [SD] age, 66.1 [12.7] years), with median follow-up of 61 months (interquartile range, 40-87 months), 122 patients (29.5%) developed disease recurrence. On multivariate analysis, nodal disease was not associated with disease recurrence; only tumor deposits (hazard ratio [HR], 1.90; 95% CI, 1.21-2.98; P?=?.006) and extramural venous invasion (HR, 1.97; 95% CI, 1.26-3.06; P?=?.003) on CT were associated with disease recurrence. Significant differences in disease-free survival were found using CT-T3 substage classification (HR, 1.88; 95% CI, 1.32-2.68) but not CT-TNM (HR, 1.55; 95% CI, 0.94-2.55). The presence of tumor deposits or extramural venous invasion on CT (HR, 2.45; 95% CI, 1.68-3.56) had the strongest association with poor outcome. Conclusions and Relevance:In this study, T3 substaging and detection of tumor deposits or extramural venous invasion on preoperative CT scans of sigmoid colon cancer were prognostic factors for disease-free survival, whereas TNM and nodal staging on CT had no prognostic value. T3 substaging and detection of tumor deposits or extramural venous invasion of sigmoid colon cancer was superior to TNM on CT and could be used to preoperatively identify patients at high risk of recurrence.

SUBMITTER: D'Souza N

PROVIDER: S-EPMC6902773 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography.

D'Souza Nigel N Shaw Annabel A Lord Amy A Balyasnikova Svetlana S Abulafi Muti M Tekkis Paris P Brown Gina G

JAMA network open 20191202 12

<h4>Importance</h4>Preoperative TNM stratification of colon cancer on computed tomography (CT) does not identify patients who are at high risk of recurrence that could be selected for preoperative treatment.<h4>Objective</h4>To evaluate the utility of CT findings for prognosis of sigmoid colon cancer.<h4>Design, setting, and participants</h4>This prognostic study used retrospective data from patients who underwent bowel resection for sigmoid colon cancer between January 1, 2006, and January 1, 2 ...[more]

PMID: 31808924

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Project description:To investigate the association between computed tomography (CT)-detected extramural venous invasion (EMVI)-related genes and immunotherapy resistance and immune escape in patients with gastric cancer (GC). Thirteen patients with pathologically proven locally advanced GC who had undergone preoperative abdominal contrast-enhanced CT and radical resection surgery were included in this study. Transcriptome sequencing was multidetector performed on the cancerous tissue obtained during surgery, and EMVI-related genes (P value for association < 0.001) were selected. A single-sample gene set enrichment analysis algorithm was also used to divide all GC samples (n = 377) in The Cancer Genome Atlas (TCGA) database into high and low EMVI-immune related groups based on immune-related differential genes. Cluster analysis was used to classify EMVI-immune-related genotypes, and survival among patients was validated in TCGA and Gene Expression Omnibus (GEO) cohorts. The EMVI scores were calculated using principal component analysis (PCA), and GC samples were divided into high and low EMVI score groups. Microsatellite instability (MSI) status, tumor mutation burden (TMB), response rate to immune checkpoint inhibitors (ICIs), immune escape were compared between the high and low EMVI score groups. Hub gene of the model in pan-cancer analysis was also performed. There were 17 EMVI-immune-related genes used for cluster analysis. PCA identified 8 genes (PCH17, SEMA6B, GJA4, CD34, ACVRL1, SOX17, CXCL12, DYSF) that were used to calculate EMVI scores. High EMVI score groups had lower MSI, TMB and response rate of ICIs, status but higher immune escape status. Among the 8 genes used for EMVI scores, CXCL12 and SOX17 were at the core of the protein-protein interaction (PPI) network and had a higher priority in pan-cancer analysis. Immunohistochemical analysis showed that the expression of CXCL12 and SOX17 was significantly higher in CT-detected EMVI-positive samples than in EMVI-negative samples (P < 0.0001). A CT-detected EMVI gene signature could be a potential negative biomarker for ICIs treatment, as the signature is negatively correlated with TMB, and MSI, resulting in poorer prognosis.

Project description:BackgroundStructured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in colon cancer during the staging phase in order to improve communication between the radiologist, members of multidisciplinary teams and patients.Materials and methodsA panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.ResultsThe final SR version was built by including n = 18 items in the "Patient Clinical Data" section, n = 7 items in the "Clinical Evaluation" section, n = 9 items in the "Imaging Protocol" section and n = 29 items in the "Report" section. Overall, 63 items were included in the final version of the SR. Both in the first and second round, all sections received a higher than good rating: a mean value of 4.6 and range 3.6-4.9 in the first round; a mean value of 5.0 and range 4.9-5 in the second round. In the first round, Cronbach's alpha (Cα) correlation coefficient was a questionable 0.61. In the first round, the overall mean score of the experts and the sum of scores for the structured report were 4.6 (range 1-5) and 1111 (mean value 74.07, STD 4.85), respectively. In the second round, Cronbach's alpha (Cα) correlation coefficient was an acceptable 0.70. In the second round, the overall mean score of the experts and the sum of score for structured report were 4.9 (range 4-5) and 1108 (mean value 79.14, STD 1.83), respectively. The overall mean score obtained by the experts in the second round was higher than the overall mean score of the first round, with a lower standard deviation value to underline greater agreement among the experts for the structured report reached in this round.ConclusionsA wide implementation of SR is of critical importance in order to offer referring physicians and patients optimum quality of service and to provide researchers with the best quality data in the context of big data exploitation of available clinical data. Implementation is a complex procedure, requiring mature technology to successfully address the multiple challenges of user-friendliness, organization and interoperability.

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Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography.

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Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography.

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