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C olorectal cancer (CRC) is the third most common cancer and ranks third as the cause of death among malignant neoplasms (1). Up to 21% of patients with CRC demonstrate distant disease at the time of diagnosis with a relative 5-year survival rate of 14% (1). Contrast material-enhanced CT is the diagnostic modality of choice for initial workup, staging, restaging, assessment of treatment response, and surveillance according to the National Comprehensive Cancer Network guidelines (2).Patients with CRC with specific mutation profiles may benefit from tailored therapies, and evidence-based guidelines for determination of tumor biomarkers such as BRAF, KRAS, NRAS, and microsatellite instability status were recently published (3). Tumors with BRAF mutation are resistant to anti-epidermal growth factor receptor therapeutic agents, leading to a poorer prognosis, whereas tumors with microsatellite instability have been shown to have a better prognosis (4,5). In one study, BRAF-mutated CRC tumors showed histopathologic features that were distinct from those of wild-type BRAF tumors, independent of microsatellite instability status (6). Typically, BRAF mutation is determined through genetic molecular profiling by sampling the tumor. However, biopsy is invasive, fraught with sampling-error limitations, and often does not represent the complete tumor heterogeneity.Interest in image biomarker development and validation in patients with cancer has led to substantial research efforts for extracting tumor radiomics features using computational models. These radiomics features have been shown to be a quantitative tool that relays information about tumor phenotype as well as clinical and genotypic end points (7)(8)(9)(10)(11). Preliminary studies have shown that CT radiomic features correlate with clinical outcomes in esophageal cancer, tumor grade in melanoma, tumor histologic findings in renal cell carcinoma, tumor hypoxia, and angiogenesis in non-small-cell lung cancer (12)(13)(14)(15). The value of radiomics in predicting malignant potential in pulmonary nodules was verified by Digumarthy et al ( 16). In another study, Meyer et al ( 17) demonstrated that

The decrease in the RCTFC muscle volume is proportional during the different stages of life, maintaining a constant ratio between its components (physiological RCTFC muscle atrophy). The time-saving segmentation method and volume ratio formula proposed in this study contribute to the management and understanding of rotator cuff tear/pathology.

Aim.To develop a CT predictor scale for the need for colectomy and to evaluate predictors of all-cause mortality within 30 days after diagnosis ofC. difficile infection (CDI).MethodsWe conducted a retrospective study of adult hospitalized patients whounderwent abdominal CT within 72 h of diagnosis of CDI.ResultsPresence of abnormal wall thickening in caecum (OR 8.0; CI 1.37–46.81; p = 0.021), transverse colon (OR 6.7; CI 1.15–35.60; p = 0.034), sigmoid colon (OR 12.6; CI 1.37–115.97; p = 0.025), pancolitis (OR 7.0; CI 1.36–36.01; p = 0.02) and bowel dilation (OR 16.5; CI 2.41–112.83; p = 0.004) predicted colectomy. With these values, a five parameter radiological scale from 0 to 24 was developed (sensitivity and NPV of 100%, cut-off of 6). Furthermore, wall thickening of caecum (OR 6.2; CI 1.06–35.57; p = 0.043), ascending colon (OR 12.0; CI 1.29–111.32; p = 0.029), descending colon (OR 17.0; CI 1.81–160.05; p = 0.013) and sigmoid (OR 10.2; CI 1.10–94.10; p = 0.041) independently predicted mortality within 30 days of CDI diagnosis.ConclusionWe designed a CT scale to predict colectomy, able to rule out the development of fulminant colitis and the need for surgical procedure. Patients with wall thickening of the caecum, ascending, descending or sigmoid colon were more likely to die within 30 days of CDI diagnosis.