PURPOSE: To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases. MATERIALS AND METHODS: Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained. RESULTS: There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets ( P < 0.05). They were the roundness, concavity, gray average and skewness of breast lesions. Compared with non-TNBC, TNBC cases have following characteristics of (1) more round with the roundness of 0.621 vs. 0.413 ( P < 0.001), (2) more regular with the concavity of 0.087 vs. 0.141 ( P < 0.01), (3) higher density or gray average (67.261 vs. 56.842, P < 0.05), and (4) lower skewness (– 0.837 vs.– 0.671, P = 0.034). AUCs of ROC curves computed using features of the roundness and concavity were both larger than 0.70. CONCLUSION: Radiomics features based on X-ray mammography may be helpful to distinguish between TNBC and non-TNBC, which were associated with breast tumor histology.

To delineate the features of multi-detector computed tomography (MDCT) images and clinical characteristics of pancreatic solid pseudopapillary tumors (SPTs) of the pancreas in asymptomatic patients and compare these features and characteristics between asymptomatic and symptomatic patients. This work is a retrospective study approved by our institutional review board. MDCT images and clinical data of 109 patients with pathologically proven SPTs obtained from October 2008 to October 2016 were reviewed. Patients were categorized into two groups: asymptomatic patients and patients with symptomatic disease. Cases were reviewed to determine the reason for detection, intervention, shape, diameter, location, calcification, encapsulation, internal composition, CT attenuation, enhancement pattern, and tumor pathology. Clinical factors and imaging features were also compared between groups. Statistical analysis was performed using χ2 and t-tests. Data from 49 asymptomatic and 60 symptomatic patients were collected. Asymptomatic SPTs were identified most frequently during routine health examination (18 patients, 36.7%), various screening purposes (12 patients, 24.5%), and traumatic injury (9 patients, 18.4%). Except for a smaller tumor size (5.8 cm in asymptomatic SPTs vs. 7.4 cm in symptomatic SPTs, P = 0.023), the clinical factors or imaging features of asymptomatic patients were very similar to those of symptomatic patients. The current research is the first single-center study to characterize SPTs in asymptomatic patients. Asymptomatic SPTs are gradually being identified with greater frequency. Although generally smaller in size than that in symptomatic patients, an asymptomatic pancreatic mass with the typical imaging features of SPT may be found, the treatment for which is similar to that for symptomatic patients. Evaluating asymptomatic SPTs requires further systematic and multi-center trials.

Background: Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer. Methods: This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II–V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity. Results: Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE_40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74–0.91), with a sensitivity of 77.8% and specificity of 87.9%. Conclusions: The model combining MonoE_40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans. Critical relevance statement: The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans. Key points: • Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications. Graphical