Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation.

OBJECTIVE: The aim of the study is to demonstrate whether radiomics based on an automatic segmentation method is feasible for predicting molecular subtypes. METHODS: This retrospective study included 516 patients with confirmed breast cancer. An automatic segmentation-3-dimensional UNet-based Convolutional Neural Networks, trained on our in-house data set-was applied to segment the regions of interest. A set of 1316 radiomics features per region of interest was extracted. Eighteen cross-combination radiomics methods-with 6 feature selection methods and 3 classifiers-were used for model selection. Model classification performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: The average dice similarity coefficient value of the automatic segmentation was 0.89. The radiomics models were predictive of 4 molecular subtypes with the best average: AUC = 0.8623, accuracy = 0.6596, sensitivity = 0.6383, and specificity = 0.8775. For luminal versus nonluminal subtypes, AUC = 0.8788 (95% confidence interval [CI], 0.8505-0.9071), accuracy = 0.7756, sensitivity = 0.7973, and specificity = 0.7466. For human epidermal growth factor receptor 2 (HER2)-enriched versus non-HER2-enriched subtypes, AUC = 0.8676 (95% CI, 0.8370-0.8982), accuracy = 0.7737, sensitivity = 0.8859, and specificity = 0.7283. For triple-negative breast cancer versus non-triple-negative breast cancer subtypes, AUC = 0.9335 (95% CI, 0.9027-0.9643), accuracy = 0.9110, sensitivity = 0.4444, and specificity = 0.9865. CONCLUSIONS: Radiomics based on automatic segmentation of magnetic resonance imaging can predict breast cancer of 4 molecular subtypes noninvasively and is potentially applicable in large samples.

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging.

Purpose: In clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI). Materials and methods: A total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity. Results: In the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10-15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3. Conclusions: Our model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.

Interventional radiology followed by endoscopic drainage for pancreatic fluid collections associated with high bleeding risk: Two case reports.

BACKGROUND: Endoscopic ultrasound (EUS)-guided transluminal drainage is an advanced technique used to treat pancreatic fluid collections (PFCs). However, gastric varices and intervening vessels may be associated with a high risk of bleeding and are, therefore, listed as relative contraindications. Herein, we report two patients who underwent interventional embolization before EUS-guided drainage. CASE SUMMARY: Two 32-year-old males developed symptomatic PFCs after acute pancreatitis and came to our hospital for further treatment. One patient suffered from intermittent abdominal pain and vomiting, and computed tomography (CT) imaging showed an encapsulated cyst 7.93 cm × 6.13 cm in size. The other patient complained of a mass inside the abdomen, which gradually became enlarged. Gastric varices around the ideal puncture site were detected by EUS when we evaluated the possibility of endoscopic drainage in both patients. Interventional embolization was recommended as the first procedure to decrease the risk of bleeding. After that, EUS-guided transluminal drainage was successfully conducted, without vascular rupture. No postoperative complications occurred during hospitalization, and no recurrence was detected at the last follow-up CT scan performed at 1 mo. CONCLUSION: Interventional embolization is a safe, preoperative procedure that is performed before EUS-guided drainage in PFC patients with gastric varices or at high risk of bleeding.