An MRI-based radiomics model performed better than visual assessment by experienced radiologists for diagnosing pathologic complete response in patients with locally advanced rectal cancer after ne... Background Preoperative assessment of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT) is increasingly needed for organ preserv...

Purpose: To evaluate the usefulness of the radiomic model in predicting early (≤2 years) and late (>2 years) recurrence after curative resection in cases involving a single hepatocellular carcinoma (HCC) 2–5 cm in diameter using preoperative gadoxetic acid–enhanced magnetic resonance imaging (MRI), in comparison with the clinicopathologic model. Experimental Design: This retrospective study included 167 patients with surgically resected and pathologically confirmed single HCC 2–5 cm in diameter (n = 167, training set:validation set = 128:39) who underwent preoperative gadoxetic acid–enhanced MRI between January 2010 and December 2015. A radiomic model, a clinicopathologic model, and a combined clinicopathologic-radiomic (CCR) model were built using a random survival forest to predict disease-free survival (DFS) in the following conditions: early DFS versus late DFS, dynamic phases, and the peritumoral area included in the segmentation. Results: The radiomic model showed a prognostic performance comparable with the clinicopathologic model only with 3-mm peritumoral border extension [c-index difference (radiomic-clinicopathologic), −0.021, P = 0.758]. The CCR model with the 3-mm border extension showed the highest c-index value but no statistically significant improvement over the clinicopathologic model [CCR, 0.716 (0.627–0.799); clinicopathologic model, 0.696 (0.557–0.799)]. Conclusions: The prognostic value of the preoperative radiomic model with 3-mm border extension showed comparable performance with that of the postoperative clinicopathologic model for predicting DFS of early recurrence of HCC using gadoxetic acid–enhanced MRI. This suggests the importance of including peritumoral changes in the radiomic analysis of HCC.

Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.

We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.