No abstract available

No abstract available

[en] Previous studies have suggested that the intratumoral texture features may reflect the tumor recurrence risk in intrahepatic cholangiocarcinoma (ICC). The peritumoral features may be associated with the distribution of microsatellites. Therefore, integrating the imaging features based on intratumoral and peritumoral areas may provide more accurate predictions in tumor recurrence (both early and late recurrences) than the predictions conducted based on the intratumoral area only. This retrospective study included 209 ICC patients. We divided the patient population into two sub-groups according to the order of diagnosis time: a training cohort (159 patients) and an independent validation cohort (50 patients). The MR imaging features were quantified based on the intratumoral and peritumoral (3 and 5 mm) areas. The radiomics signatures, clinical factor-based models and combined radiomics-clinical models were developed to predict the tumor recurrence. The prediction performance was measured based on the validation cohort using the area under receiver operating characteristic curve (AUC) index. For the prediction of early recurrence, the combined radiomics-clinical model of intratumoral area with 5 mm peritumoral area showed the highest performance (0.852(95% confidence interval (CI), 0.724–0.937)). The AUC for the clinical factor-based model was 0.805(95%CI, 0.668–0.903). For the prediction of late recurrence, the radiomics signature of intratumoral area with 5 mm peritumoral area had the optimal performance with an AUC of 0.735(95%CI, 0.591–0.850). The clinical factor-based showed inferior performance (0.598(95%CI, 0.450–0.735)). For both early and late recurrences prediction, the optimal models were all constructed using imaging features extracted based on intratumoral and peritumoral areas together. These suggested the importance of involving the intratumoral and peritumoral areas in the radiomics studies. (paper)

[en] Highlights: • The differentiation between GBM and solitary MET is a radiological challenge. • The Radscore was significant in the differentiation between GBM and solitary MET. • The radiomics classifier based on CE-T1WI yielded good performance. • The radiomics signature will facilitate the differentiation of GBM from solitary MET. To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis.

[en] Highlights: • The methodological quality of CT radiomics predicting invasive adenocarcinomas needs to be improved. • CT radiomics predicting invasive adenocarcinomas shows encouraging. • Future studies should focus on prospective, independent validation, and appropriate analysis. To provide an overview of the available studies investigating the use of computer tomography (CT) radiomics features for differentiating invasive adenocarcinomas (IAC) from indolent lung adenocarcinomas presenting as ground-glass nodules (GGNs), to identify the bias of the studies and to propose directions for future research.

[en] The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 ± 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19. (paper)

[en] To develop and evaluate a CT-based radiomics nomogram for differentiating gastric neuroendocrine carcinomas (NECs) from gastric adenocarcinomas (ADCs).

[en] Highlights: • Quantitative 3D radiomics features are extracted and analyzed. • A radiomics nomogram was developed for differentiating MIA and IAC groups in patients with sub-solid pulmonary nodules. • The proposed nomogram yields 0.943 AUC and 0.912 AUC in the training set and validation set, respectively. -- Abstract: To evaluate the preoperative differentiation between the minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) in patients with sub-solid pulmonary nodules using a radiomics nomogram.

[en] Highlights: • The relationships between ultrasound features and different liver focal lesions. • Differentiating infected focal liver lesions and malignant hepatic tumours. • Radiomics signature has good predictive potential in identifying focal liver lesions. To establish an ultrasound-based radiomics model through machine learning methods and then to assess the ability of the model to differentiate infected focal liver lesions from malignant mimickers.

[en] Highlights: • CT texture analysis can well differentiate TCFA and non-TCFA lesions. • Radiomics model using CT performs better at identifying TCFA compared with conventional HRP features model and FAI model. • CT Texture analysis can help evaluate vulnerable plaque determined by OCT. To explore whether CT texture analysis can identify thin-cap fibroatheroma (TCFA) determined by optical coherence tomography (OCT).