European radiology
-
To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions. ⋯ • The CNN model had good performance for detection of pneumothorax on chest radiographs after PTNB and showed high specificity and negative predictive value. • The CNN model found all cases of pneumothorax requiring drainage after PTNB. • The CNN model can be used as a screening tool prior to radiologist interpretation.
-
To compare whole-body MRI (WB-MRI) at 1.5/3T and bone scintigraphy in the skeletal staging of Ewing sarcoma (ES) of bone. ⋯ • Whole-body MRI is more sensitive than bone scintigraphy in detecting skeletal metastases in Ewing sarcoma of bone. • Whole-body MRI can safely replace bone scintigraphy for staging of the skeleton, with the acknowledgement of the possibility of missing a clinically occult skull vault metastasis.
-
To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer. ⋯ • A deep learning-based CAD system could accurately classify cervical lymph node metastasis. The AUROC for the eight tested algorithms ranged from 0.909 to 0.953. • Of the eight models, the ResNet50 algorithm was the best-performing model for the validation dataset with 0.953 AUROC. The sensitivity, specificity, and accuracy of the ResNet50 model were all 90.4%, respectively, in the test dataset. • Based on its high accuracy of 90.4%, we consider that this model may be useful in a clinical setting to detect LNM on preoperative CT in patients with thyroid cancer.
-
To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI. ⋯ • Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.
-
To construct a radiomics nomogram for the individualized estimation of the survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI, which could facilitate the clinical decision-making for GBM patients. ⋯ • Non-invasive survival stratification of GBM patients can be obtained with a radiomics nomogram. • The proposed nomogram constructed by radiomics signature selected from 4000 radiomics features, combined with independent clinical risk factors such as age, Karnofsky performance status, and treatment strategy. • The proposed radiomics nomogram exhibited good calibration and discrimination for survival stratification of GBM patients in both training (C-index, 0.971) and validation (C-index, 0.974) sets.