BackgroundIt is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.MethodsThis retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists’ evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.ResultsThe areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists’ evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.ConclusionsThe radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.
Purpose To identify and quantify lung changes associated with coronavirus disease-2019 (COVID-19) with quantitative lung CT during the disease. Methods This retrospective study reviewed COVID-19 patients who underwent multiple chest CT scans during their disease course. Quantitative lung CT was used to determine the nature and volume of lung involvement. A semi-quantitative scoring system was also used to evaluate lung lesions. Results This study included eighteen cases (4 cases in mild type, 10 cases in moderate type, 4 cases in severe type, and without critical type cases) with confirmed COVID-19. Patients had a mean hospitalized period of 24.1 ± 7.1 days (range: 14-38 days) and underwent an average CT scans of 3.9 ± 1.6 (range: 2-8). The total volumes of lung abnormalities reached a peak of 8.8 ± 4.1 days (range: 2-14 days). The ground-glass opacity (GGO) volume percentage was higher than the consolidative opacity (CO) volume percentage on the first CT examination (Z = 2.229, P = 0.026), and there was no significant difference between the GGO volume percentage and that of CO at the peak stage (Z =-0.628, P = 0.53). The volume percentage of lung involvement identified by AI demonstrated a strong correlation with the total CT scores at each stage (r = 0.873, P = 0.0001). Conclusions Quantitative lung CT can automatically identify the nature of lung involvement and quantify the dynamic changes of lung lesions on CT during COVID-19. For patients who recovered from COVID-19, GGO was the predominant imaging feature on the initial CT scan, while GGO and CO were the main appearances at peak stage.
Background: Preoperative staging of pancreatic cancer determines the choice of treatment. Magnetic resonance imaging (MRI) plays an important role in preoperative staging of pancreatic cancer. The American Joint Committee on Cancer (AJCC) TNM staging system was revised to its 8 th version in 2016, there has been no report correlating the 8 th edition of the AJCC TNM staging with preoperative MRI examinations and pathological findings. The purpose of our study is to determine the staging accuracy and evaluate the resectability by using MRI about pancreatic cancer compared with intraoperative or pathological findings according to the 8 th edition of the AJCC TNM staging system.Methods: One hundred thirty-two patients with a pathological diagnosis of pancreatic cancer who underwent preoperative MRI were identified. The clinical data, MRI findings and pathological findings were analyzed. Preoperative MRI staging and resectability evaluation were compared with pathological findings.The accuracy of MRI for preoperative T and N staging was evaluated, and the sensitivity, specificity and accuracy of MRI in evaluating the resectability were assessed. All the staging and resectability assessments were according to the 8 th edition of the AJCC TNM staging system.Results: Analysis showed that the accuracy of MRI for evaluation of the T and N stages was 82.6% (109/132) and 74.2% (98/132), respectively. The sensitivity and specificity of MRI in assessing the resectability were 94.2% and 71.4%, respectively. Integrating the 8 th edition of the AJCC TNM stage, no significant differences were identified between the preoperative MRI and pathological results for the staging of pancreatic cancer (P=0.805).Conclusions: MRI is highly accurate for T staging and moderately accurate for N staging. MRI provides important preoperative evaluation of the stage and resectability of pancreatic cancer based on the 8 th edition of the AJCC TNM staging system.
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