Multiphase Dual-Energy Spectral CT-Based Deep Learning Method for the Noninvasive Prediction of Head and Neck Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer.

Purpose: To develop deep learning (DL) models based on multiphase dual-energy spectral CT for predicting lymph nodes metastasis preoperatively and noninvasively in papillary thyroid cancer patients. Methods: A total of 293 lymph nodes from 78 papillary thyroid cancer patients who underwent dual-energy spectral CT before lymphadenectomy were enrolled in this retrospective study. The lymph nodes were randomly divided into a development set and an independent testing set following a 4:1 ratio. Four single-modality DL models based on CT-A model, CT-V model, Iodine-A model and Iodine-V model and a multichannel DL model incorporating all modalities (Combined model) were proposed for the prediction of lymph nodes metastasis. A CT-feature model was also built on the selected CT image features. The model performance was evaluated with respect to discrimination, calibration and clinical usefulness. In addition, the diagnostic performance of the Combined model was also compared with four radiologists in the independent test set. Results: The AUCs of the CT-A, CT-V, Iodine-A, Iodine-V and CT-feature models were 0.865, 0.849, 0.791, 0.785 and 0.746 in the development set and 0.830, 0.822, 0.744, 0.739 and 0.732 in the testing set. The Combined model had outperformed the other models and achieved the best performance with AUCs yielding 0.890 in the development set and 0.865 in the independent testing set. The Combined model showed good calibration, and the decision curve analysis demonstrated that the net benefit of the Combined model was higher than that of the other models across the majority of threshold probabilities. The Combined model also showed noninferior diagnostic capability compared with the senior radiologists and significantly outperformed the junior radiologists, and the interobserver agreement of junior radiologists was also improved after artificial intelligence assistance. Conclusion: The Combined model integrating both CT images and iodine maps of the arterial and venous phases showed good performance in predicting lymph nodes metastasis in papillary thyroid cancer patients, which could facilitate clinical decision-making.

Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT.

PURPOSE: To investigate the diagnostic value and feasibility of radiomics-based texture analysis in differentiating pulmonary sclerosing pneumocytoma (PSP) from solid malignant pulmonary nodules (SMPN) on single- and three-phase computed tomography (CT) images. MATERIALS AND METHODS: A total of 25 PSP patients and 35 SMPN patients with pathologically confirmed results were retrospectively included in this study. For each patient, the tumor regions were manually labeled in images acquired at the noncontrast phase (NCP), arterial phase (AP), and venous phase (VP). The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features extracted from the CT images. The predictive models that discriminate PSP from SMPN based on single-phase CT images (NCP, AP, and VP) or three-phase CT images (Combined model) were developed and validated through fivefold cross-validation using a logistic regression classifier. Model performance was evaluated using receiver operating characteristic (ROC) analysis. The predictive performance was also compared between the Combined model and human readers. RESULTS: Four, five, and five features were selected from NCP, AP, and VP CT images for the development of radiomic models, respectively. The NCP, AP, and VP models exhibited areas under the curve (AUCs) of 0.748 (95% confidence interval [CI], 0.620-0.852), 0.749 (95% CI, 0.620-0.852), and 0.790 (95% CI, 0.665-0.884) in the validation dataset, respectively. The Combined model based on three-phase CT images outperformed the NCP, AP, and VP models (all p < 0.05), yielding an AUC of 0.882 (95% CI, 0.773-0.951) in the validation dataset. The Combined model displayed noninferior performance compared to two senior radiologists; however, it outperformed two junior radiologists (p = 0.004 and 0.001, respectively). CONCLUSION: The Combined model based on radiomic features extracted from three-phase CT images achieved radiologist-level performance and could be used as promising noninvasive tool to differentiate PSP from SMPN.

Imaging and treatment of idiopathic abdominal cocoon in 9 patients.

The aim of the current study was to investigate the imaging features of abdominal cocoon (AC), the methods of diagnosis and treatment of AC and improve understanding of AC. To do this, the current study retrospectively analyzed the clinical data and imaging findings of nine patients with AC. The clinical manifestations included intestinal obstruction in six out of the nine cases of AC, which included two cases of bowel strangulation and five cases of soft masses in the abdomen. Imaging features of AC included the following: i) Bowel loops were usually present in a fixed cluster; ii) bowel loops were encapsulated partially or totally by a thickening fibrous membrane-like 'cocoon'; iii) bowel wall thickened in the 'cocoon', and the bowel loops and sac adhered; iv) loculated ascites in the 'cocoon'; v) abnormal clustering of the mesenteric vasculature, hypoplasia of the omentum majus was present or absent; and vi) Ileus occurred in some cases. The features of a plain abdominal X-ray, a gastrointestinal barium meal series and ultrasonography lacked specificity. The results of CT in eight out of nine cases of AC were consistent with the surgical findings. Surgery was the first choice of therapy. All cases were treated surgically, showing that the bowel loops were encapsulated partially or totally by a thickened fibrous membrane. All cases received operations including partial or total excision of the membrane and enterolysis. In conclusion, CT may be highly valuable in the preoperative diagnosis of AC.

Application Value of Magnetic Resonance Perfusion Imaging in the Early Diagnosis of Rat Hepatic Fibrosis.

OBJECTIVE: To assess the application value of perfusion-weighted imaging (PWI) in early diagnosis, quantitation, and hepatic fibrosis staging by analyzing the related parameters in hepatic fibrosis. METHODS: A total of 60 rats were randomly divided into the hepatic fibrosis and control groups, and carbon tetrachloride (CCL4) was used to establish the liver fibrosis model. All rats underwent PWI examination, and the trend of the time-signal intensity curve (TIC, automatically generated by the software) was observed. Also, the perfusion parameters, maximum signal reduction ratio (SRRmax), time to peak (TTP), and mean transit time (MTT), were analyzed and compared with pathological staging. RESULTS: The TIC curve was characterized by slow wash-in and wash-out with a low and wide peak. The PWI perfusion parameters were statistically significant in specific groups (P < 0.05): SRRmax values (control group and F3, F4), TTP, and MTT values (control group and F2-F4, F1 and F3, F1 and F4, and F2 and F4 in addition to TTP values for F1 and F2). Pearson's correlation analysis showed a negative correlation of SRRmax with hepatic fibrosis stage (r = -0.439, P < 0.05), while TTP and MTT values were positively correlated with hepatic fibrosis stage (TTP, r = 0.798; MTT, r = 0.647; all P < 0.001). CONCLUSIONS: PWI perfusion parameters reflect the degree of hepatic fibrosis, especially TTP and MTT, and PWI is recommended for the early diagnosis of liver fibrosis for timely intervention and treatment of the disease and delaying its progression.