ObjectivesTo develop and validate a nomogram to predict the overall survival (OS) of patients with primary nodal diffuse large B-cell lymphoma(N-DLBCL) based on radiomic features and clinical features.Materials and methodsA retrospective analysis was performed on 145 patients confirmed with N-DLBCL and they were randomly assigned to training set(n=78), internal validation set(n=33), external validation set(n=34). First, a clinical model (model 1) was established according to clinical features and ultrasound (US) results. Then, based on the radiomics features extracted from conventional ultrasound images, a radiomic signature was constructed (model 2), and the radiomics score (Rad-Score) was calculated. Finally, a comprehensive model was established (model 3) combined with Rad-score and clinical features. Receiver operating characteristic (ROC) curves were employed to evaluate the performance of model 1, model 2 and model 3. Based on model 3, we plotted a nomogram. Calibration curves were used to test the effectiveness of the nomogram, and decision curve analysis (DCA) was used to asset the nomogram in clinical use.ResultsAccording to multivariate analysis, 3 clinical features and Rad-score were finally selected to construct the model 3, which showed better predictive value for OS in patients with N-DLBCL than mode 1 and model 2 in training (AUC,0. 891 vs. 0.779 vs.0.756), internal validation (AUC, 0.868 vs. 0.713, vs.0.756) and external validation (AUC, 914 vs. 0.866, vs.0.789) sets. Decision curve analysis demonstrated that the nomogram based on model 3 was more clinically useful than the other two models.ConclusionThe developed nomogram is a useful tool for precisely analyzing the prognosis of N-DLBCL patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.
Fine needle aspiration biopsy is a crucial method for preoperative diagnosis of thyroid nodules. However, thyroid nodules classified as Bethesda categories III–V cannot obtain definite cytological results. Our aim was to study the diagnostic value of thyroid imaging reporting and data system combined with BRAFV600E mutation analysis in Bethesda categories III–V thyroid nodules, so as to provide more precise direction for the follow-up treatments. A total of 174 Bethesda categories III–V thyroid nodules performed TIRADS and BRAFV600E mutation analysis were included in the study. We retrospectively analyzed the ultrasound features as well as the results of BRAFV600E mutation of the 174 thyroid nodules. In the multiple regression analysis models, ultrasound features including lobulated or irregular margin, punctate echogenic foci, and shape with taller-than-wide were statistically significant in malignant nodules (p < 0.05). The area under the curve of the combination of TIRADS and BRAFV600E increased to 0.925, which were much higher than TIRADS (0.861) and BRAFV600E (0.804) separately. Combined diagnosis was of the greatest value to identify Bethesda III–V thyroid nodules definitely, especially with higher sensitivity (93%) and accuracy (90%).
The purpose of this paper was to assess the value of ultrasonography in the prognosis of diffuse large b-cell lymphoma (DLBCL) by developing a new prognostic model. One hundred and eleven DLBCL patients with complete clinical information and ultrasound findings were enrolled in our study. Univariate and multivariate regression analyses were used to identify independent risk factors for progression-free survival (PFS) and overall survival (OS). Receiver operator characteristic (ROC) curves were plotted and the corresponding area under the curve (AUC) was calculated to assess the accuracy of the international prognostic index (IPI) and new model in DLBCL risk stratification. The results suggested that hilum loss and ineffective treatment were independent risk variables for both PFS and OS in DLBCL patients. Additionally, the new model that added hilum loss and ineffective treatment to IPI had a better AUC for PFS and OS than IPI alone (AUC: 0.90, 0.88, and 0.82 vs. 0.71, 0.74, and 0.68 for 1-, 3-, and 5-year PFS, respectively; AUC: 0.92, 0.85 and 0.86 vs. 0.71, 0.75 and 0.76, for 1-, 3-, and 5-year OS, respectively). The model based on ultrasound images could better suggest PFS and OS of DLBCL, allowing for better risk stratification.
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