Academic Radiology最新文献

Rationale and objectives: The first-line treatment for prolactinoma is drug therapy with dopamine agonists (DAs). However, some patients with resistance to DA treatment should prioritize surgical treatment. Therefore, it is crucial to accurately identify the drug treatment response of prolactinoma before treatment. The present study was performed to determine the DA treatment response of prolactinoma using a clinical radiomic model that incorporated radiomic and clinical features before treatment.

Materials and methods: In total, 255 patients diagnosed with prolactinoma were retrospectively divided to training and validation sets. An elastic net algorithm was used to screen the radiomic features, and a fusion radiomic model was established. A clinical radiomic model was then constructed to integrate the fusion radiomic model and the most important clinical features through multivariate logistic regression analysis for individual prediction. The calibration, discrimination, and clinical applicability of the established models were evaluated. 60 patients with prolactinoma from other centers were used to validate the performance of the constructed model.

Results: The fusion radiomic model was constructed from three significant radiomic features, and the area under the curve in the training set and validation set was 0.930 and 0.910, respectively. The clinical radiomic model was constructed using the radiomic model and three clinical features. The model exhibited good recognition and calibration abilities as evidenced by its area under the curve of 0.96, 0.92, and 0.92 in the training, validation, and external multicenter validation set, respectively. Analysis of the decision curve showed that the fusion radiomic model and clinical radiomic model had good clinical application value for DA treatment response prediction in patients with prolactinoma.

Conclusion: Our clinical radiomic model demonstrated high sensitivity and excellent performance in predicting DA treatment response in prolactinoma. This model holds promise for the noninvasive development of individualized diagnosis and treatment strategies for patients with prolactinoma.

Purpose: To assess the diagnostic performance of Diffusion Weighted Imaging (DWI) and provide optimal apparent diffusion coefficient (ADC) cut-off values for differentiating between benign and metastatic lymph nodes in women with uterine cervical cancer.

Method: MEDLINE and EMBASE databases were searched. Methodological quality was assessed with QUADAS-2. Data analysis was performed for three subgroups: (1) All studies; (2) Studies with maximum b-values of 800 s/mm², and (3) Studies containing b-values of 1000 s/mm². Receiver-operating characteristics (ROC) curves were constructed and the area under the curve (AUC) was calculated. The maximum Youden index was used to determine optimal ADC cut-off values, following calculations of sensitivity and specificity.

Results: 16 articles (1156 patients) were included. Overall, their quality was limited. For all studies combined, the optimum ADC cut-off value was 0.985×10⁻³ mm²/s at maximum Youden Index of 0.77, resulting in sensitivity and specificity of 84%, and 94%, respectively. Studies with b-values up to 800 s/mm², gave an optimum ADC cut-off value of 0.985×10⁻³ mm²/s at maximum Youden Index of 0.62, with a sensitivity and specificity of 62%, and 100%. Studies containing b-values of 1000 s/mm² gave an optimum ADC cut-off value of 0.9435×10⁻³ mm²/s at maximum Youden Index of 0.93, with a sensitivity and specificity of 100%, and 93%, respectively.

Conclusion: Studies using DWI including b-values of 1000 s/mm² have higher sensitivity and specificity than those with b-values up to 800 s/mm². At the cut-off value of 0.9435×10⁻³ mm²/s DWI can sufficiently discriminate between benign and metastatic lymph nodes.

Rationale and objectives: The Association of Program Directors in Radiology (APDR) administers an annual survey to assess issues and experiences related to residency program management and education. Response data from the 2023 survey provides insights on the impact of COVID-19 on resident recruitment (Part I) and education (Part II), which can be used to facilitate planning and resource allocation for the evolving needs of programs and their leadership.

Materials and methods: An observational, cross-sectional study of the APDR membership was performed using a web-based survey consisting of 45 questions, 12 of which pertain to resident education in the post-pandemic era and are discussed in Part II of a two-part survey analysis. All active APDR members (n = 393) were invited to participate in the survey.

Results: The response rate was 32% (124 of 393). Results were tallied using Qualtrics software and qualitative responses were tabulated or summarized as comments.

Conclusions: The primary challenges to resident education are faculty burnout, rising case volumes, and remote instruction. However, most program leaders report that in-person readouts are much more common than remote readouts. The ability to offer both in-person and remote AIRP sessions is viewed positively. Most program leaders require Authorized User certification, although many do not think all residents need it. Assessment of procedural competence varies by the type of procedure and is similar to graduates' self-assessment of competence.

Rationale and objectives: The Association of Program Directors in Radiology (APDR) administers an annual survey to assess issues and experiences related to residency program management and education. Our purpose is to provide the response data from the 2023 survey and discuss its insights on the impact of COVID-19 on resident recruitment (Part I) and education (Part II), which can be used to facilitate planning and resource allocation for the evolving needs of programs and their leadership. In Part I, we consider the effects of ERAS preference signaling, the virtual interview format, and the potential of a universal interview release date.

Materials and methods: An observational, cross-sectional study of the APDR membership was performed using a web-based survey consisting of 45 questions, 23 of which pertain to virtual recruitment and are discussed in Part I of a two-part survey analysis. All active APDR members (n = 393) were invited to participate in the survey.

Results: The response rate was 32% (124 of 393). 83% reported that signaling increased the likelihood of an interview offer. 96% reported only offering virtual interviews; however, 59% intended to offer virtual-only interviews in the future. 53% would adhere to a universal interview release date but an additional 44% would do so depending on the agreed date, Results were tallied using Qualtrics software and qualitative responses were tabulated or summarized as comments.

Conclusions: Virtual recruitment is expected to continue for many programs and most respondents would accept a universal interview release date. Preference signaling and geographic signaling are considered positive additions to the application process.

Rationale and objectives: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pathological prediction outcomes by combining habitat analysis with deep learning.

Materials and methods: 387 cases of primary glioma from three hospitals were collected, along with their T1 contrast-enhanced and T2-weighted MR sequences, pathological reports and clinical histories. The training set consisted of 264 patients, 82 patients composed the test set, and 41 patients were used as the validation set for hyperparameter tuning and optimal model selection. All groups were sourced from different centers. Through radiomics, deep learning, habitat analysis and combined analysis, we extracted imaging features separately and jointly modeled them with clinical features. We identified the optimal models for predicting glioma grades, Ki67 expression levels, P53 mutation and IDH1 mutation.

Results: Using a LightGBM model with DenseNet161 features based on habitat subregions, the best tumor grade prediction model was achieved. A LightGBM model with ResNet50 features based on habitat subregions yielded the best Ki67 expression level prediction model. An SVM model with Radiomics and Inception_v3 features provided the best prediction of P53 mutation. The best model for predicting IDH1 mutation was achieved by an MLP model with Radiomics features based on habitat subregions. Clinical features might be potentially helpful for the prediction with relatively weak evidence.

Conclusion: Habitat+Deep Learning feature extraction methods were optimal for predicting grades and Ki67 levels. Deep Learning is optimal for predicting P53 mutation, while the combination of Habitat+ Radiomics models yielded the best prediction for IDH1 mutation.

Rationale and objectives: To investigate the clinical and computed tomography characteristics of inflammatory solid pulmonary nodules (SPNs) with morphology suggesting malignancy, hereinafter referred to as atypical inflammatory SPNs (AI-SPNs).

Materials and methods: The CT data of 515 patients with SPNs who underwent surgical resection were retrospectively analyzed. These patients were divided into inflammatory and malignant groups and their clinical and imaging features were compared. Binary logistic regression analysis was performed to identify the independent factors for diagnosing AI-SPNs. An external validation cohort included 133 consecutive patients to test the model's predictive efficiency.

Results: Univariate analysis showed that age < 62 years, male sex, maximum spiculation length > 9 mm, polygonal shapes, three-planar ratio > 1.48, Lung window/mediastinal window (L/M) ratio > 1.13, pleural tag type I, satellite lesions, and halo sign were more frequent in AI-SPNs, whereas pleural tag type III, bronchial truncation, and perifocal fibrosis were more common in malignant SPNs (M-SPNs) (all P < 0.05). Binary logistic regression showed age < 62 years, male sex, polygonal shape, three-planar ratio > 1.48, L/M ratio > 1.13, pleural tag type I, satellite lesions, halo sign, and absence of bronchial truncation were independent factors for diagnosing AI-SPNs (AUC, sensitivity, specificity, and accuracy of 0.951, 83.30%, 92.30%, and 87.20%, respectively). In the external validation cohort, the AUC, sensitivity, specificity, and accuracy were 0.969, 90.47%, 90.00%, and 90.23%, respectively.

Conclusion: AI-SPNs and M-SPNs exhibited different clinical and imaging characteristics. A good understanding of these differences may help reduce diagnostic errors in AI-SPNs and enable to choose an optimal treatment strategy.