CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

RATIONALE AND OBJECTIVES: Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs). MATERIALS AND METHODS: The present study included 201 patients with PPGLs from three hospitals (training set: n = 125; external validation set: n = 45; external test set: n = 31). Patients were divided into low-risk and high-risk groups using a staging system for adrenal pheochromocytoma and paraganglioma (GAPP). We extracted and selected CT radiomics features, and built radiomics models using support vector machines (SVM), k-nearest neighbors, random forests, and multilayer perceptrons. Using receiver operating characteristic curve analysis to select the optimal radiomics model, a combined model was built using the output of the optimal radiomics model and clinical radiological features, and its accuracy and clinical applicability were evaluated using calibration curves and clinical decision curve analysis (DCA). RESULTS: Finally, 13 radiomics features were selected to construct machine learning models. In the radiomics model, the SVM model demonstrated higher accuracy and stability, with an AUC value of 0.915 in the training set, 0.846 in external validation set, and 0.857 in external test set. Combining the outputs of SVM models with two clinical radiological features, a combined model constructed has demonstrated optimal risk stratification ability for PPGLs with an AUC of 0.926 for the training set, 0.883 for the external validation set, and 0.899 for the external test set. The calibration curve and DCA show good calibration accuracy and clinical effectiveness for the combined model. CONCLUSION: Combined model that integrates radiomics and clinical radiological features can discriminate the risk stratification of PPGLs.

Radiomics Based on DCE-MRI for Predicting Response to Neoadjuvant Therapy in Breast Cancer.

RATIONALE AND OBJECTIVES: To compare the value of radiomics and diameter% based on pre- and early-treatment dynamic enhanced MR (DCE-MRI) of the breast in predicting response to neoadjuvant therapy (NAT) in breast cancer and to construct a tool for early noninvasive prediction of NAT outcomes. MATERIALS AND METHODS: Retrospective analysis of clinical and imaging data of 142 patients with primary invasive breast cancer who underwent DCE-MRI before and after two cycles of NAT at our institution. Enroled patients were randomly assigned in a 7:3 ratio to the training group and the test group. Patients were divided into pathological complete response (pCR) and non-pathological complete response groups based on surgical pathology findings after NAT. The maximum diameter relative regression values (Diameter%) before and after treatment were calculated and the conventional imaging Diameter% model was constructed. Based on pre- and early-NAT DCE-MRI, the optimal features of pre-NAT, early-NAT, and delta radiomics were screened using redundancy analysis, least absolute shrinkage, and selection operator methods to construct the corresponding radiomics model and calculate the Radscores. Indicators that were statistically significant in the univariate analysis of clinical data were further screened by stepwise regression and combined with Radscores to construct the fusion model. All models were evaluated and compared. RESULTS: In the test set, the area under the curve (AUC) of the delta radiomics model (0.87) was higher than that of the pre-NAT, early-NAT radiomics models (0.57, 0.78) and the Diameter% model (0.83). The fusion model had the best efficacy in predicting pCR after NAT, with AUCs of 0.91 in the training and test sets. And its nomogram plot showed that Radscore of early-NAT radiomics had the greatest weight. In the test set, the fusion model and Delta radiomics model improved the efficacy of predicting pCR by 35.56% and 14.19%, respectively, compared to the Diameter% model (P = 0 and .039). Clinical decision curves showed the highest overall clinical benefit for the fusion model. CONCLUSION: Radiomics, especially delta and early-NAT radiomics, may be potential biomarkers for early noninvasive prediction of NAT outcomes. And a fusion model constructed from meaningful clinicopathological indicators combined with radiomics can effectively predict NAT response.

Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy.

RATIONALE AND OBJECTIVES: To investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics at baseline and after two cycles of neoadjuvant therapy (NAT) and associated longitudinal changes for early prediction of the NAT response in patients with breast cancer. MATERIALS AND METHODS: One hundred seventeen patients with breast cancer who underwent DCE-MRI before NAT and after two cycles of NAT from April 2019 to November 2021 were enrolled retrospectively. Patients were randomly divided into a training set (n = 81) and a test set (n = 36) at a ratio of 7:3. Clinical-pathological data and the relative tumor maximum diameter regression value (diameter%) were also collected. A total of 851 radiomic features were extracted from the phase with the most pronounced tumor enhancement on DCE-MRI T1 imaging acquired both pre- and post-treatment. Delta and delta% radiomics features were also calculated. The Least Absolute Shrinkage and Selection Operator (LASSO) method was applied to select features, and a logistic regression model was used to calculate pre-NAT, early-NAT, delta, and delta% radscores and then select among four radscores to build a Fusion radiomics model. The final clinical-radiomics model was constructed by combining fusion radscores and clinical-pathological variables. The discrimination and clinical utility of the models were further evaluated and compared. RESULTS: The area under the curve (AUC) values of the fusion radiomics model based on pre-NAT, Delta, and Delta% radscores were 0.868 of 0.825. The clinical-radiomics model integrating Fusion radscores and clinical-pathological variables achieved AUC values of 0.920 of 0.884, which were higher than those of the clinical model constructed by AUC values (0.858/0.831), although no significant improvement was observed in the test set (Delong test, p = 0.196). Decision curve analysis (DCA) showed that the clinical-radiomics model demonstrated more clinical utility than the clinical model. CONCLUSION: DCE-MRI-based radiomics features may have potential for pathological complete response (pCR) prediction in the early phase of NAT. By combining radiomics features and clinical-pathological characteristics, higher diagnostic performance can be achieved.

Imaging features of granular cell tumor in the breast: Case report.

RATIONALE: Granular cell tumor of the breast (GCTB) is a benign rare tumor. There are limited reports on its imaging manifestations. GCTB is often misdiagnosed as breast cancer, which results in unnecessary radical mastectomy and excessive treatment. In this article, we have reported a case of a 56-year-old postmenopausal woman with GCTB and highlighted the imaging features to differentiate this rare tumor from breast cancer. PATIENT CONCERNS: A 56-year-old postmenopausal patient had a chief complaint of a subcutaneous nodule in the upper outer quadrant of her right breast for 2 months. She underwent physical examination, color Doppler ultrasonography, mammography, magnetic resonance, and postoperative pathology. DIAGNOSES: The final diagnosis was GCTB. The tumor cells were intermingled with the fibrous stroma and normal breast parenchyma and showed positive immunoreaction to S-100, CD68, and neuron-specific enolase. INTERVENTIONS: The patient underwent lumpectomy and sentinel lymph node biopsy. OUTCOMES: The patient recovered well after lumpectomy and had no complications during the 2-year follow-up. LESSONS: There are some important imaging features of GCTB that can be used to distinguish it from breast carcinoma to reduce misdiagnosis.