Objective: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. Methods: 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. Results: 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591–0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). Conclusion: The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. Advances in knowledge: ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.

To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROI_Intra) was dilated by 2, 4, 6 and 8 mm (ROI_Peri2mm, ROI_Peri4mm, ROI_Peri6mm, and ROI_Peri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat‐saturated T2‐weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648–0.778), 0.706 (95% CI, 0.637–0.768), and 0.719 (95% CI, 0.651–0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686–0.810), 0.754 (95% CI, 0.688–0.812) and 0.725 (95% CI, 0.657–0.786), respectively, in the test set. Combining three sequences in the ROI_Intra, ROI_Peri2mm, ROI_Peri4mm, ROI_Peri6mm and ROI_Peri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733–0.849) in the test set. This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.

To study the imaging and clinical features of breast sclerosing adenosis (SA), and to enhance the recognition of this disease, as well as to help the clinic to give a correct diagnosis. Imaging findings were retrospectively reviewed in 47 women with SA lesions confirmed by pathology (including 39 cases of mammography, 40 cases of ultrasound [US], and 34 cases magnetic resonance imaging [MRI]). Of 47 patients confirmed with SA, 18 cases were pure SA, and 29 cases coexist with other proliferative lesions and malignancies; the maximum diameter of SA lesions was 0.5 to 3.5 cm with an average of 1.6 cm. On the mammogram of 39 SA cases, the percentage of architectural distortion, calcifications, mass/nodular, asymmetric density, and mass combining with calcifications were 30.8%, 23.1%, 17.9%, 12.8%, and 7.7%, respectively; and 3 cases had no abnormal findings. On the sonogram (excluding 5 normal finding cases), the majority of lesions showed regular shaped (57.1%), well defined margined (60.0%), heterogenous low echoed (71.4%) nodulus. 85.3% lesions showed high signal on T2-weighted images, and all lesions were enhanced markedly, including 82.4% lesions appearing mass-like enhancement (17 star-shaped enhanced masses included); and the percentage of the time-signal intensity curve in type 1, type 2, and type 3 were 52.9%, 41.2%, and 5.9%, respectively. If the category breast imaging-reporting and data system ≥4b was considered to be a suspicious malignant lesion, the misdiagnostic rates of mammography, US, and MRI would be 17.9%, 17.5%, and 35.3%, respectively. The SA lesions are small and can occur with other diseases histologically. The majority of SA lesions showed distortion or calcifications on mammograms, low echo-level nodules with heterogenous echo on US and mass-like lesion with or without star shape on enhanced MRI.

Background: Primitive neuroectodermal tumors (PNETs) constitute a rare type of malignant neuroectodermal tumors that have chromosomal translocations identical to Ewing’s sarcoma (ES), and the characteristics of this disease remain unclear. Purpose: To describe the clinical, radiological, and pathological features of peripheral PNETs (pPNETs) to enhance their recognition. Material and Methods: The clinical, imaging, and pathologic findings of 35 patients with pPNETs were retrospectively reviewed, all being confirmed by biopsy or surgical pathology. All 35 patients had preoperative computed tomography (CT) examinations; 10 patients had preoperative magnetic resonance imaging (MRI) studies. Results: Of 35 pPNET patients, 54.3% had a primary tumor in soft tissue, the others in bone. On plain CT images, 33 lesions demonstrated heterogeneous hypodense masses with multiple lamellar lower density, and with osteolytic destruction if the tumor originated in bone. Calcification was only found in five lesions arising in soft tissue. All lesions enhanced heterogeneously with varying areas of cystic changes, and all lesions in bone and 52.6% of lesions in soft tissue showed ill-defined margins after contrast administration. On MRI, these tumors appeared in conjunction with osteolytic bone destruction and irregular soft tissue masses iso- to hypointense to skeletal muscle on T1-weighted images and showed heterogeneously high intensity on T2-weighted images. All lesions enhanced heterogeneously with cystic changes. Homer–Wright rosettes were observed in 15 lesions, and 97.1% lesions were positive for CD99 in histopathological results. Conclusion: pPNETs can involve any part of the body, and a large, ill-defined, aggressive soft tissue mass and heterogeneous enhancement with or without osteolytic bone destruction on CT or MR images could suggest the diagnosis.

Background: Core biopsy sampling may not fully capture tumor heterogeneity. Radiomics provides a non-invasive method to assess tumor characteristics, including both the core and surrounding tissue, with the potential to improve the accuracy of HER-2 status prediction. Objective: To explore the clinical value of intratumoral and peritumoral radiomics features from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for preoperative prediction of human epidermal growth factor receptor-2 (HER-2) expression status in breast cancer. Methods: Two tasks were designed, including Task1-distinguished HER-2 positive and HER-2 negative from 382 breast cancer patients and Task2-distinguished HER-2 low and HER-2 zero expression from 249 patients with HER-2 negative. Three radiomics models (intratumoral, peritumoral 5 mm, intratumoral+peritumoral 5 mm) were constructed based on decision tree, and clinical combined radiomics models were constructed with logistic regression based on clinicopathological features and radscore. The area under the curve (AUC), sensitivity, specificity, accuracy and decision curve analysis (DCA) were used to evaluate the predictive performance of models. Results: Estrogen receptor (ER), progesterone receptor (PR) and Ki67 showed statistically significant in the different groups of HER- 2 expression. Additionally, magnetic resonance imaging-reported axillary lymph nodes (MRI-reported ALN) in the positive and negative groups and histological grade in the low and zero expression groups showed significant differences (all P < 0.05). For task 1, the peritumoral radiomics model outperformed the other two radiomics models, with AUC values of 0.774 and 0.727 in the training and testing sets, respectively. For task 2, intratumoral + peritumoral radiomics model in the testing set showed the best predictive performance among the three radiomics models, and the AUC values were 0.777. The addition of clinicopathological features slightly altered the AUC values in both tasks. Conclusion: Both radiomics methods based on DCE-MRI and the nomogram are helpful for preoperative prediction of HER-2 expression status.

To describe the clinical, computed tomography (CT), and pathological findings of pediatric peripheral PNET (pPNET) to enhance the recognition of these rare tumors. The clinical, CT and pathologic findings of 18 pediatric patients with pPNET confirmed by biopsy or surgical pathology were retrospectively reviewed. The age of these 18 pediatric pPNET patients ranged from 4 months to 15 years, with a mean age of 7.7 years. The lesion locations of these 18 pediatric pPNET patients were located in head and neck (n=4), chest (n=2), abdomen and pelvic cavity (n=6), spine (n=3), ilium (n=2) and femur (n=1). Immunohistochemical examination revealed Homer–Wright rosettes in 7 lesions, and 94.4% lesions showed consistent positive staining for CD99. On plain CT images, the majority of pPNET showed such lesions that were ill-defined (72.2%), irregular shaped (83.3%), heterogeneous (66.7%) or hypodense mass (94.4%); and together with osteolytic bone destruction when the lesion originated in the bone. Calcifications were found in three lesions. After contrast administration, all soft tissue masses were persistently enhanced heterogeneously with various cystic or necrotic regions, and 71.4% of them had linear enhancement. 94.4% of soft tissue masses showed a moderate degree of enhancement. Seven cases had lymph node metastasis at diagnosis. Pediatric pPNET can involve any part of the body, and a large, ill-defined, aggressive soft tissue mass and moderate heterogeneous enhancement with varying cystic regions and linear enhancement, with or without osteolytic bone destruction on CT images could suggest the diagnosis. PNETs constitute a rare type of malignant neuroectodermal tumors that have chromosomal translocations identical to Ewing’s sarcoma (ES), and the reports about radiological characteristics of this disease in children are insufficient. This study has described the clinical features, computed tomography (CT) and pathological findings in 18 pediatric patients diagnosed as pPNET in different locations, as a way to enhance the recognition of these tumors and help to differentiate from other types of pediatric malignant bone and soft tissue tumors.