Sclerosing lesions of the breast encompass a spectrum of benign and malignant entities and often pose a diagnostic challenge. Awareness of key morphologic features and pitfalls in the assessment of morphology and immunophenotype is essential to avoid over- or underdiagnosis and ensure optimal clinical management. This review summarizes nonneoplastic sclerosing lesions such as radial scar/complex sclerosing lesion, sclerosing adenosis, sclerosing intraductal papilloma, sclerosing variants of ductal adenoma and nipple adenoma, and fibroadenoma with extensive sclerosis, including their clinical presentation, characteristic morphology, differential diagnostic considerations, appropriate immunohistochemical work-up, when needed, and the clinical significance. In addition, atypical or neoplastic entities (such as atypical ductal hyperplasia, ductal carcinoma in situ, low-grade adenosquamous carcinoma, and fibromatosis-like metaplastic carcinoma) that can involve these sclerosing lesions are also briefly discussed.

UNASSIGNED: To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA.
UNASSIGNED: A total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed.
UNASSIGNED: There was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (P<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (P<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets.
UNASSIGNED: SA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.

UNASSIGNED: We aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsies.
UNASSIGNED: From January 2020 to March 2022, 345 cases of SA or IDC that were pathologically confirmed were included in the study. All participants underwent pre-surgical ultrasound (US), from which clinical information and ultrasound images were collected. The patients from the study population were randomly divided into a training cohort (n = 208) and a validation cohort (n = 137). The US images were imported into MaZda software (Version 4.2.6.0) to delineate the region of interest (ROI) and extract features. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of the extracted features. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation were performed to obtain the radiomics score of the features. Based on univariate and multivariate logistic regression analyses, a model was developed. 56 cases from April 2022 to December 2022 were included for independent validation of the model. The diagnostic performance of the model and the radiomics scores were evaluated by performing the receiver operating characteristic (ROC) analysis. The calibration curve and decision curve analysis (DCA) were used for calibration and evaluation. Leave-One-Out Cross-Validation (LOOCV) was used for the stability of the model.
UNASSIGNED: Three predictors were selected to develop the model, including radiomics score, palpable mass and BI-RADS. In the training cohort, validation cohort and independent validation cohort, AUC of the model and radiomics score were 0.978 and 0.907, 0.946 and 0.886, 0.951 and 0.779, respectively. The model showed a statistically significant difference compared with the radiomics score (p<0.05). The Kappa value of the model was 0.79 based on LOOCV. The Brier score, calibration curve, and DCA showed the model had a good calibration and clinical usefulness.
UNASSIGNED: The model based on radiomics, ultrasonic features, and clinical manifestations can be used to distinguish SA from IDC, which showed good stability and diagnostic performance. The model can be considered a potential candidate diagnostic tool for breast lesions and can contribute to effective clinical diagnosis.

Sclerosing adenosis of the prostate (SAP) is a rare benign non-neoplastic small acinar hyperplasia. Like sclerosing adenosis of the breast, which is confused with breast cancer, SAP is a trap in the pathological differential diagnosis of benign and malignant lesions of the prostate. We report such a case to help colleagues better distinguish and diagnose such diseases. A 75-year-old patient with SAP had a prostate specific antigen (PSA) level of 11.0 ng/mL, and he had been suffering from progressive dysuria for 3 years. The central glandular area and the right periphery of the prostate were found to have nodular low signals on magnetic resonance imaging (MRI). Prostate biopsy showed that basal cells were positive for P63 and P504s, few basal cells were positive for S-100, and the positive rate of Ki67 was approximately 2%. We consider that the possibility of SAP is high. The patient was treated conservatively and was discharged in good health, free of dysuria and other problems. SAP is a rare benign lesion that is easily misdiagnosed as prostate cancer. The prostatic gland tube has a complete basal cell layer surrounding it, as well as myoepithelial cell metaplasia of basal cells, which is a key trait in distinguishing it from prostate cancer. Although the latest research indicates that SAP does not require treatment, the question of whether it is a risk factor for prostate cancer remains unanswered.

OBJECTIVE: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC).
METHODS: A total of 97 patients with SA and 100 patients with BC were included in this study. The best model for classification was selected from among four different convolutional neural network (CNN) models, including Vgg16, Resnet18, Resnet50, and Desenet121. The intra-/inter-class correlation coefficient and least absolute shrinkage and selection operator method were used for radiomics feature selection. The clinical features selected were patient age and nodule size. The overall accuracy, sensitivity, specificity, Youden index, positive predictive value, negative predictive value, and area under curve (AUC) value were calculated for comparison of diagnostic efficacy.
RESULTS: All the CNN models combined with radiomics and clinical data were significantly superior to CNN models only. The Desenet121+radiomics+clinical data model showed the best classification performance with an accuracy of 86.80%, sensitivity of 87.60%, specificity of 86.20% and AUC of 0.915, which was better than that of the CNN model only, which had an accuracy of 85.23%, sensitivity of 85.48%, specificity of 85.02%, and AUC of 0.870. In comparison, the diagnostic accuracy, sensitivity, specificity, and AUC value for breast radiologists were 72.08%, 100%, 43.30%, and 0.716, respectively.
CONCLUSIONS: A combination of the CNN-radiomics model and clinical data could be a helpful auxiliary diagnostic tool for distinguishing between SA and BC.

OBJECTIVE: To explore the diagnostic value of multimodal imaging techniques, including automatic breast volume scanner (ABVS), mammography (MG), and magnetic resonance (MRI) in breast sclerosing adenosis (SA) associated with malignant lesions.
METHODS: From January 2018 to October 2020, 76 patients (88 lesions) with pathologically confirmed as SA associated with malignant or benign lesions were retrospective analyzed. All patients completed ABVS examination, 58 patients (67 lesions) with MG and 50 patients (62 lesions) with MRI were also completed before biopsy or surgical excision, of which, six patients (eight lesions) diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 3 by all imaging examinations underwent surgical excision without biopsy, other 70 patients (80 lesions) with BI-RADS category 4 or above by any imaging examination completed biopsy, including 65 patients (75 lesions) were further surgical excised and the other five patients (five lesions) were just followed up. All lesions were retrospectively described and classified, and were divided into benign group and malignant group according to their pathological results. Image features of different examination methods between the two groups were compared and analyzed. A ROC curve was established using the sensitivity of BI-RADS categories to predict malignant lesions in different imaging techniques as the ordinate and 1-specificity as the abscissa.
RESULTS: 88 lesions including 26 purely SA and 45 SA associated with benign lesions were classified as benign group, and the remaining 17 SA associated with malignant lesions were classified as malignant group. On ABVS, 40 mass lesions, their heterogeneous echo, not circumscribed margin and coronal convergence signs were statistically significant for malignant lesions (p < .05), but the remain 48 nonmass lesions lack specific sonographic features. On MG, 12 showed negative results, 55 showed with microcalcification, mass, structural distortion, and asymmetric density shadow, of which 11 lesions had the above two signs at the same time, but only microcalcification had statistical difference between the two groups. 35 mass enhanced lesions and 27 nonmass enhanced lesions on MRI, but there were no significant difference between their pathological results. Time signal intensity curves showed no differences, but ADC value <1.10 × 10-3  mm2 /s is more significant in malignant lesions (p < .05). The area under the ROC curve (AUC) of BI-RADS classification of ABVS, MG, and MRI in the diagnosis of malignant lesions were 0.611, 0.474, and 0.751, respectively, and the AUC of the combined diagnosis of the three was 0.761.
CONCLUSIONS: Mass lesions with heterogeneous echo, not circumscribed margin and coronal convergence sign on ABVS, microcalcification on MG and the ADC value <1.10 × 10-3  mm2 /s on MRI are significant signs for SA associated with malignant lesions. The combined diagnosis of the three methods was the highest, and the following were MRI, ABVS, and MG. Therefore, be cognizant of significant characteristics in SA associated with malignancy showed in different imaging examinations can improve the preoperative evaluation of SA and better provide basis for subsequent clinical decision-making.

UNASSIGNED: Sclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA.
UNASSIGNED: Sixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation.
UNASSIGNED: In the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03).
UNASSIGNED: Radiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.

Squamous metaplasia of the breast is a rare and unusual finding. A number of benign and malignant differential entities exist when squamous cells are present in a breast lesion. Our patient was found to have pronounced squamous metaplasia and keratin cysts arising in a complex fibroadenoma. The rare nature of squamous metaplasia arising in such a lesion poses some diagnostic challenges, as squamous epithelium and squamous metaplasia in the breast may raise suspicion for malignancy. Herein we present a unique case and discussion of benign and malignant differential entities. We also retrospectively reviewed a series of complex fibroadenomas in our institution, including the demographic and histologic features, and more importantly the associated breast cancer risk.

Atypical apocrine adenosis (AAA) is a benign lesion of the breast that is identified more frequently today than in the past when it was considered a rare diagnosis and commonly misdiagnosed as other malignant lesions of the breast. AAA is defined as the presence of apocrine cytology in a recognisable lobular unit associated with sclerosing adenosis. We present a case of an incidental finding of AAA and discuss diagnostic challenges and their implications on clinical management.

BACKGROUND: Sclerosing adenosis (SA) is a benign lesion with complicated pathological components and could mimic breast carcinoma in both clinical palpation and medical imaging findings. The present study was conducted to assess the value of ultrasound (US) characteristics in diagnosing SA and their differentiation from breast carcinoma.
METHODS: We retrospectively reviewed the medical records of 305 women (347 lesions) with invasive ductal carcinoma (IDC) and 54 women with single SA lesion, who had breast excision between April 2016 and July 2018. US BI-RADS atlas and elastography were applied and their associated characteristics were compared between SA and IDC.
RESULTS: The mean age of SA was younger than that of IDC (43.6 ± 7.4 vs 53.2 ± 10.3, P < 0.001). Compared to IDC, SA had more frequency of parallel orientation (94.44% vs 71.76%, P < 0.001) and circumscribed margin (48.15% vs 4.90%, P < 0.001), less frequency of irregular shape (64.81% vs 95.97%, P < 0.001), hypoechoic echotexture (88.89% vs 98.27%, P = 0.002), calcification (12.96% vs 55.04%, P < 0.001), and posterior acoustic changes (3.70% vs 53.89%, P < 0.001) or associated features (architectural distortion, 3.70% vs 59.65%, P < 0.001; duct changes, 18.52% vs 63.40%, P < 0.001). Vascularity absence was more common in SA compared to IDC (35.19% vs 6.63%, P < 0.001). And the elasticity score was lower in SA (2.38 ± 0.60 vs 3.91 ± 0.81, P < 0.001). After adjusting for age, we found spiculated margin, posterior shadowing, calcification, architectural distortion, and vascularity could independently identify the differences between these two entities. After involving elasticity score, the calcification and vascularity could still be independent indicators for differential diagnosis.
CONCLUSIONS: Understanding SA imaging features will enable radiologists to communicate results to the referring physician consistently, which could benefit a reliable assessment and specific management recommendations. A systematic evaluation of the US BI-RADS atlas together with breast elastography may be a powerful tool to identify SA and differentiate it from breast cancer.