Hematological malignancies rarely affect the breast, and the majority of those that do are lymphomas. In this review, we describe the clinical aspects and multimodal imaging findings of breast lymphoma. We also illustrate the key clinical and radiological findings that allow it to be distinguished from various other malignant and benign diseases of the breast. Breast lymphoma manifests as a breast mass, a change in the subcutaneous tissue or the skin, or enlargement of the associated lymph node on radiological examination. Radiological findings associated with other breast malignancies, such as calcifications, spiculations, or architectural distortions are extremely rare. Skin and subcutaneous changes frequently accompany T-cell lymphoma. Multimodal breast imaging characteristics may aid in the diagnosis of breast lymphoma.
Breast ; Hematologic Neoplasms ; Lymph Nodes ; Lymphoma ; Lymphoma, T-Cell ; Magnetic Resonance Imaging ; Skin ; Subcutaneous Tissue

The use of autologous tissue transfer for breast augmentation is an alternative to using foreign implant materials. The benefits of this method are the removal of unwanted fat from other body parts, no risk of implant rupture, and the same feel as real breast tissue. However, sometimes there is a dilemma about whether or not to biopsy for calcifications or masses detected after the procedure is completed. The purpose of this study is to illustrate the procedures of breast augmentation with autologous tissues, the imaging features of various complications, and the role of imaging in the diagnosis and management of complications and hidden breast diseases.
Biopsy ; Breast ; Breast Diseases ; Female ; Human Body ; Mammaplasty ; Mammography ; Rupture

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists’ bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33– 0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.