Lymphoma Affecting the Breast: A Pictorial Review of Multimodal Imaging Findings

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.     

Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment

ObjectiveTo 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 MethodsA 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.ResultsThe 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).ConclusionThe 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.     

The “polka-dot” sign

Abdominal Radiology -     

Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction

Purpose:To compare the image quality of CT obtained using a deep learning-based image reconstruction (DLIR) engine with images with adaptive statistical iterative reconstruction-V (AV).Materials and Methods:Using a phantom, the noise power spectrum (NPS) and task-based transfer function (TTF) were measured in images with different reconstructions (filtered back projection [FBP], AV30, 50, 100, DLIR-L, M, H) at multiple doses. One hundred and twenty abdominal CTs with 30% dose reduction were processed using AV30, AV50, DLIR-L, M, H. Objective and subjective analyses were performed.Results:The NPS peak of DLIR was lower than that of AV30 or AV50. Compared with AV30, the NPS average spatial frequencies were higher with DLIR-L or DLIR-M. For lower contrast objects, TTF in images with DLIR were higher than those with AV. The standard deviation in DLIR-H and DLIR-M was significantly lower than AV30 and AV50. The overall image quality was the best for DLIR-M (p < 0.001).Conclusions:DLIR showed improved image quality and decreased noise under a decreased radiation dose.