The present review aimed to systematically review skin toxicity changes following breast cancer radiotherapy (RT) using ultrasound (US). PubMed and Scopus databases were searched according to PRISMA guidelines. The characteristics of the selected studies, measured parameters, US skin findings, and their association with clinical assessments were extracted. Seventeen studies were included with a median sample size of 29 (range 11–166). There were significant US skin changes in the irradiated skin compared to the nonirradiated skin or baseline measurements. The most observed change is skin thickening secondary to radiation-induced oedema, except one study found skin thinning after pure postmastectomy RT. However, eight studies reported skin thickening predated RT attributed to axillary surgery. Four studies used US radiofrequency (RF) signals and found a decrease in the hypodermis’s Pearson correlation coefficient (PCC). Three studies reported decreased dermal echogenicity and poor visibility of the dermis–subcutaneous fat boundary (statistically analysed by one report). The present review revealed significant ultrasonographic skin toxicity changes in the irradiated skin most commonly skin thickening. However, further studies with large cohorts, appropriate US protocol, and baseline evaluation are needed. Measuring other US skin parameters and statistically evaluating the degree of the association with clinical assessments are also encouraged.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.

Pulmonary sclerosing pneumocytoma (PSP) is a rare condition, occupying only approximately 1% of all benign lung neoplasms and the description of this disease through a radiological perspective is limited. Only approximately a third of PSP has been correctly diagnosed pre-operatively through contrast-enhanced computed tomography (CECT) thorax. To date, only few radiological features have been described to ascertain this disease and there is still a significant portion of patients being misdiagnosed with a malignant neoplasm. We report a case of PSP that was initially misdiagnosed as lung adenocarcinoma stage IIA and the histopathological findings of surgical resection confirmed the diagnosis of PSP. We try to add some points from this case into the previous radiological description of this uncommon disease with a common presentation.

Neurological disorders are among the leading causes of disability and mortality worldwide, making timely and accurate diagnosis essential. Differential diagnosis (DD) of Multiple Sclerosis (MS) and Neuromyelitis Optica (NMO) spectrum disorder using brain MRI remains challenging due to overlapping clinical manifestations and similar lesion characteristics. To address this challenge, this study proposes a neuroinformatics-driven computer-aided differential diagnosis (CADD) framework that automatically extracts, encodes, and analyzes quantitative 3D lesion biomarkers from brain MRI using deep learning and interpretable neuroimaging analytics. The proposed framework integrates three artificial intelligence (AI) modules: the NeuroImaging Sequence Examiner (NISE) for MRI sequence identification, the NeuroImaging Volumetric Extractor (NIVE) for skull stripping, and the NeuroImaging Lesion Extractor (NILE) for lesion segmentation. These modules are combined into an end-to-end neuroinformatics framework termed NeuroImaging Differential Evaluation (NIDE), which performs automated neuroimaging processing, lesion biomarker extraction, structured feature encoding, and differential diagnosis. Following lesion segmentation, quantitative 3D morphological and geometric lesion properties are extracted and transformed into a structured neuroimaging representation, where each row corresponds to an individual lesion and each column encodes a specific lesion biomarker. The resulting feature matrix is subsequently classified using a 2D convolutional neural network (2D-CNN). Comparative experiments against 3D-CNN models trained on raw MRI volumes, skull-stripped MRI volumes, lesion-mask volumes, and conventional machine learning classifiers demonstrate the superiority of the proposed lesion-driven learning strategy. Using ResNet50, the proposed framework achieved an accuracy of 80% for MS–NMO spectrum disorder differential diagnosis. Furthermore, the framework is independent of MRI view-plane orientation, enabling robust performance across different imaging orientations while operating in a fully automated manner. Beyond classification performance, the framework provides a structured representation of quantitative lesion biomarkers and enables interpretable neuroimaging analytics through Explainable Artificial Intelligence (XAI), with model interpretations corroborated by expert radiological assessment. The proposed NIDE framework demonstrates how automated neuroimaging processing, quantitative lesion characterization, structured feature encoding, and explainable AI can be integrated into a unified neuroinformatics workflow for differential diagnosis. These findings highlight the potential of neuroinformatics-driven decision-support systems for advancing neuroimaging analysis and clinical management of demyelinating neurological disorders.