Purpose Measurement of tumor size is critical for thyroid tumor management; however, optimal measurement methods and evaluation criteria remain unclear. This study aimed to quantitatively compare the papillary thyroid microcarcinoma tumor size measured by ultrasound, gross examination, and histological assessment and to determine how discrepancies between these different measurements may influence clinical decision making, such as indications for fine-needle aspiration, eligibility for active surveillance, and surgical management. Materials and methods This study included 412 papillary thyroid carcinoma cases with tumors of < 15 mm on preoperative ultrasound examination. Results The reduction rates of gross and histological sizes compared to ultrasound sizes were 93.5% and 86.9%, respectively. Significant differences were observed between all combinations of ultrasound, gross, and histological sizes ( p< 0.001). Papillary thyroid microcarcinoma accounted for 42.0%, 55.6%, and 65.1% of cases, respectively. In 23 cases, the difference between the ultrasound and gross or histological sizes was >5 mm. This was because they were not cut at the maximal plane, were located in the isthmus or inferior pole, were associated with chronic thyroiditis, and were lobulated. Conclusions We demonstrated that the size of papillary thyroid carcinoma depends on the measurement method used. Gross and histological tumor sizes tended to be smaller than ultrasonographic sizes. Clinicians should be aware that tumors slightly exceeding 10 mm on ultrasound may ultimately be classified as papillary thyroid microcarcinoma postoperatively. Tumor location, shape, and coexisting thyroiditis can all contribute to measurement errors in ultrasound.
Purpose:The lumbar plexus, formed by the anterior rami of T12/L1 to L4/L5, provides the main neural supply to the lower abdominal wall, groin, and anterior-medial thigh. Its principal peripheral branches (the iliohypogastric, ilioinguinal, genitofemoral, lateral femoral cutaneous, femoral, and obturator nerves) are clinically relevant targets for invasive diagnostic and therapeutic procedures, including nerve blocks and neuromodulatory techniques. This manual aims to describe how to visualize these six main lumbar plexus branches using ultrasound guidance. Materials and methods:For each nerve, this guide summarizes its typical anatomical course, key clinical considerations related to neuropathy or iatrogenic injury, the most relevant anatomical landmarks, recommended transducer positioning, and characteristic sonographic appearance. The text is complemented by illustrations of probe placement, schematic depictions of nerve pathways, and representative ultrasound images. Results:This manual provides a structured ultrasound-based approach for identifying the main lumbar plexus branches in the lower abdominal wall, groin, and thigh. It details the anatomical and sonographic features that facilitate nerve recognition and supports precise probe positioning and needle guidance when these nerves are targeted in clinical practice. Conclusions:Ultrasound guidance is encouraged when performing procedures involving lumbar plexus branches, as it may improve procedural accuracy and patient safety by enabling precise needle placement while reducing the risk of injury to adjacent non-target structures. This manual offers a practical anatomical and sonographic reference for clinicians performing ultrasound-guided interventions in this region.
Purpose To develop and validate an end-to-end interactive software that integrates lung ultrasound radiomics and machine learning for the objective assessment of pediatric pneumonia severity at the point of care. Materials and methods This retrospective study included a dataset of lung ultrasound images from 293 pediatric patients (157 with mild pneumonia and 136 with severe pneumonia). A total of 104 radiomics features were extracted from clinician-delineated regions of interest. Feature selection was performed using Least Absolute Shrinkage and Selection Operator regression, and 10 machine learning algorithms were constructed and evaluated. The optimal model was interpreted using SHapley Additive exPlanations. The finalized classifier was integrated into an interactive software platform that guides users from image upload to severity prediction. Results The Light Gradient Boosting Machine classifier, which forms the core of the software, demonstrated superior performance on the independent test set, achieving an accuracy of 89.8%, a sensitivity of 91.7%, a specificity of 88.6%, and an area under the curve of 90.1%. SHapley Additive exPlanation analysis identified and ranked the contribution of key predictive features, with morphological characteristics being the most influential. Conclusions We present an end-to-end interactive software that successfully leverages lung ultrasound radiomics and machine learning to provide an objective, accurate, and rapid assessment of pediatric pneumonia severity. This tool has significant potential to standardize diagnosis and support clinical decision-making in real-world settings.
