A Case Report of Unilateral Hypertrophy of Foot Intrinsics: Elastographic Properties of Hyperthrophied Muscles.

Congenital hypertrophy of intrinsic foot muscles is a rare condition. We report an unusual case of a 24-year-old male with a painless swelling at the plantar and medial aspect of the right foot, which was present since birth. The significant size of deformity and discomfort in wearing shoes were major concerns. MRI revealed expansion of multiple intrinsic foot muscles, which are abductor hallucis, flexor digitorum brevis, and quadratus plantae. We used shear wave elastography (SWE) as an imaging technique besides MRI and ultrasonography, which has not been used or published previously. Muscle shear wave velocity values were measured in abductor hallucis, flexor hallucis brevis, flexor digitorum brevis, and quadratus plantaris muscles. The mean stiffness values of muscles on the affected side were significantly higher compared to the healthy side. The median SWE value was 6.67 kPa (range: 4.7-8.6 kPa) on the healthy side, while it ranged between 9.2 and 13.4 kPa on the affected side. Total excision of abductor hallucis and subtotal resection of flexor digitorum brevis and quadratus plantae muscles were performed, and motor and sensory functions were preserved by protecting the neurovascular bundle. Excess skin was also removed. Surgery and postoperative course were uneventful. The patient was allowed to bear weight as tolerated. There was no recurrence, and the patient was satisfied with the shape and size of his foot at the six-month follow-up. Congenital hypertrophy of foot muscle is uncommon especially when multiple intrinsic foot muscles are involved. The main aim is to relieve patients' concerns, correct deformities, and provide comfortable shoe wear.

Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study.

BACKGROUND: The Prostate Imaging Reporting and Data System (PI-RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability. PURPOSE: To develop an artificial intelligence (AI) solution for PI-RADS classification and compare its performance with an expert radiologist using targeted biopsy results. STUDY TYPE: Retrospective study including data from our institution and the publicly available ProstateX dataset. POPULATION: In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI-RADS score >1) according to PI-RADSv2. FIELD STRENGTH/SEQUENCE: T2 -weighted, diffusion-weighted imaging (DWI; five evenly spaced b values between b = 0-750 s/mm2 ) for apparent diffusion coefficient (ADC) mapping, high b-value DWI (b = 1500 or 2000 s/mm2 ), and dynamic contrast-enhanced T1 -weighted series were obtained at 3.0T. ASSESSMENT: PI-RADS lesions were segmented by a radiologist. Bounding boxes around the T2 /ADC/high-b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI-RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy. STATISTICAL TESTS: Agreement between the AI and the radiologist-driven PI-RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test. RESULTS: For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI-RADS score in 86 patients undergoing targeted biopsy (P = 0.4-0.6). DATA CONCLUSION: We developed an AI system for assignment of a PI-RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Apical periurethral transition zone lesions: MRI and histology findings.

PURPOSE: Apical periurethral transition zone (TZ) cancers can pose unique problems for surgery and radiation therapy. Here, we describe the appearance of such cancers on multiparametric MRI (mpMRI) and correlate this with histopathology derived from MRI-targeted biopsy. MATERIALS AND METHODS: Between May 2011 and January 2019, a total of 4381 consecutive patients underwent 3 T mpMRI. Of these, 53 patients with 58 apical periurethral TZ lesions underwent TRUS/MRI fusion-guided biopsy and 12-core systematic TRUS-guided biopsy. Correlation was made with patient age, PSA, PSA density, whole prostate volume, and Gleason scores. RESULTS: A total 53 men (median age 68 years, median PSA 7.94 ng/ml) were identified as having at least one apical periurethral TZ lesion on mpMRI and 5 (9%) patients had more than one apical periurethral lesion. Thus, 58 lesions were identified in 53 patients. Of these 37/53 patients (69%) and 40/58 lesions were positive at biopsy for prostate cancer. Seven were diagnosed by 12-core systematic TRUS-guided biopsy and 34 were diagnosed by TRUS/MRI fusion-guided biopsy. Gleason score was ≥ 3 + 4 in 34/58 (58%) lesions. CONCLUSION: Identification of apical periurethral TZ prostate cancers is important to help guide surgical and radiation therapy as these tumors are adjacent to critical structures. Because of the tendency to undersample the periurethral zone during TRUS biopsy, MRI-guided biopsy is particularly helpful for detecting apical periurethral TZ prostate cancers many of which prove to be clinically significant.

Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.

Multimodality Imaging Findings of a Renal Aspergilloma.

BACKGROUND: Renal aspergillosis is a rare infection that usually occurs in persons with a predisposition for this condition. Its differential diagnosis includes primary and metastatic renal malignancies, pyelonephritis and secondary abscess formation, granulomatous disorders, and renal infarction. We aim to stress the role of multimodality imaging and percutaneous biopsy in the diagnosis of this condition. CASE REPORT: We present diffusion weighted imaging (DWI) and positron emission tomography-computed tomography (PET-CT) findings in addition to conventional imaging modalities in a 55-year-old man with secondary renal aspergilloma. CONCLUSION: Radiological imaging methods are an integral part of diagnostic workup for renal aspergillosis. A definitive diagnosis is made by histopathological and/or microbiological examination of the material obtained via percutaneous biopsy under guidance of imaging methods.