Bizarre Parosteal Osteochondromatous Proliferation (Nora's Lesion) Of the Second Proximal Phalanx Encasing the Flexor Tendon of the Foot: A Case Report.

Introduction: Bizarre Parosteal Osteochondromatous Proliferation (BPOP) is a rare benign lesion commonly referred to as Nora's lesion. It typically affects adults in their 20s-30s. Due to its aggressive local invasion, it can be confused with some malignant tumors, including chondrosarcoma. Nora's lesion can be diagnosed radiographically, and its diagnosis is confirmed with an excisional biopsy. Case Report: A 40-year-old Bahraini male complained of swelling over the metatarsal head of the second digit, increasing in size over a year. He also complained of a reduced range of motion of the second toe and a pins-and-needles sensation affecting the entire toe. Plain X-ray, computed tomography, and magnetic resonance imaging were done, showing findings suggestive of bizarre parosteal osteochondromatous. The lesion was encasing the flexor tendon of the second digit. He was treated with surgical excision, and histopathology confirmed the diagnosis of BPOP. Conclusion: We report on a rare presentation of BPOP in the second proximal phalanx of a male in his 40 s. The patient underwent a wide local excision, and the diagnosis was confirmed with histopathology.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.

Introduction: Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment. Methods: Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated. Results: Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001). Conclusion: Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

Combined unicompartmental knee arthroplasty and anterior cruciate ligament reconstruction in a young patient with post-traumatic arthritis.

Post-traumatic arthritis can result in significant pain and difficulty in managing daily life activities. Multiple factors are weighed in selecting the proper surgical intervention, with patient age and level of activity being most important. Isolated osteoarthritis is a well-known indication for unicompartmental knee arthroplasty, where a better range of motion, preservation of natural knee kinematics and less invasive resection of knee joint bone are used. Moreover, the high improvement rate and long-term results after anterior cruciate ligament (ACL) reconstruction and restoration of knee stability can make the combined procedure favourable, particularly for young active patients.We report on an active man in his 30s presenting with isolated medial compartment advanced arthritis after sustaining distal femur intra-articular fracture. He was initially treated with partial unicompartmental knee replacement combined with ACL reconstruction, delivering a good short-term follow-up outcome.Though this case involves just a single patient, the positive outcome suggests that combined partial unicompartmental knee replacement with an ACL reconstruction should be considered for young and active patients diagnosed with isolated advanced medial compartment osteoarthritis.

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.