Higher Soong grade predicts flexor tendon issues after volar plating of distal radius fractures - a retrospective cohort study.

BACKGROUND AND PURPOSE: Soong classification is used to estimate volar locking plate prominence and evaluate the risk for flexor tendon ruptures after surgical treatment of distal radius fractures (DRFs). However, the scientific community has questioned the Soong classification due to lacking evidence. Therefore, this study aimed to evaluate the accuracy of Soong grading as a predictor for flexor tendon issues and plate removal. PATIENTS AND METHODS: We performed a retrospective single-center review of adult distal radius fracture patients treated with a volar locking plate between 2009 and 2019. In total, 2779 patients were included in the study. The primary outcome was a flexor tendon issue (flexor tendon rupture, tendinitis, or flexor irritation), whereas plate removal was a secondary outcome. Using Soong grade 0 as a reference, we used univariable and multivariable logistic regression to calculate odds ratios (OR) with 95% confidence intervals (CI) for flexor tendon issues and plate removal. RESULTS: In total, 756 (27%) patients were graded as Soong 0, 1679 (60%) Soong 1, and 344 (12%) Soong 2, respectively. There were 32 (1.2%) patients with flexor tendon issues, of which 4 were flexor tendon ruptures, 8 tendinitises, and 20 flexor irritations. The adjusted OR for flexor tendon issues was 4.4 (CI 1.1-39.7) for Soong grade 1 and 9.7 (CI 2.2-91.1) for Soong grade 2. The plate was removed from 167 (6.0%) patients. Soong grade 1 had a univariable OR of 1.8 (CI 1.2-2.8) for plate removal, and Soong grade 2 had an OR of 3.5. (CI 2.1-5.8), respectively. CONCLUSION: Flexor tendon ruptures are rare complications after the volar plating of DRFs. A higher Soong grade is a risk factor for flexor tendon issues and plate removal. TRIAL REGISTRATION: The trial was retrospectively registered.

Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model.

Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture displacement are critical steps. The aim of this study was to use pixel-level annotations of fractures to develop a deep learning model for precise distal radius fracture detection. We randomly divided 3785 consecutive emergency wrist radiograph examinations from six hospitals to a training set (3399 examinations) and test set (386 examinations). The training set was used to develop the deep learning model and the test set to assess its validity. The consensus of three hand surgeons was used as the gold standard for the test set. The area under the ROC curve was 0.97 (CI 0.95-0.98) and 0.95 (CI 0.92-0.98) for examinations without a cast. Fractures were identified with higher accuracy in the postero-anterior radiographs than in the lateral radiographs. Our deep learning model performed well in our multi-hospital and multi-radiograph system manufacturer settings. Thus, segmentation-based deep learning models may provide additional benefit. Further research is needed with algorithm comparison and external validation.

Jugulotympanic paragangliomas in southern Finland: a 40-year experience suggests individualized surgical management.

Treatment of jugulotympanic paragangliomas (JTPGLs) remains challenging with no clear guidelines for management or follow-up. The aim of this retrospective case-note study was to assess long-term results of operatively and conservatively managed JTPGLs between years 1974-2013. A total of 36 patients with JTPGLs were identified. Clinical characteristics and management outcomes of patients were reviewed. Data were extracted on demographics, symptoms, timing of diagnosis, tumor location and size, embolization, and management, including pre- and post-operative imaging, analysis of operative techniques, and follow-up. Pulsatile tinnitus and hearing loss were the most common presenting symptoms. Thirty-four (94 %) patients were treated with primary surgical therapy and two (6 %) with radiotherapy. The surgical approaches included endaural approach for Fisch Class A tumors and a variety of approaches for Fisch Class B-D tumors with an increasing predilection for function-preserving surgery. Eight (24 %) patients received subtotal resection. Five (15 %) patients had a local recurrence within 10 years after primary surgery. Two (6 %) patients suffered a permanent cranial nerve (CN) deficit after primary surgery. We advocate radical surgery when tumor resection is possible without compromising CNs. Function-preserving surgery with at least a 10-year follow-up for Fisch Class B-D tumors should be considered if CNs are in danger.

Detecting Avascular Necrosis of the Lunate from Radiographs Using a Deep-Learning Model.

Deep-learning (DL) algorithms have the potential to change medical image classification and diagnostics in the coming decade. Delayed diagnosis and treatment of avascular necrosis (AVN) of the lunate may have a detrimental effect on patient hand function. The aim of this study was to use a segmentation-based DL model to diagnose AVN of the lunate from wrist postero-anterior radiographs. A total of 319 radiographs of the diseased lunate and 1228 control radiographs were gathered from Helsinki University Central Hospital database. Of these, 10% were separated to form a test set for model validation. MRI confirmed the absence of disease. In cases of AVN of the lunate, a hand surgeon at Helsinki University Hospital validated the accurate diagnosis using either MRI or radiography. For detection of AVN, the model had a sensitivity of 93.33% (95% confidence interval (CI) 77.93-99.18%), specificity of 93.28% (95% CI 87.18-97.05%), and accuracy of 93.28% (95% CI 87.99-96.73%). The area under the receiver operating characteristic curve was 0.94 (95% CI 0.88-0.99). Compared to three clinical experts, the DL model had better AUC than one clinical expert and only one expert had higher accuracy than the DL model. The results were otherwise similar between the model and clinical experts. Our DL model performed well and may be a future beneficial tool for screening of AVN of the lunate.

Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool-A Feasibility Study.

Enchondromas are common benign bone tumors, usually presenting in the hand. They can cause symptoms such as swelling and pain but often go un-noticed. If the tumor expands, it can diminish the bone cortices and predispose the bone to fracture. Diagnosis is based on clinical investigation and radiographic imaging. Despite their typical appearance on radiographs, they can primarily be misdiagnosed or go totally unrecognized in the acute trauma setting. Earlier applications of deep learning models to image classification and pattern recognition suggest that this technique may also be utilized in detecting enchondroma in hand radiographs. We trained a deep learning model with 414 enchondroma radiographs to detect enchondroma from hand radiographs. A separate test set of 131 radiographs (47% with an enchondroma) was used to assess the performance of the trained deep learning model. Enchondroma annotation by three clinical experts served as our ground truth in assessing the deep learning model's performance. Our deep learning model detected 56 enchondromas from the 62 enchondroma radiographs. The area under receiver operator curve was 0.95. The F1 score for area statistical overlapping was 69.5%. Our deep learning model may be a useful tool for radiograph screening and raising suspicion of enchondroma.

Enchondromas of the Hand: Curettage With Autogenous Bone vs. Bioactive Glass S53P4 for Void Augmentation.

Background/Aim Enchondroma is the most common primary bone tumour of the hand. When surgery is indicated, curettage with or without void augmentation has been described. However, only few comparative studies exist. The aim of this study was to compare the outcomes of hand enchondromas treated with autologous bone graft (AG) and bioactive glass S53P4 (BAG). PATIENTS AND METHODS: A retrospective comparative analysis was conducted among patients surgically treated for hand enchondromas at a tertiary referral centre during a 17-year period. RESULTS: A total of 190 patients (116 AG vs. 74 BAG) with 205 enchondromas were included. No statistically significant differences in outcome measures were observed. A reoperation was performed in five patients in the autologous bone-graft group; one patient presented a rare malignant transformation from enchondroma to chondrosarcoma after the primary operation. No reoperations were performed in the BAG group. CONCLUSION: Although AG is the gold standard for filling bony cavities, bone-graft retrieval can cause complications and postoperative pain. Our results suggest that S53P4 BAG is a safe and effective bone-graft material alternative for filling of enchondroma-evacuated cavities.

Platelet-rich plasma versus corticosteroid injection for treatment of trigger finger: study protocol for a prospective randomized triple-blind placebo-controlled trial.

BACKGROUND: Trigger finger is a common hand disorder that limits finger range of motion and causes pain and snapping of the affected finger. Trigger finger is caused by an imbalance of the tendon sheath and the flexor tendon. The initial treatment is generally a local corticosteroid injection around the first annular (A1) pulley. However, it is not unusual that surgical release of the A1 pulley is required. Moreover, adverse events after local corticosteroid injection or operative treatment may occur. Platelet-rich plasma (PRP) has been shown to be safe and to reduce symptoms in different tendon pathologies, such as DeQuervain's disease. However, the effects of PRP on trigger finger have not been studied. The aim of this single-center triple-blind randomized controlled trial is to study whether PRP is non-inferior to corticosteroid injection in treating trigger finger. The secondary outcome is to assess the safety and efficacy of PRP in comparison to placebo. METHODS: The trial is designed as a randomized, controlled, patient-, investigator-, and outcome assessor-blinded, single-center, three-armed 1:1:1 non-inferiority trial. The patients with clinical symptoms of trigger finger will be randomly assigned to treatment with PRP, corticosteroid, or normal saline injection. The primary outcome is Patient-Rated Wrist Evaluation and symptom resolution. Secondary outcomes include Quick-Disabilities of the Arm, Shoulder and Hand; pain; grip strength; finger active range of motion; and complications. Appropriate statistical methods will be applied. DISCUSSION: We present a novel RCT study design on the use of PRP for the treatment of trigger finger compared to corticosteroid and normal saline injection. The results of the trial will indicate if PRP is appropriate for the treatment of trigger finger. TRIAL REGISTRATION: ClinicalTrials.gov NCT04167098 . Registered on November 18, 2019.