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Background: Recurrence is one of the most common complications following hallux valgus surgery. Moreover, hallux varus occurs in cases of overcorrection. We aimed to quantitatively measure, using radiographic examination, the dynamics of the soft tissues that act on deformities (recurrence of valgus and occurrence of varus) after the surgery. Methods: This retrospective single-institution study included 60 patients (98 feet) who underwent hallux valgus surgery between 2009 and 2018. According to radiographic findings of the foot under weightbearing conditions at postoperative month 1, we examined the tendons’ pathway and calculated the forces on the first metatarsophalangeal joint, which we termed the deformity force angle (DFA). We compared whether there was a significant difference in DFAs between the groups in which deformities occurred and those in which deformities did not occur after correction. In addition, the DFA was compared to known radiographic measurements of hallux valgus recurrence (hallux valgus angle, distal metatarsal articular angle, intermetatarsal angle, and sesamoid position) to assess association with recurrence. Results: We observed a significant difference in the DFA between patients with and without hallux valgus recurrence ( P < .001) and between those with and without hallux varus ( P < .001) based on standing radiographs taken at a minimum of 6 months postoperation. For predicting the deformities, the areas under the curve were 0.863 (hallux valgus recurrence) and 0.831 (hallux varus occurrence), respectively, which was greater than other factors evaluated. The DFA values greater than 9.5 degrees and less than 5.5 degrees were associated with the recurrence of valgus and occurrence of varus, respectively. Conclusion: In our study, DFA was associated with hallux valgus recurrence when it exceeded 9.5 degrees and hallux varus when it was less than 5.5 degrees. Moreover, in the hallux valgus surgery we performed, a DFA from 5.5 to 9.5 degrees appeared to be a “safe zone” for preventing early deformity after surgery. Level of Evidence: Level III, prognostic.

BACKGROUND In this meta-analysis, we compared the risk of obtaining nondiagnostic results and the diagnostic accuracy for detection of salivary gland malignancy between core needle biopsy (CNB) and fine-needle aspiration (FNA). METHODS All published English-language studies comparing CNB and FNA diagnostic accuracy for salivary gland masses through December 2019 were searched. Pooled risk ratios (RRs) of nondiagnostic results, sensitivities, and specificities of CNB and FNA for salivary gland malignancy diagnosis were determined. Complication rates were compared. RESULTS Six studies (1924 procedures) were quantitatively analyzed. CNB yielded significantly fewer nondiagnostic results (P

OBJECTIVE. The purpose of this study is to evaluate the diagnostic outcome of ultrasound (US)–guided core needle biopsy (CNB) of soft-tissue tumors and to assess the factors associated with signifi...

To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT.This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort.A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p0.01) and the test (AUC, 0.95; p=0.01) cohorts.Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.

We compared appendiceal visualization on 2-mSv CT vs. conventional-dose CT (median 7 mSv) in adolescents and young adults and analyzed the undesirable clinical and diagnostic outcomes that followed appendiceal nonvisualization.A total of 3074 patients aged 15-44 years (mean ± standard deviation, 28 ± 9 years; 1672 female) from 20 hospitals were randomized to the 2-mSv CT or conventional-dose CT group (1535 vs. 1539) from December 2013 through August 2016. A total of 161 radiologists from 20 institutions prospectively rated appendiceal visualization (grade 0, not identified; grade 1, unsure or partly visualized; and grade 2, clearly and entirely visualized) and the presence of appendicitis in these patients. The final diagnosis was based on CT imaging and surgical, pathologic, and clinical findings. We analyzed undesirable clinical or diagnostic outcomes, such as negative appendectomy, perforated appendicitis, more extensive than simple appendectomy, delay in patient management, or incorrect CT diagnosis, which followed appendiceal nonvisualization (defined as grade 0 or 1) and compared the outcomes between the two groups.In the 2-mSv CT and conventional-dose CT groups, appendiceal visualization was rated as grade 0 in 41 (2.7%) and 18 (1.2%) patients, respectively; grade 1 in 181 (11.8%) and 81 (5.3%) patients, respectively; and grade 2 in 1304 (85.0%) and 1421 (92.3%) patients, respectively (The use of 2-mSv CT instead of conventional-dose CT impairs appendiceal visualization in more patients. However, appendiceal nonvisualization on 2-mSv CT rarely leads to undesirable clinical or diagnostic outcomes.

Oxalosis is a metabolic disorder characterized by the accumulation of calcium oxalate deposits in various organ systems. Bone oxalosis is a systemic manifestation of oxalosis, in which calcium oxalate deposits accumulate in the skeletal system. This report describes a 69-year-old female patient who presented with right hip pain and was later found to have an osseous mass-like lesion in the right proximal femur, with radiographic findings resembling those of a benign bone tumor. CT-guided biopsy and surgical biopsy showed that the mass had pathologic findings typical of bone oxalosis. Because the patient had no predisposing factors for systemic oxalosis and had no hyperoxalemia or hyperoxaluria, she could be diagnosed with localized bone oxalate deposition disease or bone oxaloma rather than bone oxalosis. This is the first report of this clinical entity to present as a benign-appearing bone mass and without any predisposing systemic causes.

OBJECTIVE. The objective of our study was to compare the sensitivity of a deep learning (DL) algorithm with the assessments by radiologists in diagnosing osteonecrosis of the femoral head (ONFH) using digital radiography. MATERIALS AND METHODS. We performed a two-center, retrospective, noninferiority study of consecutive patients (≥ 16 years old) with a diagnosis of ONFH based on MR images. We investigated the following four datasets of unilaterally cropped hip anteroposterior radiographs: training (n = 1346), internal validation (n = 148), temporal external test (n = 148), and geographic external test (n = 250). Diagnostic performance was measured for a DL algorithm, a less experienced radiologist, and an experienced radiologist. Noninferiority analyses for sensitivity were performed for the DL algorithm and both radiologists. Subgroup analysis for precollapse and postcollapse ONFH was done. RESULTS. Overall, 1892 hips (1037 diseased and 855 normal) were included. Sensitivity and specificity for the temporal external test set were 84.8% and 91.3% for the DL algorithm, 77.6% and 100.0% for the less experienced radiologist, and 82.4% and 100.0% for the experienced radiologist. Sensitivity and specificity for the geographic external test set were 75.2% and 97.2% for the DL algorithm, 77.6% and 75.0% for the less experienced radiologist, and 78.0% and 86.1% for the experienced radiologist. The sensitivity of the DL algorithm was noninferior to that of the assessments by both radiologists. The DL algorithm was more sensitive for precollapse ONFH than the assessment by the less experienced radiologist in the temporal external test set (75.9% vs 57.4%; 95% CI of the difference, 4.5-32.8%). CONCLUSION. The sensitivity of the DL algorithm for diagnosing ONFH using digital radiography was noninferior to that of both less experienced and experienced radiologist assessments.

To develop and validate a scoring system integrating MRI and laboratory findings to differentiate necrotizing fasciitis (NF) from non-necrotizing fasciitis (non-NF). This retrospective study included 144 subjects who underwent surgery in one of three tertiary referral centers for NF or cellulitis with non-NF. The development cohort consisted of 96 subjects (NF = 47; non-NF = 49) from one center, and the validation cohort consisted of 48 subjects (NF = 23; cellulitis with non-NF = 25) from two different centers. The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) scoring system and five MRI findings (thickening of the intermuscular deep fascia ≥ 3 mm, extensive involvement of the deep fascia, multi-compartmental involvement in one extremity, presence of gas, and contrast-enhancement pattern) were included in univariate and multivariate logistic regression analysis to identify independent predictors of NF. An additive scoring system was developed using the coefficients of the final regression model. Model performance was assessed for discrimination and calibration. The scoring system was externally validated. The final scoring system consisted of three variables: thickening of the deep fascia ≥ 3 mm, multi-compartmental involvement, and LRINEC score. The new predictive model showed improved performance (area under the receiver operating characteristic curve [AUC], 0.862; positive and negative predictive values, 82% and 79%, respectively), compared with the LRINEC score alone (0.814, 77% and 67%, respectively). The model also showed good discrimination with the external validation dataset (AUC, 0.933). Differentiation of NF from severe cellulitis with non-NF can be achieved with the new predictive scoring system. • The new predictive scoring system integrating two MRI findings with the LRINEC score can help in the differentiation of necrotizing fasciitis from severe cellulitis with non-necrotizing fasciitis. • Thickening of the deep fascia ≥ 3 mm and multi-compartmental involvement were the most important MRI findings for the differentiation.

To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. One hundred sixty-five patients with vertebral compression fractures were allocated to training (n = 110 [62 acute benign and 48 malignant fractures]) and validation (n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features (n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set. The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort. The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. • A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. • The model showed good calibration and discrimination in both training and validation cohorts. • The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.

Purpose To build and validate a decision tree model using classification and regression tree (CART) analysis to distinguish lipoma and lipoma variants from well-differentiated liposarcoma of the extremities and superficial trunk. Methods This retrospective study included patients who underwent surgical resection and preoperative contrast-enhanced MR imaging for lipoma, lipoma variants, and well-differentiated liposarcoma in two tertiary referral centers. Six MRI findings (tumor size, anatomical location, tumor depth, shape, enhancement pattern, and presence of intermingled muscle fibers) and two demographic factors (patient age and sex) were assessed to build a classification tree using CART analysis with minimal error cross-validation pruning based on a complexity parameter. Results The model building cohort consisted of 231 patients (186 lipoma and lipoma variants and 45 well-differentiated liposarcoma) from one center, while the validation cohort consisted of 157 patients (136 lipoma and lipoma variants and 21 well-differentiated liposarcoma) from another center. In the CART analysis, the contrast enhancement pattern (no enhancement or thin septal enhancement versus thick septal, nodular, confluent hazy, or solid enhancement) was the first partitioning predictor, followed by a maximal tumor size of 12.75 cm. The tree model allowed distinction of lipoma and lipoma variants from well-differentiated liposarcoma in both the model building cohort (C-statistics, 0.955; sensitivity 80 %, specificity 94.62 %, accuracy 91.77 %) and the external validation cohort (C-statistics, 0.917; sensitivity 66.67 %, specificity 95.59 %, accuracy 91.72 %). Conclusion The distinction of lipoma and lipoma variants from well-differentiated liposarcoma can be achieved with the simple classification tree model.

Background The radiographic diagnosis of osteonecrosis of the femoral head (ONFH) is challenging for radiologists. Purpose To measure the sensitivity and specificity of digital radiography for diagnosing ONFH and to evaluate the diagnostic value of the frog-leg view. Material and Methods Patients diagnosed with ONFH by magnetic resonance imaging (MRI) (n = 132) and normal controls (n = 69) were included. Two radiologists independently graded the likelihood of ONFH and subchondral fracture on radiographs. First, the anteroposterior (AP) and frog-leg radiographs were assessed as a whole. Subsequently, randomly ordered unilaterally cropped AP radiographs were analyzed. Sensitivity and specificity were measured using MRI as reference standard. A subgroup analysis for pre-collapse ONFH was performed. The effect of within-subject correlation was analyzed using a generalized-estimating-equation approach. Inter-reader agreement was assessed with κ statistics. McNemar’s test was used to determine the additional diagnostic value of the frog-leg view. Results Among the 402 hips from 201 individuals, 191 were classified as normal, 73 as pre-collapse ONFH, and 138 as post-collapse ONFH. The sensitivity and specificity ranges for ONFH were 76–89% and 77–97%, respectively. For pre-collapse ONFH, the sensitivity and specificity were 52–71% and 78–97%, respectively. The κ ranges for the likelihood of ONFH and pre-collapse ONFH were 0.59–0.66 and 0.35–0.59, respectively. The frog-leg view did not provide additional value for diagnosing ONFH. Conclusion Diagnostic performance of digital radiography for ONFH revealed low sensitivity and poor-to-moderate inter-reader agreement for diagnosing pre-collapse ONFH. There was no additional value of the frog-leg view.

Spontaneous intracranial hypotension (SIH), caused by CSF leaks, can trigger headaches, and is treated using epidural blood patch (EBP) procedures. We aimed to determine the effectiveness of a newly devised two-site blind EBP procedure as the initial treatment for SIH and compare its effectiveness with that of conventional targeted EBP. We retrospectively reviewed data for 116 patients who underwent EBP for SIH between November 2013 and April 2017. Patients were divided into two groups: those who initially received two-site blind EBP (n = 28) at the cervicothoracic (C7/T1) and thoracolumbar junctions (T12/L1) for sealing CSF leaks at all levels, and those who received targeted EBP after CT myelography (n = 88). The initial recovery status and the need for additional blood patches were evaluated. In total, 71.4% and 69.3% patients in the two-site blind EBP and targeted EBP groups, respectively, experienced complete relief after the first patch (p > .05); collectively, 89.3% and 96.6% patients, respectively, experienced complete or partial relief after the first patch (p > .05). A second EBP was required by 35.7% and 29.5% patients in the two-site blind EBP and targeted EBP groups, respectively, with no significant between-group difference (p > .05). All patients ultimately experienced relief; a few required up to four patches. We observed similar outcomes after initial two-site blind EBP and targeted EBP. Therefore, two-site blind EBP is an effective alternative to conventional targeted EBP and a potential initial patch of choice for SIH treatment.

We aimed to analyze the pattern of teres minor atrophy with regard to its two-bundle anatomy and to assess its association with clinical factors. Shoulder MRIs performed between January and December 2016 were retrospectively reviewed. Images were evaluated for the presence and pattern of isolated teres minor atrophy. Isolated teres minor atrophy was categorized into complete or partial pattern, and partial pattern was further classified according to the portion of the muscle that was predominantly affected. The medical records were reviewed to identify clinical factors associated with teres minor atrophy. Seventy-eight shoulders out of 1,264 (6.2%) showed isolated teres minor atrophy; complete pattern in 41.0%, and partial pattern in 59.0%. Most cases of partial pattern had predominant involvement of the medial–dorsal component (82.6%). There was no significant association between teres minor atrophy and previous trauma, shoulder instability, osteoarthritis, and previous operation. The history of shoulder instability was more frequently found in patients with isolated teres minor atrophy (6.4%), compared with the control group (2.6%), although the difference was not statistically significant. Isolated teres minor atrophy may be either complete or partial, and the partial pattern may involve either the medial–dorsal or the lateral–ventral component of the muscle. The imaging findings of partial pattern teres minor atrophy indicate that the two muscle components may have separate innervation.