[Imaging Findings of Spinal Metastases with Differential Diagnosis: Focusing on Solitary Spinal Lesion in Older Patients]. / ì „ì´ì„± 척추 ì¢ ì–‘ì˜ 영상 소견: ê³ ë ¹ 환자의 단일병소를 중심으로 한 감별 질환.

If a solitary spinal lesion is found in an older patient, bone metastasis can be primarily considered as the diagnosis. Bone metastasis can occur anywhere, but it mostly occurs in the vertebral body and may sometimes show typical imaging findings, presenting as a single lesion. Therefore, differentiating it from other lesions that mimic bone metastases can be challenging, potentially leading to delayed diagnosis and initiation of primary cancer treatment. This review provides an overview of imaging findings and clinical guidelines for bone metastases and discusses its differences from other diseases that can occur as solitary spinal lesions in older patients.

Enhancing local recurrence detection in patients with high-grade soft tissue sarcoma: value of short-term Ultrasonography added to post-operative MRI surveillance.

BACKGROUND: Limited data exist on the optimal postoperative surveillance protocol for high-grade soft tissue sarcoma, particularly regarding the optimal imaging modality and imaging interval for detecting local recurrence. This study aimed to assess the benefit of short-term postoperative ultrasonography (USG) for detecting local recurrence in patients with high-grade soft tissue sarcoma. METHODS: Patients with newly diagnosed high-grade soft tissue sarcoma who underwent surgical resection between January 2010 and June 2020 were included. Short-term USG was added to the follow-up protocol as a surveillance tool alongside routine magnetic resonance imaging (MRI). The primary outcome was the additional detection rate of short-term USG compared with routine MRI surveillance for early local recurrence detection. Subgroup analysis was performed to evaluate factors influencing USG detection rate. The additional detection rate of short-term USG for detection of metastatic lymph nodes was also evaluated. The secondary outcome was the false referral rate of short-term USG. RESULTS: In total, 198 patients (mean age ± standard deviation: 52.1 ± 15.8 years; 94 women) were included. Local recurrence occurred in 20 patients (10.1%; 20/198). Short-term USG detected local recurrence in advance of routine MRI visits in 7 out of 198 patients, resulting in an additional detection rate of 3.5% (95% CI: 1.7-7.1%). Subgroup analysis showed no significant difference in the short-term USG detection rate based on initial tumor characteristics, and receipt of radiotherapy or chemotherapy. Short-term USG additionally detected five of seven patients with metastatic lymph nodes [2.5% (95% CI, 1.1-5.8%, 5/198)]. The false referral rate of short-term USG was 3.5% (95% CI: 1.7-7.1%; 7/198). CONCLUSIONS: Short-term USG as part of postoperative surveillance for high-grade soft tissue sarcoma can enhance early detection of local tumor recurrence and metastatic lymphadenopathy. Early detection of local tumor recurrence could lead to a prompt surgical resection and aid in local disease control.

Vertebral Venous Congestion That May Mimic Vertebral Metastasis on Contrast-Enhanced Chest Computed Tomography in Chemoport Inserted Patients.

OBJECTIVE: This study aimed to determine the prevalence of vertebral venous congestion (VVC) in patients with chemoport insertion, evaluate the imaging characteristics of nodular VVC, and identify the factors associated with VVC. MATERIALS AND METHODS: This retrospective single-center study was based on follow-up contrast-enhanced chest computed tomography (CT) of 1412 adult patients who underwent chemoport insertion between January 2016 and December 2016. The prevalence of venous stenosis, reflux, and VVC were evaluated. The imaging features of nodular VVC, including specific locations within the vertebral body, were analyzed. To identify the factors associated with VVC, patients with VVC were compared with a subset of patients without VVC who had been followed up for > 3 years without developing VVC after chemoport insertion. Toward this, a multivariable logistic regression analysis was performed. RESULTS: After excluding 333 patients, 1079 were analyzed (mean age ± standard deviation, 62.3 ± 11.6 years; 540 females). The prevalence of VVC was 5.8% (63/1079), with all patients (63/63) demonstrating vertebral venous reflux and 67% (42/63) with innominate vein stenosis. The median interval between chemoport insertion and VVC was 515 days (interquartile range, 204-881 days). The prevalence of nodular VVC was 1.5% (16/1079), with a mean size of 5.9 ± 3.1 mm and attenuation of 784 ± 162 HU. Nodular VVC tended to be located subcortically. Forty-four patients with VVC underwent CT examinations with contrast injections in both arms; the VVC disappeared in 70% (31/44) when the contrast was injected in the arm contralateral to the chemoport site. Bevacizumab use was independently associated with VVC (odds ratio, 3.45; P < 0.001). CONCLUSION: The prevalence of VVC and nodular VVC was low in patients who underwent chemoport insertion. Nodular VVC was always accompanied by vertebral venous reflux and tended to be located subcortically. To avoid VVC, contrast injection in the arm contralateral to the chemoport site is preferred.

Accelerated Musculoskeletal Magnetic Resonance Imaging.

With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Detecting residual soft tissue sarcoma after unplanned excision; model-free analysis of dynamic contrast-enhanced MRI at short-term follow-up.

OBJECTIVES: To evaluate diagnostic utility of additional DCE-MRI for detecting residual soft tissue sarcomas (STS) after unplanned excision (UPE). METHODS: We retrospectively evaluated 32 patients with UPE of STS, followed by conventional MRI with DCE-MRI and wide excision (WE), between November 2019 and January 2022. Residual tumors on conventional MRI were categorized into three groups: Lesion-type-0, no abnormal enhancement, Lesion-type-1, an indeterminate lesion, and Lesion-type-2, a definite enhancing nodule. On DCE-MRI, ROIs were manually placed on enhancing areas of suspected residual tumor. The mean and 95th percentile values of AUC of time-intensity curve were calculated at 60, 90, and 120 s of Enhancement-cycle-1 and -2. Optimal DCE parameters were identified by ROC analysis. Diagnostic performance of conventional MRI and DCE-MRI was compared using McNemar's test. RESULTS: On WE, residual tumor was present in 23 (71.9%) of 32 patients. On MRI, Lesion-type-1 was found in 16/32 (50%) patients and Lesion-type-2 in 16/32 (50%). The optimal DCE parameter was the 95th percentile value of AUC at 120s of Enhancement-cycle-2. The sensitivity, specificity, and AUC were as follows: 65.2% (95% CI, 45.8-85.7%), 88.9% (CI, 68.4-100%), and 0.77 (CI, 0.62-0.92) for conventional MRI, and 100%, 55.6% (CI, 23.1-88.0%), and 0.78 (CI, 0.61-0.95) for combined conventional and DCE-MRI. CONCLUSIONS: Additional DCE-MRI aided in detecting residual STS after UPE, particularly in cases without definite soft tissue nodular enhancement. ADVANCES IN KNOWLEDGE: Close follow up may be suggested for patients showing abnormality in DCE-MRI, with more suspicion of residual tumor.

Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs.

OBJECTIVE: Conventional computer-aided diagnosis using convolutional neural networks (CNN) has limitations in detecting sensitive changes and determining accurate decision boundaries in spectral and structural diseases such as scoliosis. We devised a new method to detect and diagnose adolescent idiopathic scoliosis in chest X-rays (CXRs) employing the latent space's discriminative ability in the generative adversarial network (GAN) and a simple multi-layer perceptron (MLP) to screen adolescent idiopathic scoliosis CXRs. MATERIALS AND METHODS: Our model was trained and validated in a two-step manner. First, we trained a GAN using CXRs with various scoliosis severities and utilized the trained network as a feature extractor using the GAN inversion method. Second, we classified each vector from the latent space using a simple MLP. RESULTS: The 2-layer MLP exhibited the best classification in the ablation study. With this model, the area under the receiver operating characteristic (AUROC) curves were 0.850 in the internal and 0.847 in the external datasets. Furthermore, when the sensitivity was fixed at 0.9, the model's specificity was 0.697 in the internal and 0.646 in the external datasets. CONCLUSION: We developed a classifier for Adolescent idiopathic scoliosis (AIS) through generative representation learning. Our model shows good AUROC under screening chest radiographs in both the internal and external datasets. Our model has learned the spectral severity of AIS, enabling it to generate normal images even when trained solely on scoliosis radiographs.

Ulnar neuropathy at the elbow: associations of pre-operative DTI parameters with clinical outcomes after cubital tunnel decompression.

OBJECTIVES: To evaluate whether DTI parameters of the ulnar nerve at the elbow are associated with clinical outcomes in patients receiving cubital tunnel decompression (CTD) surgery for ulnar neuropathy. METHODS: This retrospective study included 21 patients with cubital tunnel syndrome who received CTD surgery between January 2019 and November 2020. All patients underwent pre-operative elbow MRI, including DTI. Region-of-interest analysis was performed on the ulnar nerve at three levels around the elbow: above (level 1), cubital tunnel (level 2), and below (level 3). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were calculated on three sections at each level. Clinical data on symptom improvement in respect to pain and tingling sensation after CTD were recorded. Logistic regression analysis was used to compare DTI parameters of the nerve at three levels and the entire nerve course between patients with and without symptom improvement after CTD. RESULTS: After CTD, 16 patients showed improvement in symptoms, but five did not. ROC analysis of DTI parameters showed that AUCs of FA, AD, and MD were higher at level 1 than at levels 2 and 3, with FA showing the highest AUC (level 1: FA, 0.7104 [95% CI, 0.5206-0.9002] vs AD, 0.6521 [95% CI, 0.4900-0.8142] vs MD, 0.6153 [95% CI, 0.4187-0.8119]). CONCLUSION: In patients who underwent CTD surgery for ulnar neuropathy at the elbow, the DTI parameters of FA, AD, and MD above the cubital tunnel level were associated with clinical outcomes, with FA showing the strongest associations. KEY POINTS: • After CTD surgery for ulnar neuropathy at the elbow, persistent symptoms may be observed, depending on symptom severity. • DTI parameters of the ulnar nerve at the elbow showed differences in their capacity for discriminating between patients with and without symptom improvement following CTD surgery, with this capacity depending on the nerve level at the elbow. • FA, AD, and MD measured above the cubital tunnel on pre-operative DTI may be associated with surgical outcomes, with FA showing the strongest association (AUC at level 1, 0.7104 [95% CI, 0.5206-0.9002]).

Small (≤5 cm) soft tissue tumors of the extremity and superficial trunk: MRI features and demographic characteristics associated with malignancy.

BACKGROUND: Although a substantial proportion of small soft tissue tumors are malignant, magnetic resonance imaging (MRI) features and demographic characteristics associated with these tumors have not been well described. PURPOSE: To investigate the MRI features and demographic characteristics associated with small (≤5 cm) malignant soft tissue tumors, and to identify independent predictors that allow differentiation of small benign and malignant soft tissue tumors. MATERIAL AND METHODS: This retrospective study evaluated patients who underwent surgical excision of small soft tissue tumors of the extremities and superficial trunk, and preoperative contrast-enhanced MRI. Seven MRI findings (tumor depth, tumor-fascia relationship, heterogeneity of signal intensity, necrosis, peritumoral edema, peritumoral enhancement, and margin) and two demographic parameters (age and sex) were included in univariate and multivariate logistic regression analyses to identify independent predictors of small malignant soft tissue tumors. RESULTS: A total of 221 patients (102 men; mean age=45.6 ± 17.6 years) with 72 malignant and 149 benign tumors were included. In the univariate analysis, peritumoral edema (odds ratio [OR] = 3.854; P < 0.001) and peritumoral enhancement (OR = 3.966; P < 0.001) and patient age (≥46 years) (OR = 2.154; P = 0.009) were significantly associated with malignancy. Multivariate analysis showed that peritumoral enhancement on MRI (OR = 3.728; P < 0.001) and patient age (≥46 years) (OR = 1.907; P = 0.036) were independent predictors of malignancy. The combination of these two parameters showed accuracy of 75.1%, sensitivity of 55.6%, and specificity of 84.6% to predict malignancy. CONCLUSION: Among several MRI and demographic features, the presence of peritumoral enhancement on MRI and patient age (≥46 years) were independent predictors of malignancy in small soft tissue tumors.

Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network.

Purpose: To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods: Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results: The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion: Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.

Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy.

Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93-0.94; cross-sectional area error, 2.66-2.97%; average surface distance, 0.40-0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.

Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features.

RATIONALE AND OBJECTIVES: To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT. MATERIALS AND METHODS: 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. RESULTS: 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; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts. CONCLUSION: Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.

Assessment of central cartilaginous tumor of the appendicular bone: inter-observer and intermodality agreement and comparison of diagnostic performance of CT and MRI.

BACKGROUND: Diagnostic performance, inter-observer agreement, and intermodality agreement between computed tomography (CT) and magnetic resonance imaging (MRI) in the depiction of the major distinguishing imaging features of central cartilaginous tumors have not been investigated. PURPOSE: To determine the inter-observer and intermodality agreement of CT and MRI in the evaluation of central cartilaginous tumors of the appendicular bones, and to compare their diagnostic performance. MATERIAL AND METHODS: Two independent radiologists retrospectively reviewed preoperative CT and MRI. Inter-observer and intermodality agreement between CT and MRI in the assessment of distinguishing imaging features, including lesion size, deep endosteal scalloping, cortical expansion, cortical disruption, pathologic fracture, soft tissue extension, and peritumoral edema, were evaluated. The agreement with histopathology and the accuracy of the radiologic diagnoses made with CT and MRI were also analyzed. RESULTS: A total of 72 patients were included. CT and MRI showed high inter-observer and intermodality agreements with regard to size, deep endosteal scalloping, cortical expansion, cortical disruption, and soft tissue extension (ICC = 0.96-0.99, k = 0.60-0.90). However, for the evaluation of pathologic fracture, MRI showed only moderate inter-observer agreement (k = 0.47). Peritumoral edema showed only fair intermodality agreement (k = 0.28-0.33) and moderate inter-observer agreement (k = 0.46) on CT. Both CT and MRI showed excellent diagnostic performance, with high agreement with the histopathology (k = 0.89 and 0.87, respectively) and high accuracy (91.7% for both CT and MRI). CONCLUSION: CT and MRI showed high inter-observer and intermodality agreement in the assessment of several distinguishing imaging features of central cartilaginous tumors of the appendicular bones and demonstrated comparable diagnostic performance.

Diagnostic performance of computed tomography and diffusion-weighted imaging as first-line imaging modality according to the International Myeloma Working Group (IMWG) imaging algorithm for monoclonal plasma cell disorders.

BACKGROUND: The latest International Myeloma Working Group (IMWG) guideline recommends low-dose whole-body (WB) computed tomography (CT) as the first-line imaging technique for the initial diagnosis of plasma cell disorders. PURPOSE: To evaluate diagnostic performances of CT and diffusion-weighted imaging (DWI) as the first-line imaging modalities and assess misclassification rates obtained following the guideline. MATERIAL AND METHODS: Two independent radiologists analyzed CT (acquired as PET/CT) and DWI (3-T; b-values = 50 and 900 s/mm2) of patients newly diagnosed with plasma cell disorder, categorizing the number of bone lesions. Diagnostic performance of CT and DWI was compared using the McNemar test, and misclassification rates were calculated with a consensus WB-MRI reading as the reference standard. Differences in lesion number categories were assessed using marginal homogeneity and kappa statistics. RESULTS: Of 56 patients (36 men; mean age = 63.5 years), 39 had myeloma lesions. DWI showed slightly higher sensitivity for detecting myeloma lesions (97.4%) than CT (84.6%-92.3%; P > 0.05). CT showed significantly higher specificity (88.2%) than DWI (52.9%-58.8%; P<0.05). CT had a higher additional study requirement rate than DWI (7.7%-15.4% vs. 2.6%), but a lower unnecessary treatment rate (11.8% vs. 41.2%-47.1%). Both readers showed significant differences in categorization of the number of lesions on CT compared with the reference standard (P < 0.001), and one reader showed a significant difference on DWI (P = 0.006 and 0.098). CONCLUSION: CT interpreted according to the IMWG guideline is a diagnostically effective first-line modality with relatively high sensitivity and specificity. DWI alone may not be an acceptable first-line imaging modality because of low specificity.

Diagnostic accuracy and complication rate of image-guided percutaneous transthoracic needle lung biopsy for subsolid pulmonary nodules: a systematic review and meta-analysis.

OBJECTIVES: To determine the diagnostic accuracy and complication rate of percutaneous transthoracic needle biopsy (PTNB) for subsolid pulmonary nodules and sources of heterogeneity among reported results. METHODS: We searched PubMed, EMBASE, and Cochrane libraries (until November 7, 2020) for studies measuring the diagnostic accuracy of PTNB for subsolid pulmonary nodules. Pooled sensitivity and specificity of PTNB were calculated using a bivariate random-effects model. Bivariate meta-regression analyses were performed to identify sources of heterogeneity. Pooled overall and major complication rates were calculated. RESULTS: We included 744 biopsies from 685 patients (12 studies). The pooled sensitivity and specificity of PTNB for subsolid nodules were 90% (95% confidence interval [CI]: 85-94%) and 99% (95% CI: 92-100%), respectively. Mean age above 65 years was the only covariate significantly associated with higher sensitivity (93% vs 85%, p = 0.04). Core needle biopsy showed marginally higher sensitivity than fine-needle aspiration (93% vs 83%, p = 0.07). Pooled overall and major complication rate of PTNB were 43% (95% CI: 25-62%) and 0.1% (95% CI: 0-0.4%), respectively. Major complication rate was not different between fine-needle aspiration and core needle biopsy groups (p = 0.25). CONCLUSION: PTNB had acceptable performance and a low major complication rate in diagnosing subsolid pulmonary nodules. The only significant source of heterogeneity in reported sensitivities was a mean age above 65 years. ADVANCES IN KNOWLEDGE: This is the first meta-analysis attempting to systemically determine the cause of heterogeneity in the diagnostic accuracy and complication rate of PTNB for subsolid pulmonary nodules.

Differences between 3D isovoxel fat suppression VIBE MRI and CT models of proximal femur osseous anatomy: A preliminary study for bone tumor resection planning.

PURPOSE: To evaluate the osseous anatomy of the proximal femur extracted from a 3D-MRI volumetric interpolated breath-hold (VIBE) sequence using either a Dixon or water excitation (WE) fat suppression method, and to measure the overall difference using CT as a reference standard. MATERIAL AND METHODS: This retrospective study reviewed imaging of adult patients with hip pain who underwent 3D hip MRI and CT. A semi-automatically segmented CT model served as the reference standard, and MRI segmentation was performed manually for each unilateral hip joint. The differences between Dixon-VIBE-3D-MRI vs. CT, and WE-VIBE-3D-MRI vs. CT, were measured. Equivalence tests between Dixon-VIBE and WE-VIBE models were performed with a threshold of 0.1 mm. Bland-Altman plots and Lin's concordance-correlation coefficient were used to analyze the agreement between WE and Dixon sequences. Subgroup analyses were performed for the femoral head/neck, intertrochanteric, and femoral shaft areas. RESULTS: The mean and maximum differences between Dixon-VIBE-3D-MRI vs. CT were 0.2917 and 3.4908 mm, respectively, whereas for WE-VIBE-3D-MRI vs. CT they were 0.3162 and 3.1599 mm. The mean differences of the WE and Dixon methods were equivalent (P = 0.0292). However, the maximum difference was not equivalent between the two methods and it was higher in WE method. Lin's concordance-correlation coefficient showed poor agreement between Dixon and WE methods. The mean differences between the CT and 3D-MRI models were significantly higher in the femoral shaft area (P = 0.0004 for WE and P = 0.0015 for Dixon) than in the other areas. The maximum difference was greatest in the intertrochanteric area for both techniques. CONCLUSION: The difference between 3D-MR and CT models were acceptable with a maximal difference below 3.5mm. WE and Dixon fat suppression methods were equivalent. The mean difference was highest at the femoral shaft area, which was off-center from the magnetization field.

Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT.

OBJECTIVES: To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. METHODS: 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. RESULTS: 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. CONCLUSIONS: The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. KEY POINTS: • 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.

Diagnostic performance of plain radiography for sacroiliitis in patients with suspected axial spondyloarthritis: a systematic review and meta-analysis.

BACKGROUND: Plain radiography serves a pivotal role in diagnosing axial spondyloarthritis. However, a broad range of diagnostic performance of plain radiography has been reported. PURPOSE: To perform a systematic review and meta-analysis to measure the diagnostic performance of plain radiography for sacroiliitis in patients suspected of having axial spondyloarthritis using magnetic resonance imaging (MRI) findings as the reference standard. MATERIAL AND METHODS: Studies comparing radiography and MRI in the diagnosis of sacroiliitis in patients suspected of having axial spondyloarthritis were searched in PubMed and EMBASE. Additionally, studies analyzed SPondyloaArthritis Caught Early (SPACE), DEvenir des Spondylarthropathies Indifferenciées Récentes (DESIR), GErman Spondyloarthritis Inception Cohort (GESPIC), and South Swedish Arthritis Treatment Group (SSATG) cohorts were manually searched. Pooled sensitivity and specificity of radiography were calculated by using a bivariate random-effects model. Meta-regression analyses were performed to identify the sources of heterogeneity. RESULTS: Eight eligible studies with 1579 patients were included. The pooled sensitivity and specificity of radiography were 0.55 (95% confidence interval [CI] = 0.40-0.69) and 0.87 (95% CI = 0.72-0.95). The meta-regression analyses showed prospective study design and criteria for MRI positivity considering only active bone marrow edema were associated with lower sensitivity. CONCLUSION: The plain radiography showed low sensitivity and reasonable specificity in diagnosis of sacroiliitis in patients suspected of having axial spondyloarthritis.

An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis.

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.

Role of whole-body MRI for treatment response assessment in multiple myeloma: comparison between clinical response and imaging response.

BACKGROUND: Whole-body MRI (WB-MRI) including diffusion-weighted image (DWI) have been widely used in patients with multiple myeloma. However, evidence for the value of WB-MRI in the evaluation of treatment response remains sparse. Therefore, we evaluated the role of WB-MRI in the response assessment. METHODS: In our WB-MRI registry, we searched multiple myeloma patients treated with chemotherapy who underwent both baseline and follow-up WB-MRI scans. Clinical responses were categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD), using IMWG criteria. Using RECIST 1.1, MD Anderson (MDA) criteria, and MDA-DWI criteria, imaging responses on WB-MRI were rated as CR, PR, SD, or PD by two radiologists independently. Then, discrepancy cases were resolved by consensus. Weighted Kappa analysis was performed to evaluate agreement between the imaging and clinical responses. The diagnostic accuracy of image responses in the evaluation of clinical CR, objective response (CR and PR), and PD was calculated. RESULTS: Forty-two eligible patients were included. There was moderate agreement between imaging and clinical responses (κ = 0.54 for RECIST 1.1, κ = 0.58 for MDA criteria, κ = 0.69 for MDA-DWI criteria). WB-MRI showed excellent diagnostic accuracy in assessment of clinical PD (sensitivity 88.9%, specificity 94.7%, positive predictive value [PPV] 84.2%, negative predictive value [NPV] 96.4% in all three imaging criteria). By contrast, WB-MRI showed low accuracy in assessment of clinical CR (sensitivity 4.5%, specificity 98.1%, PPV 50.0%, NPV 71.2% in all three imaging criteria). As to the clinical objective response, the diagnostic accuracy was higher in MDA-DWI criteria than RECIST 1.1 and MDA criteria (sensitivity/specificity/PPV/NPV, 84.2%/94.4%/98.0%/65.4, 54.4%/100%/100%/40.9, and 61.4%/94.4%/97.2%/43.6%, respectively). CONCLUSIONS: In the imaging response assessment of multiple myeloma, WB-MRI showed excellent performance in the evaluation of PD, but not in the assessment of CR or objective response. When adding DWI to imaging response criteria, diagnostic accuracy for objective response was improved and agreement between imaging and clinical responses was increased.