Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.

Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.

PURPOSE: To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS: A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS: Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION: Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas.

BACKGROUND: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. METHODS: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. RESULTS: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60-70% accuracy, 55-80% sensitivity, and 63-77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. CONCLUSION: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.

BACKGROUND: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Determining a Threshold of Medial Meniscal Extrusion for Prediction of Knee Pain and Cartilage Damage Progression Over 4 Years: Data From the Osteoarthritis Initiative.

BACKGROUND. The extent of medial meniscal extrusion (MME) that is associated with structural and symptomatic progression of knee osteoarthritis has not been defined yet. OBJECTIVE. The purpose of our study was to investigate MRI-based thresholds of MME that are associated with structural progression of knee degenerative disease and symptoms over a period of 4 years. METHODS. We studied 328 knees of 235 participants that were randomly selected from the Osteoarthritis Initiative cohort. MME was quantified on coronal sections of intermediate-weighted MRI sequences obtained at 3 T. Knee pain and cartilage abnormalities were measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scale and the cartilage whole-organ MRI score (WORMS). General estimating equations with logistic regression models were used to correlate baseline MME and changes in pain (WOMAC) and cartilage damage (WORMS). ROC analyses were performed to determine the area under the ROC curve (AUROC). Individual thresholds were determined by maximizing the product of sensitivity and specificity. RESULTS. The AUROC for predicting progression of knee pain, medial compartment cartilage damage, and medial tibial cartilage damage were 0.71, 0.70, and 0.72, respectively, and the individual thresholds for MME were 2.5, 2.7, and 2.8 mm. A single threshold of 2.5 mm was determined by maximizing the mean of the product of sensitivity and specificity of the three outcome variables (knee pain progression, medial compartmental cartilage damage progression, and medial tibial cartilage damage progression). CONCLUSION. MME was associated with knee pain and cartilage damage progression over 4 years. A single threshold of 2.5 mm was found to be the most useful threshold for predicting knee pain, medial compartment cartilage damage progression, and tibial cartilage damage progression over 4 years. CLINICAL IMPACT. This threshold could be used to standardize the diagnostic criterion of extrusion and to better characterize the risk for subsequent structural and symptomatic progression of knee osteoarthritis.

Meniscal Root Tears and Extrusion Are Significantly Associated with the Development of Accelerated Knee Osteoarthritis: Data from the Osteoarthritis Initiative.

OBJECTIVE: To identify joint structural risk factors, measured using quantitative compositional and semiquantitative magnetic resonance imaging (MRI) scoring, associated with the development of accelerated knee osteoarthritis (AKOA) compared with a more normal rate of knee osteoarthritis (OA) development. DESIGN: From the Osteoarthritis Initiative we selected knees with no radiographic OA (Kellgren-Lawrence grade [KL] 0/1) that developed advanced-stage OA (KL 3/4; AKOA) within a 4-year timeframe and a comparison group with a more normal rate of OA development (KL 0/1 to KL 2 in 4 years). MRIs at the beginning of the 4-year timeframe were assessed for cartilage T2 values and structural abnormalities using a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Associations of MRI findings with AKOA versus normal OA were assessed using multivariable logistic regression models. RESULTS: A total of 106 AKOA and 168 subjects with normal OA development were included. Mean cartilage T2 values were not significantly associated with AKOA (odds ratio [OR] 1.06; 95% confidence interval [CI] 0.82-1.36). Risk factors for AKOA development included higher meniscus maximum scores (OR 1.37; 95% CI 1.11-1.68), presence of meniscal extrusion (OR 6.30; 95% CI 2.57-15.49), presence of root tears (OR 4.64; 95% CI 1.61-13.34), and higher medial tibia cartilage lesion scores (OR 1.96; 95% CI 1.19-3.24). CONCLUSIONS: We identified meniscal damage, especially meniscal extrusion and meniscal root tears as risk factors for AKOA development. These findings contribute to identifying subjects at risk of AKOA at an early stage when preventative measures targeting modifiable risk factors such as meniscal repair surgery could still be effective.

Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.

Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis still relies on expert radiologists' readings. The potential of such a model in clinical routine should be investigated. Purpose To develop a multitask deep learning model for grading radiographic hip osteoarthritis features on radiographs and compare its performance to that of attending-level radiologists. Materials and Methods This retrospective study analyzed hip joints seen on weight-bearing anterior-posterior pelvic radiographs from participants in the Osteoarthritis Initiative (OAI). Participants were recruited from February 2004 to May 2006 for baseline measurements, and follow-up was performed 48 months later. Femoral osteophytes (FOs), acetabular osteophytes (AOs), and joint-space narrowing (JSN) were graded as absent, mild, moderate, or severe according to the Osteoarthritis Research Society International atlas. Subchondral sclerosis and subchondral cysts were graded as present or absent. The participants were split at 80% (n = 3494), 10% (n = 437), and 10% (n = 437) by using split-sample validation into training, validation, and testing sets, respectively. The multitask neural network was based on DenseNet-161, a shared convolutional features extractor trained with multitask loss function. Model performance was evaluated in the internal test set from the OAI and in an external test set by using temporal and geographic validation consisting of routine clinical radiographs. Results A total of 4368 participants (mean age, 61.0 years ± 9.2 [standard deviation]; 2538 women) were evaluated (15 364 hip joints on 7738 weight-bearing anterior-posterior pelvic radiographs). The accuracy of the model for assessing these five features was 86.7% (1333 of 1538) for FOs, 69.9% (1075 of 1538) for AOs, 81.7% (1257 of 1538) for JSN, 95.8% (1473 of 1538) for subchondral sclerosis, and 97.6% (1501 of 1538) for subchondral cysts in the internal test set, and 82.7% (86 of 104) for FOS, 65.4% (68 of 104) for AOs, 80.8% (84 of 104) for JSN, 88.5% (92 of 104) for subchondral sclerosis, and 91.3% (95 of 104) for subchondral cysts in the external test set. Conclusion A multitask deep learning model is a feasible approach to reliably assess radiographic features of hip osteoarthritis. © RSNA, 2020 Online supplemental material is available for this article.

Diagnostic Performance of CT-Guided Bone Biopsies in Patients with Suspected Osteomyelitis of the Appendicular and Axial Skeleton with a Focus on Clinical and Technical Factors Associated with Positive Microbiology Culture Results.

PURPOSE: To assess diagnostic performance of CT-guided percutaneous needle bone biopsy (CTNBB) in patients with suspected osteomyelitis and analyze whether certain clinical or technical factors were associated with positive microbiology results. MATERIALS AND METHODS: All CTNBBs performed in a single center for suspected osteomyelitis of the appendicular and axial skeleton during 2003-2018 were retrospectively reviewed. Specific inclusion criteria were clinical and radiologic suspicion of osteomyelitis. Standard of reference was defined using outcome of surgical histopathology and microbiology culture and clinical and imaging follow-up. Technical and clinical data (needle size, comorbidities, clinical factors, laboratory values, blood cultures) were collected. Logistic regression was performed to assess associations between technical and clinical data and microbiology biopsy outcome. RESULTS: A total of 142 CTNBBs were included (46.5% female patients; age ± SD 46.10 y ± 22.8), 72 (50.7%) from the appendicular skeleton and 70 (49.3%) from the axial skeleton. CTNBB showed a sensitivity of 42.5% (95% confidence interval [CI], 32.0%-53.6%) in isolating the causative pathogen. A higher rate of positive microbiology results was found in patients with intravenous drug use (odds ratio [OR] = 5.15; 95% CI, 1.2-21.0; P = .022) and elevated white blood cell count ≥ 10 × 109/L (OR = 3.9; 95% CI, 1.62-9.53; P = .002). Fever (≥ 38°C) was another clinical factor associated with positive microbiology results (OR = 3.6; 95% CI, 1.3-9.6; P = .011). CONCLUSIONS: CTNBB had a low sensitivity of 42.5% for isolating the causative pathogen. Rate of positive microbiology samples was significantly higher in patients with IV drug use, elevated white blood cell count, and fever.

Postoperative MRI Findings and Associated Pain Changes After Arthroscopic Surgery for Femoroacetabular Impingement.

OBJECTIVE. The purpose of this study is to describe postoperative MRI findings after femoroacetabular impingement surgery in correlation with pain changes and surgical findings. SUBJECTS AND METHODS. We prospectively enrolled 42 patients (43 hips) who were scheduled for FAI surgery. Pre- and postoperative MR images were obtained using a 3-T MRI system. Changes in pain scores were assessed using the hip dysfunction and osteoarthritis outcome score. MR images were evaluated for the presence of acetabuloplasty or femoroplasty, presence of chondral and labral repair surgery, bone marrow edema, subchondral cysts, chondral defects, labral tears, capsular defects, and effusion. The optimal orientation to detect these changes was noted. Imaging findings were compared with pain score changes using linear regression analysis. Sensitivity and specificity were assessed using surgical correlation as the reference standard. RESULTS. Increased acetabular bony débridement length was associated with decreased improvement in pain scores (coefficient, -2.07; 95% CI, -3.53 to -0.62; p = 0.008), whereas other imaging findings were not significantly different. Femoroplasty and capsular alterations were best detected on oblique axial sequences; acetabuloplasty and cartilage and labral repair were best seen on sagittal sequences. MRI showed excellent sensitivity (100%) and specificity (100%) for detecting labral repair and excellent sensitivity for detecting femoroplasty (98%). Sensitivity and specificity were lower for detecting acetabuloplasty (83% and 80%, respectively) and chondral repair (75% and 54%, respectively). CONCLUSION. Arthroscopic acetabuloplasty showed a greater association with postoperative pain than did other aspects of surgical correction for femoroacetabular impingement. Femoroplasty and labral repair were reliably diagnosed on 3-T MRI; however, limitations were found in the evaluation of acetabular chondral repair.

Cartilage degeneration post-meniscectomy performed for degenerative disease versus trauma: data from the Osteoarthritis Initiative.

OBJECTIVE: To compare the extent of cartilage deterioration in knees with prior meniscal resection related to trauma versus knees with resection related to degenerative disease, and to compare cartilage deterioration in knees with meniscal surgery to knees without meniscal surgery, controlling for prior knee trauma. MATERIALS AND METHODS: In this cross-sectional study, we assessed cartilage deterioration in right knees of Osteoarthritis Initiative participants: (i) with meniscal surgery due to injury (n = 79); (ii) matched control knees with a prior injury but without meniscal surgery (n = 79); (iii) with meniscal surgery but without preceding injury (n = 36); and (iv) matched control knees without meniscal surgery or prior knee injury (n = 36). Cartilage composition was measured using T2 measurements derived using semi-automatic cartilage segmentation of the right. Linear regression analysis was used to compare compartmental values of T2 between groups. RESULTS: Comparing the mean T2 values in surgical cases with and without injury our results did not show significant differences (group i vs. iii, p > 0.05). However, knees with previous meniscal surgery showed significantly (p < 0.001) higher mean T2 values across all compartments (i.e., global T2) when compared to those without meniscal surgery for both knees with a history of trauma (group i vs. ii) and knees without prior trauma (group iii vs. iv). Similar results were obtained when analyzing the compartments separately. CONCLUSIONS: Cartilage deterioration, assessed by T2, is similar in knees undergoing meniscal surgery after trauma and for degenerative conditions. Both groups demonstrated greater cartilage deterioration than nonsurgical knees, controlling for prior knee injury.

Cortical bone vessel identification and quantification on contrast-enhanced MR images.

BACKGROUND: Cortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores. METHODS: For this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm3) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth. RESULTS: In the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm3, and mean average vessel volume was 0.032±0.012 mm3/vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm. CONCLUSIONS: We have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion.

Longitudinal MRI structural findings observed in accelerated knee osteoarthritis: data from the Osteoarthritis Initiative.

OBJECTIVE: To analyze structural, longitudinal MRI findings during the development of accelerated knee osteoarthritis (AKOA) over 4 years. MATERIALS AND METHODS: From the Osteoarthritis Initiative (OAI), knees with no radiographic osteoarthritis (KL 0/1) developing advanced-stage osteoarthritis (KL 3/4; AKOA) within a 4-year (y) timeframe were selected. MRIs were graded using the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS) at the beginning of the 4-year timeframe (index visit), at 2-year, and 4-year follow-up. Morphological and clinical findings associated with KL 3/4 onset within 2 years compared to 4 years were assessed using generalized estimating equations. RESULTS: AKOA was found in 162 knees of 149 subjects (age 63.25 ± 8.3; 103 females; BMI 29.4 ± 3.9). Moderate to severe meniscal lesions WORMS ≥ 3 were present in 25% (41/162) at the index visit, 64% (104/162) at 2-year and 93% (151/162) at 4-year follow-up. Meniscal extrusion was the most prevalent finding (ranging from 18% at the index visit, 45% at 2-year and 94% at 4-year follow-up) and root tears were the most common types of tears (9% at the index visit; 22% at 2 years and 38% at 4 years). Risk factors associated with KL 3/4 onset within 2 years included root tears at the index visit (adjusted OR, 2.82; 95% CI: 1.33, 6.00; p = 0.007) and incident knee injury (42%, 49/116 vs. 24%, 11/46, p = 0.032). CONCLUSIONS: Meniscal abnormalities, in particular extrusion and root tears, were the most prevalent morphological features found in subjects with AKOA. These results suggest that meniscal abnormalities have a significant role in accelerated progression of OA.

Is treated HIV infection associated with knee cartilage degeneration and structural changes? A longitudinal study using data from the osteoarthritis initiative.

BACKGROUND: Metabolic disorders presenting in HIV-infected patients on antiretroviral therapy (ART) may increase the risk of osteoarthritis. However, structural changes of the knee in HIV infected subjects are understudied. The aim of this study is to investigate knee cartilage degeneration and knee structural changes over 8 years in subjects with and without HIV infection determined based on the use of ART. METHODS: We studied 10 participants from the Osteoarthritis Initiative who received ART at baseline and 20 controls without ART, frequency matched for age, sex, race, baseline body mass index (BMI) and Kellgren & Lawrence grade. Knee abnormalities were assessed using the whole-organ magnetic resonance imaging score (WORMS) and cartilage T2 including laminar and texture analyses were analyzed using a multislice-multiecho spin-echo sequence. Signal abnormalities of the infrapatellar fat pad (IPFP) and suprapatellar fat pad (SPFP) were assessed separately using a semi-quantitative scoring system. Linear regression models were used in the cross-sectional analysis to compare the differences between ART/HIV subjects and controls in T2 (regular and laminar T2 values, texture parameters) and morphologic parameters (subscores of WORMS, scores for signal alterations of IPFP and SPFP). Mixed effects models were used in the longitudinal analysis to compare the rate of change in T2 and morphological parameters between groups over 8 years. RESULTS: At baseline, individuals on ART had significantly greater size of IPFP signal abnormalities (P = 0.008), higher signal intensities of SPFP (P = 0.015), higher effusion scores (P = 0.009), and lower subchondral cysts sum scores (P = 0.003) compared to the controls. No significant differences were found between the groups in T2-based cartilage parameters and WORMS scores for cartilage, meniscus, bone marrow edema patterns and ligaments (P > 0.05). Longitudinally, the HIV cohort had significantly higher global knee T2 entropy values (P = 0.047), more severe effusion (P = 0.001) but less severe subchondral cysts (P = 0.002) on average over 8 years. CONCLUSIONS: Knees of individuals with HIV on ART had a more heterogeneous cartilage matrix, more severe synovitis and abnormalities of the IPFP and SPFP, which may increase the risk of incident knee osteoarthritis.

Validation of scoring hip osteoarthritis with MRI (SHOMRI) scores using hip arthroscopy as a standard of reference.

PURPOSE: To validate SHOMRI gradings in preoperative hip magnetic resonance imaging (MRI) with intra-arthroscopic evaluation of intraarticular hip abnormalities. METHODS: Preoperative non-arthrographic 3.0-T MRIs of 40 hips in 39 patients (1 patient with bilateral hip surgery) with femoroacetabular impingement (FAI) syndrome (mean age, 34.7 years ± 9.0; n = 16 females), refractory to conservative measures, that underwent hip arthroscopy were retrospectively assessed by two radiologists for chondrolabral abnormalities and compared with intra-arthroscopic findings as the standard of reference. Arthroscopically accessible regions were compared with the corresponding SHOMRI subregions and assessed for the presence and grade of cartilaginous pathologies in the acetabulum and femoral head. The acetabular labrum was assessed for the presence or absence of labral tears. For the statistical analysis sensitivity and specificity as well as intraclass correlation (ICC) for interobserver agreement were calculated. RESULTS: Regarding chondral abnormalities, 58.8% of the surgical cases showed chondral defects. SHOMRI scoring showed a sensitivity of 95.7% and specificity of 84.8% in detecting cartilage lesions. Moreover, all cases with full-thickness defects (n = 9) were identified correctly, and in n = 6 cases (out of n = 36 with partial-thickness defects) the defective cartilage was identified but the actual depth overestimated. Labral tears were present in all cases and the MR readers identified 92.5% correctly. ICC showed a good interobserver agreement with 86.3% (95% CI 80.0, 90.6%) CONCLUSION: Using arthroscopic correlation, SHOMRI grading of the hip proves to be a reliable and precise method to assess chondrolabral hip joint abnormalities. KEY POINTS: • Assessment of hip abnormalities using MRI with surgical correlation. • Comparing surgery and MRI by creating a hybrid anatomic map that covers both modalities. • Non-arthrographic use of 3.0-T MRI provides detailed information on cartilage and labral abnormalities in hip joints.

Computed Tomography and Magnetic Resonance Imaging Parameters Associated with Poor Clinical Outcome in Spondylodiscitis.

BACKGROUND: Prognostic factors for the disease course of patients with spondylodiscitis have not been well studied. METHODS: The prognostic value of initial magnetic resonance imaging (MRI) and computed tomography imaging parameters was analyzed in 62 patients (47% women; mean age ± SD, 71.6 ± 9.6 years) with a confirmed diagnosis of spondylodiscitis. The disease course was separately evaluated during initial treatment response during hospitalization, relapse, and clinical short-term follow-up at 3 months. RESULTS: Overall CT findings graded as definitely inflammatory (P = 0.006), reduced disc height on MRI (P = 0.044) and fluid-equivalent hyperintensity of discs on T2 short tau inversion recovery-weighted sequences (P = 0.047) were associated with poor initial treatment response. High initial C-reactive protein value (>10.1 mg/dL) was associated with a higher relapse rate (P = 0.038). Risk factors for poor outcome were infection with low-virulence bacteria (P = 0.040) and overall MRI findings atypical for infection (P = 0.027). CONCLUSIONS: Compared with MRI, CT imaging parameters have a higher prognostic value regarding the disease course. Patients infected with low-virulence bacteria and atypical MRI findings are at higher risk for poor clinical outcome and thus warrant closer monitoring.

MR and CT Imaging to Optimize CT-Guided Biopsies in Suspected Spondylodiscitis.

BACKGROUND: The diagnostic value of computed tomography (CT)-guided spinal biopsy in patients with suspected spondylodiscitis is reported inconsistently in the literature. Our aim was to evaluate associations between procedural, clinical, and imaging parameters and the diagnostic yield of CT-guided spinal biopsy. METHODS: One hundred and two procedures performed in 87 patients with clinically suggested spondylodiscitis were analyzed retrospectively. Preprocedural magnetic resonance (MR) and CT images were evaluated regarding signal alterations, vertebral destruction, and soft-tissue involvement. The position of the biopsy needle in correlation with MR imaging findings was assessed. Patient characteristics and clinical details were noted. Parameters were compared in patients with positive and negative microbiological and histologic results. RESULTS: Following microbiologic and histologic analysis, infectious spondylodiscitis was diagnosed in 29 and 23 biopsies, respectively. Microbiology results were significantly higher in biopsy specimens with central needle positioning within contrast enhancing tissue in correlation with the MR images (36% vs. 7%; P = 0.005). Biopsy specimens positioned in fluid-equivalent hyperintense discs in T2-weighted sequences yielded significantly lower microbiology results (6% vs. 33%; P = 0.036). Purely lytic endplate destruction and mixed vertebral density as shown by CT increased microbiology results (60% vs. 24%; P = 0.028). Previous antibiotic treatment for any cause did not influence microbiology yields significantly (P = 0.232). CONCLUSIONS: MR imaging is mandatory to determine the optimal biopsy position. No clinical or imaging parameter could rule out a positive biopsy result and thus omit an unnecessary procedure. Biopsy should not be avoided if antibiotic treatment has previously been administered.