Personalized Risk Model and Leveraging of Magnetic Resonance Imaging-Based Structural Phenotypes and Clinical Factors to Predict Incidence of Radiographic Osteoarthritis.

OBJECTIVE: Our study aimed to investigate the association between time to incidence of radiographic osteoarthritis (OA) and magnetic resonance imaging (MRI)-based structural phenotypes proposed by the Rapid Osteoarthritis MRI Eligibility Score (ROAMES). METHODS: A retrospective cohort of 2,328 participants without radiographic OA at baseline were selected from the Osteoarthritis Initiative study. Utilizing a deep-learning model, we automatically assessed the presence of inflammatory, meniscus/cartilage, subchondral bone, and hypertrophic phenotypes from MRIs acquired at baseline and 12-, 24-, 36-, 48-, 72-, and 96-month follow-up visits. In addition to 4 structural phenotypes, we examined severe knee injury history and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores as time dependent. We used Cox proportional hazards regression to analyze the association between 4 structural phenotypes and radiographic OA disease-free survival, both univariate and adjusted for known risk factors including age, sex, race, body mass index, presence of Heberden's nodes, and knee malalignment. RESULTS: Inflammatory (hazard ratio [HR] 3.37 [95% confidence interval (95% CI) 2.45-4.63]), meniscus/cartilage (HR 1.55 [95% CI 1.21-1.98]), and subchondral bone (HR 1.84 [95% CI 1.63-2.09]) phenotypes were associated with time to radiographic OA at P < 0.05 when adjusted for the risk factors. Sex was a modifier of hypertrophic phenotype association with time to radiographic OA. Female participants with the hypertrophic phenotype were associated with 2.8 times higher risk of radiographic OA (95% CI 2.25-7.54) compared to male participants without the hypertrophic phenotype. CONCLUSION: Four ROAMES phenotypes may contribute to time to radiographic OA incidence and if validated could be used as a promising tool for personalized OA management.

Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis.

Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.

Erratum: Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies.

[This corrects the article DOI: 10.1148/ryai.2021200165.].

Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies.

PURPOSE: To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in the cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement. MATERIALS AND METHODS: This retrospective study was conducted on 1435 knee MRI studies (n = 294 patients; mean age, 43 years ± 15 [standard deviation]; 153 women) collected within three previous studies (from 2011 to 2014). All MRI studies were acquired using high-spatial-resolution three-dimensional fast-spin-echo CUBE sequence. Three-dimensional convolutional neural networks were developed to detect the regions of interest within MRI studies and grade abnormalities of the cartilage, bone marrow, menisci, and ACL. Evaluation included sensitivity, specificity, and Cohen linear-weighted ĸ. The impact of AI-aided grading in intergrader agreement was assessed on an external dataset. RESULTS: Binary lesion sensitivity reported for all tissues was between 70% and 88%. Specificity ranged from 85% to 89%. The area under the receiver operating characteristic curve for all tissues ranged from 0.83 to 0.93. Deep learning-assisted intergrader Cohen ĸ agreement significantly improved in 10 of 16 comparisons among two attending physicians and two trainees for all tissues. CONCLUSION: The three-dimensional convolutional neural network had high sensitivity, specificity, and accuracy for knee-lesion-severity scoring and also increased intergrader agreement when used on an external dataset.Supplemental material is available for this article. Keywords: Bone Marrow, Cartilage, Computer Aided Diagnosis (CAD), Computer Applications-3D, Computer Applications-Detection/Diagnosis, Knee, Ligaments, MR-Imaging, Neural Networks, Observer Performance, Segmentation, Statistics © RSNA, 2021See also the commentary by Li and Chang in this issue.: An earlier incorrect version of this article appeared online. This article was corrected on April 16, 2021.

Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI.

PURPOSE: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. MATERIALS AND METHODS: In this retrospective study, 1243 knee MR images (1008 intact, 18 partially torn, 77 fully torn, and 140 reconstructed ACLs) from 224 patients (mean age, 47 years ± 14 [standard deviation]; 54% women) were analyzed. The MRI examinations were performed between 2011 and 2014. A modified scoring metric was used. Classification of ACL injuries using deep learning involved use of two types of CNN, one with three-dimensional (3D) and the other with two-dimensional (2D) convolutional kernels. Performance metrics included sensitivity, specificity, weighted Cohen κ, and overall accuracy, and the McNemar test was used to compare the performance of the CNNs. RESULTS: The overall accuracies for ACL injury classification using the 3D CNN and 2D CNN were 89% (225 of 254) and 92% (233 of 254), respectively (P = .27), and both CNNs had a weighted Cohen κ of 0.83. The 2D CNN and 3D CNN performed similarly in classifying intact ACLs (2D CNN, sensitivity of 93% [188 of 203] and specificity of 90% [46 of 51] vs 3D CNN, sensitivity of 89% [180 of 203] and specificity of 88% [45 of 51]). Classification of full tears by both networks was also comparable (2D CNN, sensitivity of 82% [14 of 17] and specificity of 94% [222 of 237] vs 3D CNN, sensitivity of 76% [13 of 17] and specificity of 100% [236 of 237]). The 2D CNN classified all reconstructed ACLs correctly. CONCLUSION: Two-dimensional and 3D CNNs applied to ACL lesion classification had high sensitivity and specificity, suggesting that these networks could be used to help nonexperts grade ACL injuries. Supplemental material is available for this article. © RSNA, 2020.