Dataset Information

Molecular Classification of Knee Osteoarthritis.

ABSTRACT: Knee osteoarthritis (KOA) is the most common form of joint degeneration with increasing prevalence and incidence in recent decades. KOA is a molecular disorder characterized by the interplay of numerous molecules, a considerable number of which can be detected in body fluids, including synovial fluid, urine, and blood. However, the current diagnosis and treatment of KOA mainly rely on clinical and imaging manifestations, neglecting its molecular pathophysiology. The mismatch between participants' molecular characteristics and drug therapeutic mechanisms might explain the failure of some disease-modifying drugs in clinical trials. Hence, according to the temporal alteration of representative molecules, we propose a novel molecular classification of KOA divided into pre-KOA, early KOA, progressive KOA, and end-stage KOA. Then, progressive KOA is furtherly divided into four subtypes as cartilage degradation-driven, bone remodeling-driven, inflammation-driven, and pain-driven subtype, based on the major pathophysiology in patient clusters. Multiple clinical findings of representatively investigated molecules in recent years will be reviewed and categorized. This molecular classification allows for the prediction of high-risk KOA individuals, the diagnosis of early KOA patients, the assessment of therapeutic efficacy, and in particular, the selection of homogenous patients who may benefit most from the appropriate therapeutic agents.

SUBMITTER: Lv Z

PROVIDER: S-EPMC8429960 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Osteoarthritis (OA) is a global healthcare problem. The increasing population of OA patients demands a greater bandwidth of imaging and diagnostics. It is important to provide automatic and objective diagnostic techniques to address this challenge. This study demonstrates the utility of unsupervised domain adaptation (UDA) for automated OA phenotype classification. We collected 318 and 960 three-dimensional double-echo steady-state magnetic resonance images from the Osteoarthritis Initiative (OAI) dataset as the source dataset for phenotype cartilage/meniscus and subchondral bone, respectively. Fifty three-dimensional turbo spin echo (TSE)/fast spin echo (FSE) MR images from our institute were collected as the target datasets. For each patient, the degree of knee OA was initially graded according to the MRI Knee Osteoarthritis Knee Score before being converted to binary OA phenotype labels. The proposed four-step UDA pipeline included (I) pre-processing, which involved automatic segmentation and region-of-interest cropping; (II) source classifier training, which involved pre-training a convolutional neural network (CNN) encoder for phenotype classification using the source dataset; (III) target encoder adaptation, which involved unsupervised adjustment of the source encoder to the target encoder using both the source and target datasets; and (IV) target classifier validation, which involved statistical analysis of the classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. We compared our model on the target data with the source pre-trained model and the model trained with the target data from scratch. For phenotype cartilage/meniscus, our model has the best performance out of the three models, giving 0.90 [95% confidence interval (CI): 0.79-1.02] of the AUROC score, while the other two model show 0.52 (95% CI: 0.13-0.90) and 0.76 (95% CI: 0.53-0.98). For phenotype subchondral bone, our model gave 0.75 (95% CI: 0.56-0.94) at AUROC, which has a close performance of the source pre-trained model (0.76, 95% CI: 0.55-0.98), and better than the model trained from scratch on the target dataset only (0.53, 95% CI: 0.33-0.73). By utilising a large, high-quality source dataset for training, the proposed UDA approach enhances the performance of automated OA phenotype classification for small target datasets. As a result, our technique enables improved downstream analysis of locally collected datasets with a small sample size.

Dataset Information

Molecular Classification of Knee Osteoarthritis.

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