Trabecular bone texture analysis of conventional radiographs in the assessment of knee osteoarthritis: review and viewpoint

Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression

The present study aims to examine whether the short-term variations in trabecular bone texture (TBT) parameters, combined with a targeted set of clinical and radiographic data, would improve the prediction of long-term radiographic knee osteoarthritis (KOA) progression. Longitudinal (baseline, 24 and 48-month) data, obtained from the Osteoarthritis Initiative cohort, were available for 1352 individuals, with preexisting OA (1 < Kellgren-Lawrence < 4) at baseline. KOA progression was defined as an increase in the medial joint space narrowing score from the 24-months to the 48-months control point. 16 regions of interest were automatically selected from each radiographic knee and analyzed using fractal dimension. Variations from baseline to 24 months in TBT descriptors as well as selected radiographic and clinical readings were calculated. Different logistic regression models were developed to evaluate the progression prediction performance when associating TBT variations with the selected clinical and radiographic readings. The most predictive model was mainly determined using the area under the receiver operating characteristic curve (AUC). The proposed prediction model including short-term variations in TBT parameters, associated with clinical covariates and radiographic scores, improved the capacity of predicting long-term radiographic KOA progression (AUC of 0.739), compared to models based solely on baseline values (AUC of 0.676, p-value < 0.008).

Biomarkers in osteoarthritis: current status and outlook - the FNIH Biomarkers Consortium PROGRESS OA study

Currently, no disease-modifying therapies are approved for osteoarthritis (OA) use. One obstacle to trial success in this field has been our existing endpoints' limited validity and responsiveness. To overcome this impasse, the Foundation for the NIH OA Biomarkers Consortium is focused on investigating biomarkers for a prognostic context of use for subsequent qualification through regulatory agencies. This narrative review describes this activity and the work underway, focusing on the PROGRESS OA study.

Osteoarthritis year in review 2022: imaging

PURPOSE

This narrative review summarizes original research focusing on imaging in osteoarthritis (OA) published between April 1st 2021 and March 31st 2022. We only considered English publications that were in vivo human studies.

METHODS

The PubMed, Medline, Embase, Scopus, and ISI Web of Science databases were searched for "Osteoarthritis/OA" studies based on the search terms: "Radiography", "Ultrasound/US", "Computed Tomography/CT", "DXA", "Magnetic Resonance Imaging/MRI", "Artificial Intelligence/AI", and "Deep Learning". This review highlights the anatomical focus of research on the structures within the tibiofemoral, patellofemoral, hip, and hand joints. There is also a noted focus on artificial intelligence applications in OA imaging.

RESULTS

Over the last decade, the increasing trend of using open-access large databases has reached a plateau (from 17 to 37). Compositional MRI has had the most prominent use in OA imaging and its biomarkers have been used in the detection of preclinical OA and prediction of OA outcomes. Most noteworthy, there has been an accelerated rate of publications on the implications of artificial intelligence, used in developing prediction models and performing trabecular texture analysis, in OA imaging (from 17 to 154).

CONCLUSIONS

While imaging has maintained its key role in OA research, publication trends have shown an emphasis on the integration of AI. During the past year, MRI has maintained the highest prevalence in usage while US and CT remain as readily available modalities. Finally, there has been a notable uptake in the development and validation of AI techniques used to perform texture analysis and predict OA progression.

Evaluation of trabecular changes following advancement genioplasty combined with or without bilateral sagittal split osteotomy by fractal analysis: a retrospective cohort study

BACKGROUND

It is aimed to investigate whether there was a difference in radiographic changes in the operational areas between genioplasty alone and genioplasty combined with mandibular advancement and to evaluate the fractal dimension (FD) to assess trabecular changes after genioplasty surgery.

METHODS

Preoperative-(T0) and postoperative-(T1) panoramic radiographs of 26 patients without any complications who underwent genioplasty combined with bilateral sagittal osteotomy and mandibular advancement or genioplasty alone were selected. In the panoramic radiographs of both groups, the genial segment, mandibular angulus, and surgical osteotomy line were examined using FD. The box-counting method was used for FD evaluation.

RESULTS

It was determined that FD values before and after treatment were similar in both groups for all regions where measurements were made. After surgery, the FD values of the middle region of the genial segment were found to be significantly lower than the other regions. At T1, the FD values at the osteotomy area were found to be significantly higher than those in the middle region of the genial segment.

CONCLUSION

Trabecular structure does not differ in patients undergoing genioplasty alone or in combination with mandibular advancement osteotomy. The middle region of the genial segment heals later than other regions.

Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis

Background

To develop a magnetic resonance imaging (MRI)-based radiomics predictive model for the identification of knee osteoarthritis (OA), based on the tibial and femoral subchondral bone, and compare with the trabecular structural parameter-based model.

Methods

Eighty-eight consecutive knees were scanned with 3T MRI and scored using MRI osteoarthritis Knee Scores (MOAKS), in which 56 knees were diagnosed to have OA. The modality of sagittal three-dimensional balanced fast-field echo sequence (3D BFFE) was used to image the subchondral bone. Four trabecular structural parameters (bone volume fraction [BV/TV], trabecular thickness [Tb.Th], trabecular separation [Tb.Sp], and trabecular number) and 93 radiomics features were extracted from four regions of the lateral and medial aspects of the femur condyle and tibial plateau. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Machine learning-based support vector machine models were constructed to identify knee OA. The performance of the models was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC). The correlation between radiomics features and trabecular structural parameters was analyzed using Pearson’s correlation coefficient.

Results

Our radiomics-based classification model achieved the AUC score of 0.961 (95% confidence interval [CI], 0.912–1.000) when distinguishing between normal and knee OA, which was higher than that of the trabecular parameter-based model (AUC, 0.873; 95% CI, 0.788–0.957). The first-order, texture, and Laplacian of Gaussian-based radiomics features correlated positively with Tb.Th and BV/TV, but negatively with Tb.Sp (P < 0.05).

Conclusions

Our results suggested that our MRI-based radiomics models can be used as biomarkers for the classification of OA and are superior to the conventional structural parameter-based model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-022-03314-y.

Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty

Lacking disease-modifying osteoarthritis drugs (DMOADs) for knee osteoarthritis (KOA), Total Knee Arthroplasty (TKA) is often considered an important clinical outcome. Thus, it is important to determine the most relevant factors that are associated with the risk of TKA. The present study aims to develop a model based on a combination of X-ray trabecular bone texture (TBT) analysis, and clinical and radiological information to predict TKA risk in patients with or at risk of developing KOA. This study involved 4382 radiographs, obtained from the OsteoArthritis Initiative (OAI) cohort. Cases were defined as patients with TKA on at least one knee prior to the 108-month follow-up time point and controls were defined as patients who had never undergone TKA. The proposed TKA-risk prediction model, combining TBT parameters and Kellgren–Lawrence (KL) grades, was performed using logistic regression. The proposed model achieved an AUC of 0.92 (95% Confidence Interval [CI] 0.90, 0.93), while the KL model achieved an AUC of 0.86 (95% CI 0.84, 0.86; p < 0.001). This study presents a new TKA prediction model with a good performance permitting the identification of at risk patient with a good sensitivy and specificity, with a 60% increase in TKA case prediction as reflected by the recall values.

Study of the Efficacy of Artificial Intelligence Algorithm-Based Analysis of the Functional and Anatomical Improvement in Polynucleotide Treatment in Knee Osteoarthritis Patients: A Prospective Case Series

Knee osteoarthritis (OA) is one of the most common degenerative diseases in old age. Recent studies have suggested new treatment approaches dealing with subchondral remodeling, which is a typical feature of OA progression. However, diagnostic tools or therapeutic approaches related to such a process are still being researched. The automated artificial intelligence (AI) algorithm-based texture analysis is a new method used for OA-progression detection. We designed a prospective case series study to examine the efficacy of the AI algorithm-based texture analysis in detecting the restoration of the subchondral remodeling process, which is expected to follow therapeutic intervention. In this study, we used polynucleotide (PN) filler injections as the therapeutic modality and the treatment outcome was verified by symptom improvement, as well as by the induction of subchondral microstructural changes. We used AI algorithm-based texture analysis to observe these changes in the subchondral bone with the bone structure value (BSV). A total of 51 participants diagnosed with knee OA were enrolled in this study. Intra-articular PN filler (HP cell Vitaran J) injections were administered once a week and five times in total. Knee X-rays and texture analyses with BSVs were performed during the screening visit and the last visit three months after screening. The Visual Analogue Scale (VAS) and Korean-Western Ontario MacMaster (K-WOMAC) measurements were used at the screening visit, the fifth intra-articular injection visit, and the last visit. The VAS and K-WOMAC scores decreased after PN treatment and lasted for three months after the final injection. The BSV changed in the middle and deep layers of tibial bone after PN injection. This result could imply that there were microstructural changes in the subchondral bone after PN treatment, and that this change could be detected using the AI algorithm-based texture analysis. In conclusion, the AI- algorithm-based texture analysis could be a promising tool for detecting and assessing the therapeutic outcome in knee OA.