MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee                     Osteoarthritis Incidence

Li, Jia; Fu, Shuai; Gong, Ze; Zhu, Zhaohua; Cao, Peihua; Lin, Ting; Chen, Tianyu; Wang, Xiaoshuai; Lartey, Richard; Kwoh, C. Kent; Guermazi, Ali; Roemer, Frank W.; Hunter, David J.; Ma, Jianhua; Ding, Changhai

doi:10.1148/radiol.212009

Cited by 32 publications

(20 citation statements)

References 28 publications

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“…The results showed that the model using IPFP radiomic features (AUC =0.700) was superior to both the Hoffa-synovitis alone model (AUC = 0.598) and the Hoffa-synovitis plus clinical characteristics model (AUC =0.651) in iROA prediction. Further, during the revision of this manuscript, another study using 20 IPFP texture features also indicated that the MR-based texture analysis of the IPFP could help predict iROA 1 year before iROA diagnosis (59). This study and our own both found that IPFP texture features were predictive in iROA and outperformed clinical characteristics and/or traditional semiquantitative assessment (i.e., Hoffa-synovitis scoring system).…”

Section: Discussionmentioning

confidence: 50%

Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative

Ying

Zhao

et al. 2023

Quant Imaging Med Surg

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Background:The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis.Methods: Data used in this work were obtained from the osteoarthritis initiative (OAI). In this study, iROA was defined as a knee with a baseline Kellgren-Lawrence grade (KLG) of 0 or 1 that further progressed to KLG ≥2 during the follow-up visit. Intermediate-weighted turbo spin-echo knee MR images at the time of iROA diagnosis and 1 year prior were obtained. Five clinical characteristics-age, sex, body mass index, knee injury history, and knee surgery history-were obtained. A total of 604 knees were selected and matched (302 cases and 302 controls). A U-Net segmentation model was independently trained to automatically segment the IPFP. The prediction models were established in the training set (60%). Three main models were generated using (I) clinical characteristics; (II) radiomic features; (III) combined (clinical plus radiomic) features. Model performance was evaluated in an independent testing set (remaining 40%) using the area under the curve (AUC).Two secondary models were also generated using Hoffa-synovitis scores and clinical characteristics. Results:The comparison between the automated and manual segmentations of the IPFP achieved a Dice coefficient of 0.900 (95% CI: 0.891-0.908), which was comparable to that of experienced radiologists. The radiomic features model and the combined model yielded superior AUCs of 0.700 (95% CI: 0.630-0.763) and 0.702 (95% CI: 0.635-0.763), respectively. The DeLong test found no statistically significant difference between the receiver operating curves of the radiomic and combined models (P=0.831); however, both models outperformed the clinical model (P=0.014 and 0.004, respectively).Conclusions: Our results demonstrated that radiomic features of the IPFP are predictive of iROA 1 year prior to the diagnosis, suggesting that IPFP radiomic features can serve as an early quantitative prediction biomarker of iROA.

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Section: Discussionmentioning

confidence: 50%

Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative

Ying

Zhao

et al. 2023

Quant Imaging Med Surg

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“…Finally, three recent studies reported quantitative MR evaluation of the IFP in knee OA. 13 – 15 Zhong et al 13 investigated the characteristics of fat content and component in IFP by means of hydrogen proton MR spectroscopy (1 H-MRS) in knee OA. Alteration of fat fraction in IFP was associated with the radiographic severity of knee OA, MOAKS-based synovitis, and knee pain.…”

Section: Recent Developments In Semiquantitative and Quantitative Mri...mentioning

confidence: 99%

“…Fat fraction and T2* relaxation values decreased as the severity of radiographic OA increased. Li et al 15 performed a prospective nested case-control study including 690 participants with knees at the risk of OA from the Pivotal OA Initiative MRI Analyses incident OA cohort. The study showed MRI-based three-dimensional (3D) texture analysis of IFP was associated with future development of knee OA.…”

Section: Recent Developments In Semiquantitative and Quantitative Mri...mentioning

confidence: 99%

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Latest advancements in imaging techniques in OA

Hayashi

Roemer

Link

et al. 2022

Therapeutic Advances in Musculoskeletal

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The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.

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“…For example, Lei found that among patients aged 50 years and older with osteoarthritis, initial prescription of tramadol was associated with a significantly higher rate of mortality over 1 year of follow-up compared with commonly prescribed nonsteroidal anti-inflammatory drugs [ 19 ]. Based on clinical evidence, Ding found that greater infrapatellar fat pad (IPFP) volume was associated with greater knee cartilage volume and fewer structural abnormalities, suggesting a protective role of IPFP size in knee OA [ 20 ], while IPFP signal intensity alteration and texture score predicted knee structural changes and OA incidence [ 21 , 22 ]. Therefore, we must pay special attention to IPFP in the clinical settings for avoiding resection of normal IPFP in knee surgery.…”

Section: Academic Sharing Sessionmentioning

confidence: 99%

Current progress and trends in musculoskeletal research: Highlights of NSFC-CUHK academic symposium on bone and joint degeneration and regeneration

Tuan

Zhang

Chen

et al. 2022

Journal of Orthopaedic Translation

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MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee Osteoarthritis Incidence

Cited by 32 publications

References 28 publications

Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative

Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative

Latest advancements in imaging techniques in OA

Current progress and trends in musculoskeletal research: Highlights of NSFC-CUHK academic symposium on bone and joint degeneration and regeneration

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