Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Molecular imaging techniques such as PET, SPECT, optical imaging, and contrast-enhanced MRI are revolutionizing degenerative joint disease assessment by visualizing specific biological processes at molecular and cellular levels, surpassing traditional anatomical imaging.
“Molecular imaging techniques, such as positron emission tomography (PET), single-photon emission computed tomography (SPECT), optical imaging, and contrast-enhanced MRI, are revolutionizing degenerative joint disease assessment by visualizing specific biological processes at molecular and cellular levels, surpassing traditional anatomical imaging [163,164,165].”
From Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease (2025) · cites this paper as [164]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
(2010) · Cited by 14,134 · see citing works
Hunter, D. J., & Bierma‐Zeinstra, S. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30417-9 · Cited by 5,127 · see citing works
Kyu, H. H., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32335-3 · Cited by 4,112 · see citing works
The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis
Hoenig, J. M., & Heisey, D. M. (2001) · The American Statistician · doi:10.1198/000313001300339897 · Cited by 2,046 · see citing works
Radiomics in medical imaging—“how-to” guide and critical reflection
van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H., & Baessler, B. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00887-2 · Cited by 1,316 · see citing works
Nioche, C., Orlhac, F., Boughdad, S., Reuzé, S., Goya-Outi, J., Robert, C., Pellot-Barakat, C. J. M., Soussan, M., Frouin, F., & Buvat, I. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0125 · Cited by 1,171 · see citing works
Hunter, D. J., Guermazi, A., Lo, G. H., Grainger, A. J., Conaghan, P. G., Boudreau, R. M., & Roemer, F. W. (2011) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2011.05.004 · Cited by 1,074 · see citing works
Weight Loss Reduces the Risk for Symptomatic Knee Osteoarthritis in Women
Felson, D. T., Zhang, Y., Anthony, J. M., Naimark, A., & Anderson, J. J. (1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-116-7-535 · Cited by 928 · see citing works
PyWavelets: A Python package for wavelet analysis
Lee, G. R., Gommers, R., Waselewski, F., Wohlfahrt, K., & O'Leary, A. (2019) · The Journal of Open Source Software · doi:10.21105/joss.01237 · Cited by 780 · see citing works
PETERFY, C. G., Schneider, E., & Nevitt, M. C. (2008) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2008.06.016 · Cited by 685 · see citing works
Knee osteoarthritis prevalence, risk factors, pathogenesis and features: Part I.
Heidari, B. (2011) · Europe PMC (PubMed Central) · Cited by 600 · see citing works
Turkiewicz, A., Petersson, I. F., Björk, J., HAWKER, G. A., Dahlberg, L., Lohmander, S. L., & Englund, M. (2014) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2014.07.015 · Cited by 489 · see citing works
Cardenas, C. E., Yang, J., Anderson, B., Court, L. E., & Brock, K. (2019) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2019.02.001 · Cited by 411 · see citing works
Synovitis in knee osteoarthritis: a precursor of disease?
Atukorala, I. G., Kwoh, C. K., Guermazi, A., Roemer, F. W., Boudreau, R. M., Hannon, M. J., & Hunter, D. J. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2014-205894 · Cited by 346 · see citing works
Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., & Kijowski, R. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26841 · Cited by 319 · see citing works
History of knee injuries and knee osteoarthritis: a meta-analysis of observational studies
Muthuri, S. G., McWilliams, D. F., Doherty, M., & Zhang, W. (2011) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2011.07.015 · Cited by 284 · see citing works
Synovitis and the risk of knee osteoarthritis: the MOST Study
Felson, D. T., Niu, J., Neogi, T., Goggins, J., Nevitt, M. C., Roemer, F. W., Torner, J. C., Lewis, C. E., & Guermazi, A. (2015) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2015.09.013 · Cited by 266 · see citing works
Evaluation and Treatment of Disorders of the Infrapatellar Fat Pad
Dragoo, J. L., Johnson, C., & McConnell, J. (2011) · Sports Medicine · doi:10.2165/11595680-000000000-00000 · Cited by 233 · see citing works
Infrapatellar fat pad features in osteoarthritis: a histopathological and molecular study
Favero, M., Hadi, H. E., Belluzzi, E., Granzotto, M., Porzionato, A., Sarasin, G., Rambaldo, A., Iacobellis, C., Cigolotti, A., Fontanella, C. G., et al. (2017) · Rheumatology · doi:10.1093/rheumatology/kex287 · Cited by 219 · see citing works
Deep convolutional neural network for segmentation of knee joint anatomy
Zhou, Z., Zhao, G., Kijowski, R., & Liu, F. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27229 · Cited by 202 · see citing works
Infrapatellar Fat Pad and Knee Osteoarthritis
Zeng, N., Yan, Z.-P., Chen, X., & Ni, G. (2020) · Aging and Disease · doi:10.14336/ad.2019.1116 · Cited by 166 · see citing works
Kerkhof, H. J. M., Bierma‐Zeinstra, S., Arden, N., Metrustry, S., Castano-Betancourt, M. C., Hart, D. J., Albert Hofman, D., Rivadeneira, F., Oei, E. H. G., Spector, T. D., et al. (2013) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2013-203620 · Cited by 151 · see citing works
Joseph, G. B., Baum, T. H., Alizai, H., Carballido‐Gamio, J., Nardo, L., Virayavanich, W., Lynch, J. A., Nevitt, M. C., McCulloch, C. E., Majumdar, S., et al. (2012) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2012.04.003 · Cited by 148 · see citing works
Cusumano, D., Dinapoli, N., Boldrini, L., Chiloiro, G., Gatta, R., Masciocchi, C., Lenkowicz, J., Casà, C., Damiani, A., Azario, L., et al. (2017) · La radiologia medica · doi:10.1007/s11547-017-0838-3 · Cited by 127 · see citing works
Woźnicki, P., Westhoff, N., Huber, T. S., Riffel, P., Froelich, M. F., Gresser, E., von Hardenberg, J., Mühlberg, A., Michel, M. S., Schoenberg, S. O., et al. (2020) · Cancers · doi:10.3390/cancers12071767 · Cited by 116 · see citing works
Infrapatellar fat pad in the knee: is local fat good or bad for knee osteoarthritis?
Han, W., Cai, S., Liu, Z., Jin, X., Wang, X., Antony, B., Cao, Y., Aitken, D. A., Cicuttini, F., Jones, G., et al. (2014) · Arthritis Research & Therapy · doi:10.1186/ar4607 · Cited by 113 · see citing works
Inflammation of the infrapatellar fat pad
Eymard, F., & Chevalier, X. (2016) · Joint Bone Spine · doi:10.1016/j.jbspin.2016.02.016 · Cited by 112 · see citing works
Han, W., Aitken, D. A., Zhu, Z., Halliday, A., Wang, X., Antony, B., Cicuttini, F., Jones, G., & Ding, C. (2015) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2015-208360 · Cited by 105 · see citing works
Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform
Mello-Román, J. C., Noguera, J. L. V., Legal-Ayala, H., Pinto-Roa, D. P., Gómez-Guerrero, S., & García-Torres, M. (2019) · Entropy · doi:10.3390/e21030244 · Cited by 98 · see citing works
Cai, J., Xu, J., Wang, K., Zheng, S., He, F., Huan, S., Xu, S., Zhang, H., Laslett, L. L., & Ding, C. (2015) · The Journal of Rheumatology · doi:10.3899/jrheum.150175 · Cited by 95 · see citing works
MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee Osteoarthritis Incidence
Li, J., Fu, S., Gong, Z., Zhu, Z., Zeng, D., Cao, P., Lin, T., Chen, T., Wang, X., Lartey, R., et al. (2022) · Radiology · doi:10.1148/radiol.212009 · Cited by 94 · see citing works
Weight change and change in tibial cartilage volume and symptoms in obese adults
Teichtahl, A. J., Wluka, A. E., Tanamas, S. K., Wang, Y., Strauss, B. J., Proietto, J., Dixon, J. B., Jones, G., Forbes, A. B., & Cicuttini, F. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2013-204488 · Cited by 92 · see citing works
Byra, M., Wu, M., Zhang, X., Jang, H., Ma, Y., Chang, E. Y., Shah, S. B., & Du, J. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27969 · Cited by 84 · see citing works
Self-citationMedical Imaging 2009: Computer-aided Diagnosis
Karssemeijer, N., & Giger, M. L. (2009) · Cited by 81 · see citing works
Cowan, S., Hart, H. F., Warden, S. J., & Crossley, K. M. (2015) · Rheumatology International · doi:10.1007/s00296-015-3250-0 · Cited by 81 · see citing works
Roemer, F. W., Jarraya, M., Felson, D. T., Hayashi, D., Crema, M. D., Lœuille, D., & Guermazi, A. (2015) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2015.09.018 · Cited by 80 · see citing works
Traverso, A., Kazmierski, M., Welch, M. L., Weiss, J., Fiset, S., Foltz, W. D., Gladwish, A. P., Dekker, A. L. A. J., Jaffray, D. A., Wee, L., et al. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.08.008 · Cited by 72 · see citing works
Alvarez‐Jimenez, C., Sandino, Á. A., Prasanna, P., Gupta, A. B., Viswanath, S. E., & Romero, E. (2020) · Cancers · doi:10.3390/cancers12123663 · Cited by 66 · see citing works
Baum, T. H., Joseph, G. B., Nardo, L., Virayavanich, W., Arulanandan, A., Alizai, H., Carballido‐Gamio, J., Nevitt, M. C., Lynch, J. A., McCulloch, C. E., et al. (2012) · Arthritis Care & Research · doi:10.1002/acr.21741 · Cited by 64 · see citing works
Hirvasniemi, J., Klein, S., Bierma-Zeinstra, S., Vernooij, M. W., Schiphof, D., & Oei, E. H. G. (2021) · European Radiology · doi:10.1007/s00330-021-07951-5 · Cited by 63 · see citing works
Learning osteoarthritis imaging biomarkers from bone surface spherical encoding
Martinez, A. M., Calivá, F., Flament, I., Liu, F., Lee, J., Cao, P., Shah, R., Majumdar, S., & Pedoia, V. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28251 · Cited by 57 · see citing works
Schooler, J., Kumar, D., Nardo, L., McCulloch, C. E., Li, X., Link, T. M., & Majumdar, S. (2013) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2013.10.014 · Cited by 56 · see citing works
Janvier, T., Jennane, R., Toumi, H., & Lespessailles, É. (2017) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2017.09.004 · Cited by 54 · see citing works
Sharma, L., Hochberg, M. C., Nevitt, M. C., Guermazi, A., Roemer, F. W., Crema, M. D., Eaton, C. B., Jackson, R. D., Kwoh, K. C., Cauley, J. A., et al. (2017) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2017.02.788 · Cited by 47 · see citing works
Wang, K., Ding, C., Hannon, M. J., Chen, Z., Kwoh, C. K., & Hunter, D. J. (2018) · Arthritis Care & Research · doi:10.1002/acr.23577 · Cited by 45 · see citing works
Masaki, T., Takahashi, K. A., Hashimoto, S., Ikuta, F., Watanabe, A., Kiuchi, S., Okuaki, T., Mochizuki, Y., & Takai, S. (2018) · Journal of Orthopaedic Research® · doi:10.1002/jor.24201 · Cited by 37 · see citing works
Ou, J., Wu, L., Li, R., Wu, C., Liu, J., Chen, T., Zhang, X. M., Tang, S., Wu, Y., Yang, L.-Q., et al. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-241 · Cited by 37 · see citing works
Self-citationChen, F., Su, W., Bedenbaugh, A. V., & Oruc, A. (2020) · Journal of Medical Economics · doi:10.1080/13696998.2020.1801453 · Cited by 31 · see citing works
Eckstein, F., Maschek, S., Roemer, F. W., Duda, G. N., Sharma, L., & Wirth, W. (2018) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2018.10.006 · Cited by 29 · see citing works
Peuna, A., Hekkala, J., Haapea, M., Podlipská, J., Guermazi, A., Saarakkala, S. S., Nieminen, M. T., & Lammentausta, E. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25881 · Cited by 29 · see citing works
CT Radiomic Features for Predicting Resectability and TNM Staging in Thymic Epithelial Tumors
Araújo-Filho, J. D. A. B., Mayoral, M., Zheng, J., Tan, K. S., Gibbs, P., Shepherd, A. F., Rimner, A., Simone, C. B., Riely, G. J., Huang, J. G., et al. (2021) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2021.03.084 · Cited by 24 · see citing works
Post Hoc Power Calculation: Observing the Expected
Plate, J. D. J., Borggreve, A. S., van Hillegersberg, R., & Peelen, L. M. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002910 · Cited by 20 · see citing works
van der Heijden, R. A., de Vries, B. A., Poot, D. H. J., van Middelkoop, M., Bierma‐Zeinstra, S., Krestin, G. P., & Oei, E. H. G. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-441 · Cited by 17 · see citing works
Asai, K., Nakase, J., Oshima, T., Shimozaki, K., Yoshimizu, R., & Tsuchiya, H. (2020) · Archives of Orthopaedic and Trauma Surgery · doi:10.1007/s00402-020-03520-5 · Cited by 16 · see citing works
Garriga, C., Sanchez-Santos, M. T., Judge, A., Hart, D. J., Spector, T. D., Cooper, C. C., & Arden, N. (2019) · Arthritis Care & Research · doi:10.1002/acr.23932 · Cited by 14 · see citing works
Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy
Franzese, C., Cozzi, L., Badalamenti, M., Baldaccini, D., D’Agostino, G. R., Fogliata, A., Navarria, P., Franceschini, D., Comito, T., Clerici, E., et al. (2022) · Strahlentherapie und Onkologie · doi:10.1007/s00066-021-01886-y · Cited by 11 · see citing works
Zhu, Z., Han, W., Lu, M., Lin, J., Yin, Z., Shang, X., Weng, X., Zha, Z., Tian, J. H., Lei, G., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-043088 · Cited by 11 · see citing works
Comment on “Post-hoc Power: If You Must, At Least Try to Understand”
Althouse, A. D., & Rafi, Z. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000003296 · Cited by 5 · see citing works
Iyer, S. R., Ismail, M., Tamrazi, B., Margol, A., Verma, R., Correa, R., Prasanna, P., Beig, N. G., Bera, K., Statsevych, V., et al. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2513567 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [32]P KD, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv 2014:1421.6980.
Cited by
Sorted by citations
Radiomics signature of osteoarthritis: Current status and perspective
Jiang, T., Lau, S. H., Zhang, J., Chan, L.-C., Wang, W., Ping Keung, C. H. A. N., Cai, J., & Wen, C. (2024) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2023.10.003 · Cited by 31 · see citing works
Progress in multi-omics studies of osteoarthritis
Wei, Y., He, Q., Zhang, X., Wang, J., Yan, H., Xiao, N., Zeng, S., Chen, B., Yang, Q., Lu, H., et al. (2025) · Biomarker Research · doi:10.1186/s40364-025-00732-y · Cited by 25 · see citing works
Self-citationTingrun, C., Ruilong, L. I. U., Jiazheng, X. U., Fu, J. Q., Li, Z., 郝立波, Yang, J., Yanli, S. H. I., Fu, J. Q., & Chen, J. (2023) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-023-03837-y · Cited by 25 · see citing works
Infrapatellar fat pad as a source of biomarkers and therapeutic target for knee osteoarthritis
Herrera, B. P., Emanuel, K. S., Emans, P. J., van Griensven, M., & Cillero‐Pastor, B. (2025) · Arthritis Research & Therapy · doi:10.1186/s13075-025-03517-8 · Cited by 19 · see citing works
Zhang, Q., Geng, J., Zhang, M., Kan, T., Wang, L., Ai, S., Wei, H., Zhang, L., & Liu, C. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-1245 · Cited by 12 · see citing works
Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease
Kumar, R., Sporn, K., Borole, A., Khanna, A., Gowda, C., Paladugu, P., Ngo, A., Jagadeesan, R., Zaman, N., & Tavakkoli, A. (2025) · Diagnostics · doi:10.3390/diagnostics15111418 · Cited by 10 · see citing works
Herrera, D., Almhdie-Imjabbar, A., Toumi, H., & Lespessailles, É. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111731 · Cited by 10 · see citing works
Kiso, T., Okada, Y., Kawata, S., Shichiji, K., Okumura, E., Hatsumi, N., Matsuura, R., Kaminaga, M., Kuwano, H., & Okumura, E. (2025) · European Journal of Radiology Open · doi:10.1016/j.ejro.2025.100649 · Cited by 10 · see citing works
Davey, M. S., Davey, M. G., Kenny, P. J., & Cassar-Gheiti, A. J. (2024) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-024-03714-5 · Cited by 9 · see citing works
Chen, T., Chen, J., Liu, H., Liu, Z., Yu, B., Wang, Y., Zhao, W., Peng, Y.-X., Li, J., Yang, Y. P., et al. (2025) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2025.01.007 · Cited by 9 · see citing works
Liu, Y., Xiao, G., Zhang, Y., Wang, X., Jia, J., Xie, A., Zheng, Z., & Zhang, K. (2025) · Journal of Medical Internet Research · doi:10.2196/80430 · Cited by 7 · see citing works
Khan, S., Lartey, R., Obuchowski, N. A., Gaj, S., Kim, J., Li, M., Eck, B., Altahawi, F. F., Jones, M. H., Huston, L. J., et al. (2025) · Osteoarthritis Imaging · doi:10.1016/j.ostima.2025.100263 · Cited by 5 · see citing works
Osteoarthritis: Epidemiology, Diagnosis, and Treatment
Ru, Q., Yuan, D. Z., Jiang, S., Zhao, X., Liu, X., Li, Y., Liu, S., & Wu, Y. (2026) · MedComm · doi:10.1002/mco2.70834 · Cited by 3 · see citing works
Zhao, F., Chen, Y., Zhou, X., Chen, X., Han, C., Lu, X., Song, Y., Lin, C., Gu, R., Yang, F., et al. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.03.054 · Cited by 2 · see citing works
Lyu, L., Ren, J., Lu, W., Li, Y., Zhong, J., & Yao, W. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1730 · Cited by 2 · see citing works
Lyu, L., Ren, J., Lu, W., Zhong, J., Song, Y., Li, Y., & Yao, W. (2025) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2025.1535519 · Cited by 2 · see citing works
Role of Infrapatellar Fat Tissue in the Pathogenesis of Knee Osteoarthritis: A Review
Korneva, Y. S., & Borisenko, M. B. (2023) · Traumatology and Orthopedics of Russia · doi:10.17816/2311-2905-15999 · Cited by 2 · see citing works
Hu, W., Yang, J., Liu, L., Li, D., Zhao, Y., & Wang, A. (2024) · Cartilage · doi:10.1177/19476035241288660 · Cited by 1 · see citing works
KNN algorithm for accurate identification of IFP lesions in the knee joint: a multimodal MRI study
Zhou, P., Liu, Z., Dai, J., Yang, M., Sui, H., Huang, Z., Li, Y., & Song, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-02786-2 · Cited by 1 · see citing works
Mu, L., Fu, J., Li, M., Liu, H., Dong, D., Gong, J., Jiang, Y., Huai, X., Xu, P., & Zhang, H. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112480 · Cited by 1 · see citing works
Consolini, J., Koch, K. M., Burge, A. J., Xia, E., Majumdar, S., Gold, G. E., Potter, H. G., & Koff, M. F. (2025) · Osteoarthritis Imaging · doi:10.1016/j.ostima.2025.100379 · Cited by 1 · see citing works
Zeng, J., Liang, D., & Tang, G. (2025) · Cartilage · doi:10.1177/19476035251320747 · Cited by 1 · see citing works
Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach
Saraswat, A., Tooley, T., Shrivastav, S., & Srivastava, D. K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-8429-5_9
Radiomics in application to diseases of the musculoskeletal system: a review
Pleshkov, M. O., Zamyshevskaya, M. A., Kuchinskii, E., Jin, X., Zhang, J., Zavadovskaya, V. D., Zorkaltsev, M. A., Kim, T., Pogonchenkova, D. A., Udodov, V. D., et al. (2025) · Digital Diagnostics · doi:10.17816/dd633978
Wirth, W., & Eder, J. (2025) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2025.100719
Cui, T., Ruilong, L. I. U., Jing, Y., Fu, J., & Jiying, C. H. E. N. (2024) · Figshare · doi:10.6084/m9.figshare.26590682
Kim, K., Zaylor, W., Khan, S., Lartey, R., Eck, B., Li, M., Gaj, S., Kim, J., Winalski, C. S., Altahawi, F. F., et al. (2025) · Osteoarthritis Imaging · doi:10.1016/j.ostima.2025.100377
Zhu, Z., Zhu, Z., Li, Y., Kong, L., Dong, W., & Ning, R. (2026) · Arthritis Research & Therapy · doi:10.1186/s13075-026-03805-x
Song, S., Oh, H. S., Kim, S. J., & Lee, H. S. (2026) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-026-10295-0
Ding, C., Dang, Q., Wang, Z., Zong, X., Yu, J., Cen, H., Cao, P., Wang, X., Qi, W., Huang, S., et al. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113153
Wang, X., Yu, Y., Wencai, L., & Ouyang, Y. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113239
Artificial intelligence in osteoarthritis and osteoporosis: implications for management and research
Fairley, J. L., Kapoor, M., & Sharma, D. (2026) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2026.102209
Marth, A. A., Ziegeler, K., Joseph, G. B., Liu, F., Zwerin, R., Lynch, J. A., Lane, N. E., McCulloch, C. E., Nevitt, M. C., & Link, T. M. (2026) · Osteoarthritis Imaging · doi:10.1016/j.ostima.2026.100479
- Authors
- Yu, K., Ying, J., Zhao, T., Lei, L., Zhong, L., Hu, J., Zhou, J. W., Huang, C., & Zhang, X.
- Affiliations
- Third Affiliated Hospital of Southern Medical University (CN)
- Stony Brook University (US)
- Northside Hospital (US)
- Stony Brook Medicine (US)
- Corresponding author
- Huang, Chuan
- Zhang, Xiaodong
- Venue
- Quantitative Imaging in Medicine and Surgery
- Publisher
- AME Publishing Company
- Published
- 2023-01
- Volume
- 13
- Issue
- 1
- Pages
- 352-369
- ISSN
- 2223-4292 (ISSN-L)
- 2223-4306
- PMC
- PMC9816749
- OpenAlex
- W4313456974
- Cited by
- 34
- References
- 61
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 7 · 2023: 3 · 2022: 1
- FWCI
- 3.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Quantitative Imaging in Medicine and Surgery (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Southern Medical University
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Rheumatology › Osteoarthritis Treatment and Mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- knee osteoarthritis, infrapatellar fat pad, radiomics, magnetic resonance imaging, prediction model, U-Net segmentation, Kellgren-Lawrence grade, Osteoarthritis Initiative, clinical risk factors, body mass index, knee injury history
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
