Zhu JY, Chen L, Li J, Xu TT. Limited and frequently overlooked radiological evidence of knee osteoarthritis. World J Orthop 2026; 17(3): 115616 [DOI: 10.5312/wjo.v17.i3.115616]
Corresponding Author of This Article
Tao-Tao Xu, MD, PhD, Attending Physician, Associate Professor, Department of Orthopaedics and Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), No. 54 Youdian Road, Shangcheng District, Hangzhou 310006, Zhejiang Province, China. xut@zcmu.edu.cn
Research Domain of This Article
Orthopedics
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Orthop. Mar 18, 2026; 17(3): 115616 Published online Mar 18, 2026. doi: 10.5312/wjo.v17.i3.115616
Limited and frequently overlooked radiological evidence of knee osteoarthritis
Jia-Yao Zhu, Lei Chen, Ju Li, Tao-Tao Xu
Jia-Yao Zhu, Lei Chen, Ju Li, Tao-Tao Xu, Department of Orthopaedics and Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou 310006, Zhejiang Province, China
Co-first authors: Jia-Yao Zhu and Lei Chen.
Co-corresponding authors: Ju Li and Tao-Tao Xu.
Author contributions: Zhu JY and Chen L wrote the original draft as co-first authors; Li J and Xu TT contributed to conceptualization, writing, reviewing and editing as co-corresponding authors; all authors participated in drafting the manuscript, read and approved the final version of the manuscript.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Tao-Tao Xu, MD, PhD, Attending Physician, Associate Professor, Department of Orthopaedics and Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), No. 54 Youdian Road, Shangcheng District, Hangzhou 310006, Zhejiang Province, China. xut@zcmu.edu.cn
Received: October 21, 2025 Revised: November 10, 2025 Accepted: January 5, 2026 Published online: March 18, 2026 Processing time: 146 Days and 10.2 Hours
Abstract
Knee osteoarthritis (KOA) is a complex degenerative condition involving the entire knee joint structure, with a pathological foundation that extends far beyond the fibrosis, wear, and denudation of articular cartilage. The complete pathological process also includes subchondral bone sclerosis, osteophyte formation, synovitis, and alterations in periarticular soft tissues. However, in routine radiological diagnosis, the evaluation of KOA is often confined to the narrowing of the tibiofemoral joint and overly reliant on the Kellgren-Lawrence grading system as a single standard. This has led to the systematic neglect of assessments for critical compartments such as the patellofemoral joint. In fact, evaluating patellofemoral alignment (e.g., congruence angle) via axial X-rays and observing early cartilage damage and bone marrow lesions via magnetic resonance imaging are essential for a comprehensive diagnosis of KOA. Currently, the limited sensitivity of Kellgren-Lawrence grading for early KOA changes is increasingly recognized. This necessitates a shift in perspective, moving beyond traditional grading to integrate multimodal and multicompartment radiological evidence, including patellofemoral joint assessments and novel functional magnetic resonance imaging techniques. Building on this foundation, future research directions - such as developing deep learning models that incorporate these comprehensive parameters - can be grounded in a solid and accurate pathophysiological understanding, ultimately advancing KOA diagnosis and treatment toward greater precision and personalization.
Core Tip: The prevalence of knee osteoarthritis is rising annually, causing chronic pain, impaired joint function, and reduced quality of life. Accurate diagnosis has thus become crucial. With evolving understanding, clinicians must assess all knee compartments comprehensively. The limitations of single diagnostic tools have driven the development of multimodal approaches. These are enhanced by intelligent network technologies that analyze large clinical datasets, enabling deep learning models to improve diagnostic precision.
