Evidence map›Paper›PMID 38440779›Full record

ArticleOsteoarthritis and cartilage open2024

Radiomics analysis of patellofemoral joint improves knee replacement risk prediction: Data from the Multicenter Osteoarthritis Study (MOST).

Jiang Zhang, Tianshu Jiang, Lok-Chun Chan, Sing-Hin Lau, Wei Wang, Xinzhi Teng, Ping-Keung Chan, Jing Cai, Chunyi Wen

Open access · goldAbstract read
In one paragraph

Article in Osteoarthritis and cartilage open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Jiang ZhangDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Tianshu JiangDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Lok-Chun ChanDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Sing-Hin LauDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Wei WangDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Xinzhi TengDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Ping-Keung ChanDepartment of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China.
Jing CaiDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Chunyi WenDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Hong Kong Polytechnic University · HKUniversity of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Knee replacement (KR) is the last-resort treatment for knee osteoarthritis. Although radiographic evidence of tibiofemoral joint has been widely adopted for prognostication, patellofemoral joint has gained little attention and may hold additional value for further improvements. We aimed to quantitatively analyse patellofemoral joint through radiomics analysis of lateral view radiographs for improved KR risk prediction. Design: From the Multicenter Osteoarthritis Study dataset, we retrospectively retrieved the initial-visit lateral left knee radiographs of 2943 patients aged 50 to 79. They were split into training and test cohorts at a 2:1 ratio. A comprehensive set of radiomic features were extracted within the best-performing subregion of patellofemoral joint and combined into a radiomics score (RadScore). A KR risk score, derived from Kellgren-Lawrence grade (KLG) of tibiofemoral joint and RadScore of patellofemoral joint, was developed by multivariate Cox regression and assessed using time-dependent area under receiver operating characteristic curve (AUC). Results: While patellofemoral osteoarthritis (PFOA) was insignificant during multivariate analysis, RadScore was identified as an independent risk factor (multivariate Cox p-value < 0.001) for KR. The subgroup analysis revealed that RadScore was particularly effective in predicting rapid progressor (KR occurrence before 30 months) among early- (KLG < 2) and mid-stage (KLG ​= ​2) patients. Combining two joints radiographic information, the AUC reached 0.89/0.87 for predicting 60-month KR occurrence. Conclusions: The RadScore of the patellofemoral joint on lateral radiographs emerges as an independent prognostic factor for improving KR prognosis prediction. The KR risk score could be instrumental in managing progressive knee osteoarthritis interventions.

Indexed as

Knee replacementLateral knee radiographOsteoarthritisPatellofemoral jointRadiomics

Identifiers

PMID38440779
PMCPMC10910336
OpenAlexW4392130125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.