Work

Radiograph‐Based Radiomics Successfully Classify MRI‐Based Identification of Femoral Loosening in Patients With Total Hip Arthroplasty—A Preliminary Study

Year: 2025

Type: article

Abstract: ABSTRACT Aseptic loosening is a leading cause of total hip arthroplasty (THA) revision. While three dimensional (3D) multi‐spectral magnetic resonance imaging (MSI‐MRI) offers high sensitivity for detecting femoral stem loosening, it is less commonly acquired at routine follow‐up. This study investigates whether radiograph‐based radiomics can detect loosening confirmed by MSI‐MRI, improving routine evaluation of implant integration. In this cohort study, 3D MSI‐MRI acquisitions and standard AP pelvis radiographs were obtained from 38 symptomatic subjects with confirmed femoral component loosening and 40 asymptomatic subjects. Loosening was identified through qualitative evaluation of MSI‐MRI. A 2.5 mm periprosthetic region was segmented from radiographs and divided into six Gruen zones. Radiographs were subdivided into overlapping patches (10 × 10 to 150 × 150 mm 2 ) and a total of 96 radiomic features were extracted per patch. Features were standardized and reduced using principal component analysis. Logistic regression with L2 regularization was trained using five‐fold cross‐validation to classify symptomatic versus asymptomatic cases. Sixty‐four models were trained across zones and patch sizes. Gruen zones 3 (distal, lateral) and 7 (proximal, medial) achieved the highest performance (AUC = 0.76). The full periprosthetic model reached an AUC of 0.74 at 130 × 130 mm 2 . Fewer principal components were required as patch size increased. Radiograph‐based radiomics can detect MSI‐MRI confirmed femoral component loosening in subjects with THA with high accuracy. As radiographs are routinely acquired in clinical practice, this method offers a scalable, low‐cost adjunct to standard post‐operative evaluation.

Source: Journal of Orthopaedic Research®

DOI: 10.1002/jor.70120

Authors: Jack Consolini, Elizabeth G. Koretsky, Kevin M. Koch, Hollis G. Potter, Matthew F. Koff

Institution: Hospital for Special Surgery

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health