Automatic grading of knee osteoarthritis with a plain radiograph radiomics model: combining anteroposterior and lateral images

Autor: Wei Li, Jin Liu, Zhongli Xiao, Dantian Zhu, Jianwei Liao, Wenjun Yu, Jiaxin Feng, Baoxin Qian, Yijie Fang, Shaolin Li
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Insights into Imaging, Vol 15, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1869-4101
DOI: 10.1186/s13244-024-01719-3
Popis: Abstract Objectives To establish a radiomics-based automatic grading model for knee osteoarthritis (OA) and evaluate the influence of different body positions on the model’s effectiveness. Materials and methods Plain radiographs of a total of 473 pairs of knee joints from 473 patients (May 2020 to July 2021) were retrospectively analyzed. Each knee joint included anteroposterior (AP) and lateral (LAT) images which were randomly assigned to the training cohort and the testing cohort at a ratio of 7:3. First, an assessment of knee OA severity was done by two independent radiologists with Kallgren–Lawrence grading scale. Then, another two radiologists independently delineated the region of interest for radiomic feature extraction and selection. The radiomic classification features were dimensionally reduced and a machine model was conducted using logistic regression (LR). Finally, the classification efficiency of the model was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Results The AUC (macro/micro) of the model using a combination of AP and LAT (AP&LAT) images were 0.772/0.778, 0.818/0.799, and 0.864/0.879, respectively. The radiomic features from the combined images achieved better classification performance than the individual position image (p
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje