Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study

Lin, Ting; Peng, Shengwang; Lu, Shi‐Long; Fu, Shuai; Li, Jia; Chen, Tianyu; Fan, Tianxiang; Lang, C. Max; Feng, Siyuan; Ma, Jianhua; Zhao, Chen; Antony, Benny; Cicuttini, Flavia Maria; Quan, Xianyue; Zhu, Zhaohua; Ding, Changhai

doi:10.1016/j.joca.2022.10.014

Cited by 12 publications

(7 citation statements)

References 51 publications

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“…Several studies [ 33 – 35 ] utilized radiomic features from T2-weighted images to achieve early diagnosis of osteoarthritis. Lin et al [ 36 ] developed a radiomic and clinical features-based model to predict the prognosis of osteoarthritis, with an AUC of 0.83 (95% CI = 0.70–0.96) in the validation set. Recent studies [ 37 – 45 ] also demonstrated the ability of radiomics in osteoporosis detection and fracture prediction.…”

Section: Discussionmentioning

confidence: 99%

Determining rib fracture age from CT scans with a radiomics-based combined model: a multicenter retrospective study

Tang,

Jin,

et al. 2023

Insights Imaging

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Objectives We aimed to develop a combined model based on clinical and radiomic features to classify fracture age. Methods We included 1219 rib fractures from 239 patients from our center between March 2016 and September 2022. We created an external dataset using 120 rib fractures from 32 patients from another center between October 2019 and August 2023. According to tasks (fracture age between < 3 and ≥ 3 weeks, 3–12, and > 12 weeks), the internal dataset was randomly divided into training and internal test sets. A radiomic model was built using radiomic features. A combined model was constructed using clinical features and radiomic signatures by multivariate logistic regression, visualized as a nomogram. Internal and external test sets were used to validate model performance. Results For classifying fracture age between < 3 and ≥ 3 weeks, the combined model had higher areas under the curve (AUCs) than the radiomic model in the training set (0.915 vs 0.900, p = 0.009), internal test (0.897 vs 0.854, p < 0.001), and external test sets (0.881 vs 0.811, p = 0.003). For classifying fracture age between 3–12 and > 12 weeks, the combined model had higher AUCs than the radiomic model in the training model (0.848 vs 0.837, p = 0.12) and internal test sets (0.818 vs 0.793, p < 0.003). In the external test set, the AUC of the nomogram-assisted radiologist was 0.966. Conclusion The combined radiomic and clinical model showed good performance and has the potential to assist in the classification of rib fracture age. This will be beneficial for clinical practice and forensic decision-making. Critical relevance statement This study describes the development of a combined radiomic and clinical model with good performance in the classification of the age of rib fractures, with potential clinical and forensic applications. Key points • Complex factors make it difficult to determine the age of a fracture. • Our model based on radiomic features performed well in classifying fracture age. • Associating the radiomic features with clinical features improved the model’s performance. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Determining rib fracture age from CT scans with a radiomics-based combined model: a multicenter retrospective study

Tang,

Jin,

et al. 2023

Insights Imaging

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“…The 5942 LUAD patients included were randomly divided into a training group and an internal validation group, including 4754 and 1188 samples (the split ratio was 8:2), respectively. For the building of nomogram, the split ration of clinical cohort was usually 7:3 or 8:2 [ [62] , [63] , [64] ]. Random grouping, data inclusion and exclusion was performed by Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA).The nomogram model was built and tested as follow steps: 1) in the training cohort, Kaplan–Meier curves and univariate Cox risk regression were performed to determine factors for multivariate regression analysis by SPSS 25.0; 2) in the training cohort, factors with a p < 0.05 in univariate analysis were further analyzed in a multivariate regression analysis by SPSS 25.0; 3) in the training cohort, independent prognostic factors (p < 0.05) were used to construct the nomogram and predict the 3- and 5-year OS by R4.1.3 (R Foundation for Statistical Computing, Vienna, Austria).…”

Section: Methodsmentioning

confidence: 99%

Survival analysis and clinicopathological features of patients with stage IA lung adenocarcinoma

Li,

Wang,

Liu

et al. 2024

Heliyon

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“…Following the removal of 244 duplicates, the remaining 1027 studies were subject to our exclusion criteria. Subsequently, the application of our inclusion criteria to the remaining resulted in 5 studies (1730 patients) being included in total [15][16][17][18][19]. An additional study of 1174 patients was noted to perform radiomic analysis in predicting potential knee OA using plain radiographs, however was excluded on the basis of our aforementioned inclusion criteria.…”

Section: Literature Searchmentioning

confidence: 99%

“…All included studies represented level III evidence, with a mean RQS of 16.6 (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). Additionally, 1 study analysed radiomic features of the infra-patellar fat pad (IPFP) on MRI, whilst 4 studies evaluated the tibiofemoral (TF) joint.…”

Section: Study Characteristics Patient Demographics and Site Of Analysismentioning

confidence: 99%

The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee—a systematic review and meta-analysis

Davey,

Kenny

et al. 2024

Ir J Med Sci

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The primary aim of this study was to systematically review current literature evaluating the use of radiomics in establishing the role of magnetic resonance imaging (MRI) findings in native knees in predicting features of osteoarthritis (OA). A systematic review was performed with respect to PRISMA guidelines in search of studies reporting radiomic analysis of magnetic resonance imaging (MRI) to analyse patients with native knee OA. Sensitivity and specificity of radiomic analyses were included for meta-analysis. Following our initial literature search of 1271 studies, only 5 studies met our inclusion criteria. This included 1730 patients (71.5% females) with a mean age of 55.4 ± 15.6 years (range 24–66). The mean RQS of included studies was 16.6 (11–21). Meta-analysis demonstrated the pooled sensitivity and specificity for MRI in predicting features of OA in patients with native knees were 0.74 (95% CI 0.71, 0.78) and 0.85 (95% CI 0.83, 0.87), respectively. The results of this systematic review suggest that the high sensitivities and specificity of MRI-based radiomics may represent potential biomarker in the early identification and classification of native knee OA. Such analysis may inform surgeons to facilitate earlier non-operative management of knee OA in the select pre-symptomatic patients, prior to clinical or radiological evidence of degenerative change.

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Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study

Cited by 12 publications

References 51 publications

Determining rib fracture age from CT scans with a radiomics-based combined model: a multicenter retrospective study

Determining rib fracture age from CT scans with a radiomics-based combined model: a multicenter retrospective study

Survival analysis and clinicopathological features of patients with stage IA lung adenocarcinoma

The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee—a systematic review and meta-analysis

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