{"title":"Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones.","authors":"Rui Hong, Qian Li, Jielin Ma, Chunmiao Lu, Zhiwei Zhong","doi":"10.1055/a-2344-5398","DOIUrl":null,"url":null,"abstract":"<p><p>To explore the value of CT-based radiomics machine learning models for differentiating enchondroma from atypical cartilaginous tumor (ACT) in long bones and methods to improve model performance.59 enchondromas and 53 ACTs in long bones confirmed by pathology were collected retrospectively. The features were extracted from preoperative CT images of these patients, and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and dimensionality reduction. The selected features were used to construct classification models by thirteen machine learning algorithms. The data set was randomly divided into a training set and a test set at a proportion of 7:3 by ten-fold cross-validation to evaluate the performance of these models.A total of 1199 features were extracted, 9 features were selected, and 13 radiomics machine learning models were constructed. The area under the curve (AUC) of 11 models was more than 0.8, and that of 3 models was more than 0.9. The Extremely Randomized Trees model achieved the best performance (AUC = 0.9375 ± 0.0884), followed by the Adaptive Boosting model (AUC = 0.9188 ± 0.1010) and the Linear Discriminant Analysis model (AUC = 0.9062 ± 0.1459).CT-based radiomics machine learning models had great ability to distinguish enchondroma and ACT in long bones. By using filters to deeply mine high-order features in the original image and selecting appropriate machine learning algorithms, the performance of the model can be improved. · CT-based radiomics machine learning models can distinguish enchondroma and ACT in long bones.. · Using filters and selecting advanced machine learning algorithms can improve model performance.. · Clinical features have limited utility in distinguishing enchondroma and ACT in long bones.. · Hong R, Li Q, Ma J et al. Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones. Fortschr Röntgenstr 2025; 197: 416-423.</p>","PeriodicalId":21490,"journal":{"name":"Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren","volume":" ","pages":"416-423"},"PeriodicalIF":1.3000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-2344-5398","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/29 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
To explore the value of CT-based radiomics machine learning models for differentiating enchondroma from atypical cartilaginous tumor (ACT) in long bones and methods to improve model performance.59 enchondromas and 53 ACTs in long bones confirmed by pathology were collected retrospectively. The features were extracted from preoperative CT images of these patients, and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and dimensionality reduction. The selected features were used to construct classification models by thirteen machine learning algorithms. The data set was randomly divided into a training set and a test set at a proportion of 7:3 by ten-fold cross-validation to evaluate the performance of these models.A total of 1199 features were extracted, 9 features were selected, and 13 radiomics machine learning models were constructed. The area under the curve (AUC) of 11 models was more than 0.8, and that of 3 models was more than 0.9. The Extremely Randomized Trees model achieved the best performance (AUC = 0.9375 ± 0.0884), followed by the Adaptive Boosting model (AUC = 0.9188 ± 0.1010) and the Linear Discriminant Analysis model (AUC = 0.9062 ± 0.1459).CT-based radiomics machine learning models had great ability to distinguish enchondroma and ACT in long bones. By using filters to deeply mine high-order features in the original image and selecting appropriate machine learning algorithms, the performance of the model can be improved. · CT-based radiomics machine learning models can distinguish enchondroma and ACT in long bones.. · Using filters and selecting advanced machine learning algorithms can improve model performance.. · Clinical features have limited utility in distinguishing enchondroma and ACT in long bones.. · Hong R, Li Q, Ma J et al. Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones. Fortschr Röntgenstr 2025; 197: 416-423.
为了探索基于CT的放射组学机器学习模型在区分长骨中的软骨瘤和非典型软骨瘤(ACT)方面的价值以及提高模型性能的方法,我们回顾性地收集了59例经病理证实的长骨中的软骨瘤和53例ACT。从这些患者的术前 CT 图像中提取特征,并使用最小绝对收缩和选择算子(LASSO)回归进行特征选择和降维。所选特征通过 13 种机器学习算法构建分类模型。数据集以 7:3 的比例随机分为训练集和测试集,并进行十倍交叉验证,以评估这些模型的性能。11 个模型的曲线下面积(AUC)大于 0.8,3 个模型的曲线下面积大于 0.9。基于CT的放射组学机器学习模型具有很强的区分长骨软骨瘤和ACT的能力。通过使用滤波器深度挖掘原始图像中的高阶特征并选择适当的机器学习算法,可以提高模型的性能。- 基于CT的放射组学机器学习模型可以区分长骨中的软骨瘤和ACT。- 使用滤波器和选择先进的机器学习算法可提高模型性能- 临床特征在区分长骨软骨瘤和ACT方面作用有限- Hong R, Li Q, Ma J et al.基于计算机断层扫描的放射组学机器学习模型用于区分长骨中的软骨瘤和非典型软骨瘤Fortschr Röntgenstr 2024; DOI 10.1055/a-2344-5398.
期刊介绍:
Die RöFo veröffentlicht Originalarbeiten, Übersichtsartikel und Fallberichte aus dem Bereich der Radiologie und den weiteren bildgebenden Verfahren in der Medizin. Es dürfen nur Arbeiten eingereicht werden, die noch nicht veröffentlicht sind und die auch nicht gleichzeitig einer anderen Zeitschrift zur Veröffentlichung angeboten wurden. Alle eingereichten Beiträge unterliegen einer sorgfältigen fachlichen Begutachtung.
Gegründet 1896 – nur knapp 1 Jahr nach der Entdeckung der Röntgenstrahlen durch C.W. Röntgen – blickt die RöFo auf über 100 Jahre Erfahrung als wichtigstes Publikationsmedium in der deutschsprachigen Radiologie zurück. Sie ist damit die älteste radiologische Fachzeitschrift und schafft es erfolgreich, lange Kontinuität mit dem Anspruch an wissenschaftliches Publizieren auf internationalem Niveau zu verbinden. Durch ihren zentralen Platz im Verlagsprogramm stellte die RöFo die Basis für das heute umfassende und erfolgreiche Radiologie-Medienangebot im Georg Thieme Verlag.
Besonders eng verbunden ist die RöFo mit der Geschichte der Röntgengesellschaften in Deutschland und Österreich. Sie ist offizielles Organ von DRG und ÖRG und die Mitglieder der Fachgesellschaften erhalten die Zeitschrift im Rahmen ihrer Mitgliedschaft. Mit ihrem wissenschaftlichen Kernteil und dem eigenen Mitteilungsteil der Fachgesellschaften bietet die RöFo Monat für Monat ein Forum für den Austausch von Inhalten und Botschaften der radiologischen Community im deutschsprachigen Raum.