Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

IF 2.9 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

BMC Medical Imaging Pub Date : 2025-05-03 DOI:10.1186/s12880-025-01690-5

Hang Qu, Qiqi Ban, LiangXue Zhou, HaiHan Duan, Wei Wang, AiJun Peng

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引用次数: 0

Abstract

Objective: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. This study aims to develop and evaluate machine learning models using MRI-based radiomics features to differentiate these lesions.

Methods: Two hundred and fifty-eight pathologically diagnosed sellar region lesions, including 54 TSMs, 81 CRs, 61 RCCs and 63 PAs, were retrospectively studied. All patients underwent conventional MR examinations. Feature extraction and data normalization and balance were performed. Extreme gradient boosting (XGBoost), support vector machine (SVM), and logistic regression (LR) models were trained with the radiomics features. Five-fold cross-validation was used to evaluate model performance.

Results: The XGBoost model showed better performance than the SVM and LR models built from contrast-enhanced T1-weighted MRI features (balanced accuracy 0.83, 0.77, 0.75; AUC 0.956, 0.938, 0.929, respectively). Additionally, these models demonstrated significant differences in sensitivity (P = 0.032) and specificity (P = 0.045). The performance of the XGBoost model was superior to that of the SVM and LR models in differentiating sellar region lesions by using contrast-enhanced T1-weighted MRI features.

Conclusion: The proposed model has the potential to improve the diagnostic accuracy in differentiating sellar region lesions.

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基于多分类机器学习模型的鞍区常见病变mri鉴别放射学研究。

目的：垂体腺瘤（PAs）、颅咽管瘤（CRs）、Rathke裂隙囊肿（RCCs）和鞍区结节脑膜瘤（TSMs）是常见的鞍区病变，影像学特征相似，鉴别诊断具有挑战性。本研究旨在开发和评估机器学习模型，使用基于mri的放射组学特征来区分这些病变。方法：回顾性分析经病理诊断的鞍区病变258例，其中TSMs 54例，CRs 81例，rcc 61例，PAs 63例。所有患者均行常规MR检查。进行特征提取、数据归一化和平衡。利用放射组学特征训练极端梯度增强（XGBoost）、支持向量机（SVM）和逻辑回归（LR）模型。采用五重交叉验证评价模型性能。结果：XGBoost模型比基于对比度增强的t1加权MRI特征构建的SVM和LR模型表现出更好的性能(平衡精度0.83,0.77,0.75；AUC分别为0.956、0.938、0.929)。此外，这些模型在敏感性（P = 0.032）和特异性（P = 0.045）方面存在显著差异。XGBoost模型在利用对比增强的t1加权MRI特征鉴别鞍区病变方面优于SVM和LR模型。结论：该模型具有提高鞍区病变诊断准确性的潜力。

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来源期刊

BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.60

自引率

3.70%

发文量

198

审稿时长

27 weeks

期刊介绍： BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.