基于 X 射线放射组学的机器学习对长骨非典型软骨肿瘤和高级别软骨肉瘤的分类。

X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.

机构信息

IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.

Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy.

出版信息

EBioMedicine. 2024 Mar;101:105018. doi: 10.1016/j.ebiom.2024.105018. Epub 2024 Feb 19.

DOI:10.1016/j.ebiom.2024.105018

PMID:38377797

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10884340/

Abstract

BACKGROUND

Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones.

METHODS

This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test.

FINDINGS

Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617).

INTERPRETATION

X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones.

FUNDING

AIRC Investigator Grant.

摘要

背景

非典型性软骨肿瘤（ACT）和高级别软骨肉瘤（CS）是长骨中两种不同的疾病，分别采用主动监测或刮除和广泛切除的方法进行治疗。我们的目的是确定基于 X 射线放射组学的机器学习在长骨 ACT 和高级别 CS 分类中的诊断性能。

方法

这是一项回顾性的、经机构审查委员会批准的研究，纳入了在两个三级骨肉瘤中心接受手术治疗和组织学证实的病变的 150 名患者。在中心 1 中，数据集根据手术日期分为训练集（n=71 例 ACT，n=24 例高级别 CS）和内部测试集（n=19 例 ACT，n=6 例高级别 CS）。在中心 2 中，数据集构成了外部测试集（n=12 例 ACT，n=18 例高级别 CS）。在正位 X 射线上手动进行病变分割，使用 MRI 或 CT 对病变边界进行初步识别。在图像预处理后，提取放射组学特征。降维分析包括稳定性、变异系数和互信息分析。在训练集中，在平衡类后，使用嵌套的 10 折交叉验证自动调整机器学习分类器（支持向量机）。然后，在两个测试集上进行测试，并与两位肌肉骨骼放射科医生的表现进行比较，使用 McNemar 检验。

结果

通过降维分析，有 5 个放射组学特征（3 个形态学特征，2 个纹理特征）通过了降维分析。在训练集上进行调整后（AUC=0.75），分类器在内部（时间独立）和外部（地理独立）测试集中的准确率、敏感度和特异性分别为 80%、83%、79%和 80%、89%、67%，与放射科医生的表现无差异（p≥0.617）。