基于 MRI 影像组学的机器学习对长骨非典型性软骨肿瘤和 II 级软骨肉瘤的分类。

MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones.

机构信息

Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy; Radiology Department, Leiden University Medical Center, Leiden, The Netherlands.

Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy.

出版信息

EBioMedicine. 2022 Jan;75:103757. doi: 10.1016/j.ebiom.2021.103757. Epub 2021 Dec 18.

DOI:10.1016/j.ebiom.2021.103757

PMID:34933178

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8688587/

Abstract

BACKGROUND

Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones.

METHODS

One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test.

FINDINGS

After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134).

INTERPRETATION

Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features.

FUNDING

ESSR Young Researchers Grant.

摘要

背景

长骨的非典型软骨肿瘤（ACT）和 II 级软骨肉瘤（CS2）分别采用观察等待或刮除和广泛切除治疗。由于观察者间的变异性和活检存在样本误差，术前影像学诊断具有挑战性。本研究旨在确定基于 MRI 放射组学的机器学习在区分长骨的 ACT 和 CS2 方面的诊断性能。

方法

回顾性纳入在两个三级骨肿瘤中心接受手术治疗和组织学证实的软骨骨肿瘤的 158 名患者。训练队列由来自中心 1 的 93 例 MRI 扫描组成（n=74 例 ACT；n=19 例 CS2）。外部测试队列由来自中心 2 的 65 例 MRI 扫描组成（n=45 例 ACT；n=20 例 CS2）。在 T1 加权 MRI 上进行二维分割。提取放射组学特征。在中心 1 中进行维度降低和类别平衡后，使用 10 倍交叉验证在训练队列上调整机器学习分类器（Extra Trees Classifier），并在外部测试队列上进行测试。在中心 2 中，将其性能与经验丰富的肌肉骨骼肿瘤放射科医生使用 McNemar 检验进行比较。

发现

在训练队列上进行调整后（AUC=0.88），机器学习分类器在外部测试队列中识别病变的准确率为 92%（60/65，AUC=0.94）。其正确分类 ACT 和 CS2 的准确率分别为 98%（44/45）和 80%（16/20）。放射科医生的准确率为 98%（64/65），与分类器无差异（p=0.134）。