基于多参数 MRI 的 2.5D 深度学习对脑转移患者原发性肺癌病理亚型的鉴别诊断。
2.5D deep learning based on multi-parameter MRI to differentiate primary lung cancer pathological subtypes in patients with brain metastases.
机构信息
Department Of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China.
Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China.
出版信息
Eur J Radiol. 2024 Nov;180:111712. doi: 10.1016/j.ejrad.2024.111712. Epub 2024 Aug 28.
BACKGROUND
Brain metastases (BMs) represents a severe neurological complication stemming from cancers originating from various sources. It is a highly challenging clinical task to accurately distinguish the pathological subtypes of brain metastatic tumors from lung cancer (LC).The utility of 2.5-dimensional (2.5D) deep learning (DL) in distinguishing pathological subtypes of LC with BMs is yet to be determined.
METHODS
A total of 250 patients were included in this retrospective study, divided in a 7:3 ratio into training set (N=175) and testing set (N=75). We devised a method to assemble a series of two-dimensional (2D) images by extracting adjacent slices from a central slice in both superior-inferior and anterior-posterior directions to form a 2.5D dataset. Multi-Instance learning (MIL) is a weakly supervised learning method that organizes training instances into "bags" and provides labels for entire bags, with the purpose of learning a classifier based on the labeled positive and negative bags to predict the corresponding class for an unknown bag. Therefore, we employed MIL to construct a comprehensive 2.5D feature set. Then we used the single-slice as input for constructing the 2D model. DL features were extracted from these slices using the pre-trained ResNet101. All feature sets were inputted into the support vector machine (SVM) for evaluation. The diagnostic performance of the classification models were evaluated using five-fold cross-validation, with accuracy and area under the curve (AUC) metrics calculated for analysis.
RESULTS
The optimal performance was obtained using the 2.5D DL model, which achieved the micro-AUC of 0.868 (95% confidence interval [CI], 0.817-0.919) and accuracy of 0.836 in the test cohort. The 2D model achieved the micro-AUC of 0.836 (95 % CI, 0.778-0.894) and accuracy of 0.827 in the test cohort.
CONCLUSIONS
The proposed 2.5D DL model is feasible and effective in identifying pathological subtypes of BMs from lung cancer.
背景
脑转移瘤（BMs）是一种严重的神经系统并发症，源自各种来源的癌症。准确区分肺癌（LC）脑转移瘤的病理亚型是一项极具挑战性的临床任务。2.5 维（2.5D）深度学习（DL）在区分 LC 伴 BMs 的病理亚型中的应用尚待确定。
方法
本回顾性研究共纳入 250 例患者，按 7：3 的比例分为训练集（N=175）和测试集（N=75）。我们设计了一种方法，通过从前向后和从上到下从中心切片提取相邻切片来组装一系列 2D 图像，形成 2.5D 数据集。多实例学习（MIL）是一种弱监督学习方法，它将训练实例组织成“袋子”，并为整个袋子提供标签，目的是基于标记的正袋和负袋学习分类器，以预测未知袋的对应类别。因此，我们采用 MIL 构建全面的 2.5D 特征集。然后我们使用单张切片作为输入来构建 2D 模型。使用预先训练的 ResNet101 从这些切片中提取 DL 特征。将所有特征集输入支持向量机（SVM）进行评估。使用五重交叉验证评估分类模型的诊断性能，计算准确率和曲线下面积（AUC）指标进行分析。
结果
在测试队列中，2.5D DL 模型的性能最佳，获得了 0.868 的微 AUC（95%置信区间[CI]，0.817-0.919）和 0.836 的准确率。2D 模型在测试队列中获得了 0.836 的微 AUC（95% CI，0.778-0.894）和 0.827 的准确率。
结论
所提出的 2.5D DL 模型在识别肺癌脑转移瘤的病理亚型方面是可行且有效的。
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