评估三维CT肺结节恶性程度评估中的多任务形态学概念学习
Evaluating Multi-Task Morphological Concept Learning for Pulmonary Nodule Malignancy Assessment in 3D CT
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中文总结 AI 辅助
本研究评估多任务学习是否提升三维CT肺结节恶性风险评估，对比单任务与多任务模型，发现形态学特征未明显改善性能，强调类别平衡与标注结构的重要性。
中文摘要 AI 辅助
形态学特征（如毛刺征和分叶征）在计算机断层扫描（CT）上评估肺结节时发挥重要作用，尤其与恶性风险相关。本研究探讨了从病灶中心的三维CT体积中同时学习放射科医生标注的形态学特征与恶性风险是否能够提升分类性能。使用了肺部图像数据库联盟和图像数据库资源倡议（LIDC-IDRI）数据集，在排除不确定恶性评级后，包含来自742名患者的3,918条阅片者级别结节标注。采用患者级别划分进行训练、验证和测试，其中留出测试集包含112名患者和628条阅片者标注。将单任务三维卷积神经网络与预测恶性风险、毛刺征和分叶征的多任务模型进行了比较。单任务模型实现了0.548的平衡准确率和0.552的受试者工作特征曲线下面积（ROC-AUC），而多任务模型分别实现了0.539和0.558。患者级别自助法分析显示ROC-AUC差异为0.005（95%置信区间（CI）：-0.087至0.090），平衡准确率差异为-0.009（95% CI：-0.067至0.043）。辅助任务存在严重不平衡且预测性能有限。总体而言，包含形态学特征并未明显改善恶性风险分类，显示了类别平衡、标签制定和阅片者级别标注结构在多任务肺部CT分析中的重要性。
英文摘要
Morphological characteristics such as spiculation and lobulation play an important role in assessing pulmonary nodules on computed tomography (CT), particularly in relation to malignancy risk. This study examines whether learning radiologist-annotated morphological features together with malignancy risk from lesion-centred 3D CT volumes improves classification performance. The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset was used, comprising 3,918 reader-level nodule annotations from 742 patients after excluding indeterminate malignancy ratings. Patient-level splitting was used for training, validation, and testing, with 112 patients and 628 reader annotations in the held-out test set. A single-task 3D convolutional neural network was compared with a multi-task model predicting malignancy risk, spiculation, and lobulation. The single-task model achieved a balanced accuracy of 0.548 and receiver operating characteristic area under the curve (ROC-AUC) of 0.552, while the multi-task model achieved 0.539 and 0.558, respectively. Patient-level bootstrap analysis showed an ROC-AUC difference of 0.005 (95% confidence interval (CI): -0.087 to 0.090) and a balanced-accuracy difference of -0.009 (95% CI: -0.067 to 0.043). The auxiliary tasks were strongly imbalanced and showed limited predictive performance. Overall, including morphological features did not clearly improve malignancy-risk classification, showing the importance of class balance, label formulation, and reader-level annotation structure in multi-task pulmonary CT analysis.
