CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-development benign and malignant PNs (n=106) below the sensitivity of standard-of-care diagnosis. Classifiers predicting malignancy performed near chance when trained on ComBat-harmonized radiomic features from only early-development PNs. We then augmented training with later-development benign and malignant PNs (n=225). We evaluated whether harmonization must incorporate biology that impacts acquisition effects in added training data. To correct variability from four acquisition protocols, we compared: 1) biology-unaware harmonization, 2) harmonizing with a covariate distinguishing early-development, later-development benign, later-development malignant datasets, 3) harmonizing each dataset separately. Models trained using augmentation, but biology-unaware harmonization, failed to improve consistently. Augmented training data harmonized with a covariate (ROC-AUC 0.74 [0.69-0.79]) or separately (ROC-AUC 0.71 [0.66-0.77]) yielded higher test ROC-AUC (Delong, p<=0.05) and PR-AUC (Wilcoxon, p<=0.05). In a proof-of-principle methodological study, we demonstrate with a small single-center dataset that combining radiomic features from later-development benign and malignant PNs requires biology-aware harmonization.

翻译：基于CT影像组学的机器学习有望比标准诊疗方法更早预测肺结节（PNs）的肺癌风险。早期发育肺结节中低恶性率以及可变图像采集参数，阻碍了针对此类结节诊断的影像组学模型开发。为应对这些挑战，我们通过纳入后期发育肺结节增强训练集，并对采集效应进行谐波处理。我们研究了低于标准诊疗敏感度的早期发育良性及恶性肺结节（n=106）。当仅使用早期发育肺结节的ComBat谐波影像组学特征训练时，恶性预测分类器的表现接近随机水平。随后我们通过纳入后期发育良性及恶性肺结节（n=225）增强训练集。我们评估了谐波处理是否必须纳入影响新增训练数据采集效应的生物学因素。为校正四种采集协议引起的变异性，我们比较了：1）生物学非感知谐波处理；2）引入区分早期发育、后期发育良性、后期发育恶性数据集的协变量进行谐波处理；3）分别对各数据集进行谐波处理。采用增强训练集但使用生物学非感知谐波处理的模型未能持续提升性能。经协变量谐波处理（ROC-AUC 0.74 [0.69-0.79]）或分别谐波处理（ROC-AUC 0.71 [0.66-0.77]）的增强训练数据，获得了更高的测试集ROC-AUC（Delong检验，p≤0.05）和PR-AUC（Wilcoxon检验，p≤0.05）。在这项原理验证方法学研究中，我们通过小规模单中心数据集证明：整合来自后期发育良性及恶性肺结节的影像组学特征时，需要采用生物学感知谐波处理。