Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.
Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, No. 2, Sec.1, Shengyi Rd., Zhubei City, Hsinchu County, 302, Taiwan.
Eur Radiol. 2021 Jul;31(7):5127-5138. doi: 10.1007/s00330-020-07570-6. Epub 2021 Jan 3.
Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values.
Overall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the "near-pure" pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction.
In cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the "near-pure"-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness.
Using near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting.
• The radiomic values extracted from lung adenocarcinoma with "near-pure" histological subtypes provide useful information for high-grade (micropapillary and solid) components detection. • Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity. • Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection.
近纯肺腺癌（ADC）亚型表现出较强的放射组学值分层，为病理亚型提供了基本信息。我们试图使用定量图像分析来预测肺 ADC 中高级别（微乳头状和实性）成分的存在，这些分析基于近纯放射组学值。
共有 103 名不同组织学亚型的肺 ADC 患者纳入 10 次重复、3 倍交叉验证（队列 1）；55 名患者纳入测试（队列 2）。根据“近纯”病理亚型数据评估 CT 图像上的直方图和纹理特征。对肿瘤区域内的每个体素进行高等级可能性的斑块预测。然后根据高级别可能性区域的体积百分比阈值确定高级别成分的存在。为了与定量方法进行比较，评估 CT 图像上的实变/肿瘤（C/T）比；我们应用放射侵袭性（C/T 比>0.5）进行预测。
在队列 1 中，斑块预测、组合模型（C/T 比和斑块预测）、基于全病变的预测（仅使用“近纯”预测模型）和放射侵袭性的敏感性和特异性分别为 88.00±2.33%和 75.75±2.82%、90.00±0.00%和 77.12±2.67%、66.67%和 90.41%、90.00%和 45.21%。队列 2 的敏感性和特异性分别为 100.0%和 95.35%，使用斑块预测；100.0%和 95.35%，使用组合模型；75.00%和 95.35%，使用基于全病变的预测；100.0%和 69.77%，使用放射侵袭性。
使用近纯放射组学特征和斑块图像分析可高度敏感且中等特异性地检测高级别 ADC 亚型。
从具有“近纯”组织学亚型的肺腺癌中提取的放射组学值为检测高级别（微乳头状和实性）成分提供了有用信息。
使用近纯放射组学特征和斑块图像分析，可以高度敏感且中等特异性地预测肺腺癌的高级别成分。
使用近纯放射组学特征和斑块图像分析在肺腺癌术前预测高级别成分的存在方面具有潜在作用。
