Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taiwan; Department of Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Taiwan.
Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taiwan.
Lung Cancer. 2018 May;119:56-63. doi: 10.1016/j.lungcan.2018.03.004. Epub 2018 Mar 7.
Histological subtypes of lung adenocarcinomas (ADCs) classified by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) system have been investigated using radiomic approaches. However, the results have had limitations since <80% of invasive lung ADCs were heterogeneous, with two or more subtypes. To reduce the influence of heterogeneity during radiomic analysis, computed tomography (CT) images of lung ADCs with near-pure ADC subtypes were analyzed to extract representative radiomic features of different subtypes.
We enrolled 95 patients who underwent complete resection for lung ADC and a pathological diagnosis of a "near-pure" (≥70%) IASLC/ATS/ERS histological subtype. Conventional histogram/morphological features and complex radiomic features (grey-level-based statistical features and component variance-based features) of thin-cut CT data of tumor regions were analyzed. A prediction model based on leave-one-out cross-validation (LOOCV) and logistic regression (LR) was used to classify all five subtypes and three pathologic grades (lepidic, acinar/papillary, micropapillary/solid) of ADCs. The validation was performed using 36 near-pure ADCs in a later cohort.
A total of 31 lepidic, 14 papillary, 32 acinar, 10 micropapillary, and 8 solid ADCs were analyzed. With 21 conventional and complex radiomic features, for 5 subtypes and 3 pathological grades, the prediction models achieved accuracy rates of 84.2% (80/95) and 91.6% (87/95), respectively, while accuracy was 71.6% and 85.3%, respectively, if only conventional features were used. The accuracy rate for the validation set (n = 36) was 83.3% (30/36) and 94.4% (34/36) in 5 subtypes and 3 pathological grades, respectively, using conventional and complex features, while it was 66.7% and 77.8% only using conventional features, respectively.
Lung ADC with high purity pathological subtypes demonstrates strong stratification of radiomic values, which provide basic information for accurate pathological subtyping and image parcellation of tumor sub-regions.
国际肺癌研究协会/美国胸科学会/欧洲呼吸学会（IASLC/ATS/ERS）系统分类的肺腺癌（ADC）组织学亚型已通过放射组学方法进行了研究。然而，由于<80%的侵袭性肺 ADC 存在异质性，存在两种或更多种亚型，因此结果存在局限性。为了减少放射组学分析过程中的异质性影响，对具有近纯 ADC 亚型的肺 ADC 的 CT 图像进行了分析，以提取不同亚型的代表性放射组学特征。
我们纳入了 95 名接受完全切除术治疗的肺 ADC 患者，且经病理诊断为“近纯”（≥70%）IASLC/ATS/ERS 组织学亚型。分析了肿瘤区域薄层 CT 数据的常规直方图/形态学特征和复杂放射组学特征（基于灰度的统计特征和基于分量方差的特征）。使用留一法交叉验证（LOOCV）和逻辑回归（LR）的预测模型对所有 5 种亚型和 ADC 的 3 种病理分级（贴壁型、腺泡/乳头型、微乳头/实体型）进行分类。在随后的队列中，使用 36 例近纯 ADC 对其进行了验证。
共分析了 31 例贴壁型、14 例乳头型、32 例腺泡型、10 例微乳头/实体型和 8 例实体型 ADC。使用 21 个常规和复杂放射组学特征，对于 5 种亚型和 3 种病理分级，预测模型的准确率分别为 84.2%（80/95）和 91.6%（87/95），而仅使用常规特征时，准确率分别为 71.6%和 85.3%。在 5 种亚型和 3 种病理分级中，验证集（n=36）的准确率分别为 83.3%（30/36）和 94.4%（34/36），分别使用常规和复杂特征，而仅使用常规特征时，准确率分别为 66.7%和 77.8%。
高纯度病理亚型的肺 ADC 具有较强的放射组学值分层，为准确的病理亚型和肿瘤亚区的图像分割提供了基本信息。
