Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-dong, Bukgu, 61005, Republic of Korea.
Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-dong, Bukgu, 61005, Republic of Korea.
Comput Med Imaging Graph. 2018 Jul;67:1-8. doi: 10.1016/j.compmedimag.2018.04.003. Epub 2018 Apr 4.
The histological classification or subtyping of non-small cell lung cancer is essential for systematic therapy decisions. Differentiating between the two main subtypes of pulmonary adenocarcinoma and squamous cell carcinoma highlights the considerable differences that exist in the prognosis of patient outcomes. Physicians rely on a pathological analysis to reveal these phenotypic variations that requires invasive methods, such as biopsy and resection sample, but almost 70% of tumors are unresectable at the point of diagnosis.
A computational method that fuses two frameworks of computerized subtyping and prognosis was proposed, and it was validated against publicly available dataset in The Cancer Imaging Archive that consisted of 82 curated patients with CT scans. The accuracy of the proposed method was compared with the gold standard of pathological analysis, as defined by theInternational Classification of Disease for Oncology (ICD-O). A series of survival outcome test cases were evaluated using the Kaplan-Meier estimator and log-rank test (p-value) between the computational method and ICD-O.
The computational method demonstrated high accuracy in subtyping (96.2%) and good consistency in the statistical significance of overall survival prediction for adenocarcinoma and squamous cell carcinoma patients (p < 0.03) with respect to its counterpart pathological subtyping (p < 0.02). The degree of reproducibility between prognosis taken on computational and pathological subtyping was substantial with an averaged concordance correlation coefficient (CCC) of 0.9910.
The findings in this study support the idea that quantitative analysis is capable of representing tissue characteristics, as offered by a qualitative analysis.
非小细胞肺癌的组织学分类或亚型分类对于系统治疗决策至关重要。区分肺腺癌和鳞状细胞癌这两种主要亚型突出了患者预后存在的显著差异。医生依靠病理分析来揭示这些表型变化，这需要采用侵入性方法，如活检和切除样本，但在诊断时，几乎 70%的肿瘤是不可切除的。
提出了一种融合计算机亚型和预后两个框架的计算方法，并在包含 82 个经 curated 的 CT 扫描患者的癌症影像学存档公共数据集上进行了验证。将所提出方法的准确性与国际肿瘤学疾病分类（ICD-O）定义的病理分析黄金标准进行了比较。使用 Kaplan-Meier 估计器和对数秩检验（p 值）对一系列生存结果测试案例在计算方法和 ICD-O 之间进行了评估。
该计算方法在亚型分类方面表现出很高的准确性（96.2%），并且在腺癌和鳞状细胞癌患者的总体生存预测的统计意义方面与病理亚型分类具有良好的一致性（p < 0.03）（p < 0.02）。在预后方面，计算和病理亚型之间的可重复性程度很高，平均一致性相关系数（CCC）为 0.9910。
本研究的结果支持了这样一种观点，即定量分析能够代表组织特征，就像定性分析提供的那样。
