College of Computer Science, Shaanxi Normal University, Xian, China.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Mod Pathol. 2017 Dec;30(12):1655-1665. doi: 10.1038/modpathol.2017.98. Epub 2017 Aug 4.
Oral cavity squamous cell carcinoma is the most common type of head and neck carcinoma. Its incidence is increasing worldwide, and it is associated with major morbidity and mortality. It is often unclear which patients have aggressive, treatment refractory tumors vs those whose tumors will be more responsive to treatment. Better identification of patients with high- vs low-risk cancers could help provide more tailored treatment approaches and could improve survival rates while decreasing treatment-related morbidity. This study investigates computer-extracted image features of nuclear shape and texture on digitized images of H&E-stained tissue sections for risk stratification of oral cavity squamous cell carcinoma patients compared with standard clinical and pathologic parameters. With a tissue microarray cohort of 115 retrospectively identified oral cavity squamous cell carcinoma patients, 50 were randomly chosen as the modeling set, and the remaining 65 constituted the test set. Following nuclear segmentation and feature extraction, the Wilcoxon rank sum test was used to identify the five most prognostic quantitative histomorphometric features from the modeling set. These top ranked features were then combined via a machine learning classifier to construct the oral cavity histomorphometric-based image classifier (OHbIC). The classifier was then validated for its ability to risk stratify patients for disease-specific outcomes on the test set. On the test set, the classifier yielded an area under the receiver operating characteristic curve of 0.72 in distinguishing disease-specific outcomes. In univariate survival analysis, high-risk patients predicted by the classifier had significantly poorer disease-specific survival (P=0.0335). In multivariate analysis controlling for T/N-stage, resection margins, and smoking status, positive classifier results were independently predictive of poorer disease-specific survival: hazard ratio (95% confidence interval)=11.023 (2.62-46.38) and P=0.001. Our results suggest that quantitative histomorphometric features of local nuclear architecture derived from digitized H&E slides of oral cavity squamous cell carcinomas are independently predictive of patient survival.
口腔鳞状细胞癌是头颈部最常见的癌种。其全球发病率呈上升趋势，与较高的发病率和死亡率相关。目前尚不清楚哪些患者的肿瘤具有侵袭性、治疗抵抗性，哪些患者的肿瘤对治疗更为敏感。更好地区分高危和低危癌症患者有助于提供更具针对性的治疗方法，提高生存率，降低治疗相关的发病率。本研究旨在通过对 H&E 染色组织切片的数字化图像进行计算机提取的核形态和纹理图像特征分析，与标准的临床病理参数相结合，评估这些特征能否用于口腔鳞状细胞癌患者的风险分层。该研究纳入了 115 例经组织学证实的口腔鳞状细胞癌患者，其中 50 例随机纳入建模组，65 例纳入验证组。通过核分割和特征提取，Wilcoxon 秩和检验用于从建模组中识别出 5 个最具预后价值的定量组织形态学特征。然后，使用机器学习分类器将这些排名最高的特征组合起来，构建基于口腔组织形态学的图像分类器（OHbIC）。最后，在验证组中评估该分类器区分患者疾病特异性结局的能力。在验证组中，该分类器在区分疾病特异性结局方面的受试者工作特征曲线下面积为 0.72。在单因素生存分析中，分类器预测的高危患者疾病特异性生存率显著较低（P=0.0335）。在多因素分析中，校正 T/N 分期、切缘状态和吸烟状况后，阳性分类器结果仍可独立预测疾病特异性生存率：危险比（95%置信区间）=11.023（2.62-46.38），P=0.001。我们的研究结果表明，从口腔鳞状细胞癌 H&E 切片的数字化图像中提取的局部核结构的定量组织形态学特征可独立预测患者的生存情况。
