Abstract：
Accurate subtyping of NSCLC into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is the cornerstone of NSCLC diagnosis. Cytology samples reveal higher rates of classification failures, that is, subtyping as non-small cell carcinoma-not otherwise specified (NSCC-NOS), as compared with histology specimens. This study aims to identify specific algorithms on the basis of known cytomorphologic features that aid accurate and successful subtyping of NSCLC on cytology.
A total of 13 expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for nonkeratinizing LUSC. They selected from 23 predefined cytomorphologic features that they used in subtyping. Data were analyzed using machine learning algorithms on the basis of random forest method and regression trees.
From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792 of 1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; p < 0.0001). Keratinization, when present, recognized LUSC with high accuracy. In its absence, the machine learning algorithms developed on the basis of experts\' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates.
Suboptimal recognition of LUSC in the absence of keratinization remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS seems to be an inevitable morphologic diagnosis emphasizing that ancillary immunochemistry is necessary to achieve accurate subtyping on cytology.
A total of 13 expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for nonkeratinizing LUSC. They selected from 23 predefined cytomorphologic features that they used in subtyping. Data were analyzed using machine learning algorithms on the basis of random forest method and regression trees.
From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792 of 1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; p < 0.0001). Keratinization, when present, recognized LUSC with high accuracy. In its absence, the machine learning algorithms developed on the basis of experts\' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates.
Suboptimal recognition of LUSC in the absence of keratinization remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS seems to be an inevitable morphologic diagnosis emphasizing that ancillary immunochemistry is necessary to achieve accurate subtyping on cytology.
摘要：
将NSCLC准确分型为肺腺癌(LUAD)和肺鳞癌(LUSC)是NSCLC诊断的基石。细胞学样本显示出更高的分类失败率，也就是说,亚型为非小细胞癌-未指明(NSCC-NOS)，与组织学标本相比。这项研究旨在根据已知的细胞形态学特征确定特定的算法，以帮助在细胞学上准确和成功地对NSCLC进行分型。
共有13位专家细胞病理学家匿名参与了一项针对非角化性LUSC的119个亚型NSCLC细胞学病例的在线调查（80个为LUAD，39个为LUSC）。他们从23个预定义的细胞形态学特征中进行了选择，这些特征用于分型。在随机森林方法和回归树的基础上，使用机器学习算法对数据进行分析。
从记录的1474个响应中，53.7%(1474例中的792例)的反应达到了一致的细胞学分型.在金标准LUAD（36％）和LUSC（38％）病例中，细胞学上的NSCC-NOS率相似。金标准LUSC(17.6%)的误分类率高于金标准LUAD(5.5%;p<0.0001)。角化,当存在时,以高精度识别LUSC。在缺席的情况下,在专家选择的基础上开发的机器学习算法无法在不增加误分类率的情况下降低细胞学NSCC-NOS率.
在没有角质化的情况下，对LUSC的次优识别仍然是提高细胞学分型准确性的主要障碍，这种情况要么分类失败（NSCC-NOS），要么错误分类为LUAD。NSCC-NOS似乎是不可避免的形态学诊断，强调辅助免疫化学对于实现细胞学的准确分型是必要的。
共有13位专家细胞病理学家匿名参与了一项针对非角化性LUSC的119个亚型NSCLC细胞学病例的在线调查（80个为LUAD，39个为LUSC）。他们从23个预定义的细胞形态学特征中进行了选择，这些特征用于分型。在随机森林方法和回归树的基础上，使用机器学习算法对数据进行分析。
从记录的1474个响应中，53.7%(1474例中的792例)的反应达到了一致的细胞学分型.在金标准LUAD（36％）和LUSC（38％）病例中，细胞学上的NSCC-NOS率相似。金标准LUSC(17.6%)的误分类率高于金标准LUAD(5.5%;p<0.0001)。角化,当存在时,以高精度识别LUSC。在缺席的情况下,在专家选择的基础上开发的机器学习算法无法在不增加误分类率的情况下降低细胞学NSCC-NOS率.
在没有角质化的情况下，对LUSC的次优识别仍然是提高细胞学分型准确性的主要障碍，这种情况要么分类失败（NSCC-NOS），要么错误分类为LUAD。NSCC-NOS似乎是不可避免的形态学诊断，强调辅助免疫化学对于实现细胞学的准确分型是必要的。
