Lung cancer is the leading cause of cancer death in the U.S. Therefore, it is imperative to identify novel biomarkers for the early detection and progression of lung cancer. PRMT6 is associated with poor lung cancer prognosis. However, analyzing PRMT6 expression manually in large samples is time-consuming posing a significant limitation for processing this biomarker. To overcome this issue, we trained and validated an automated method for scoring PRMT6 in lung cancer tissues, which can then be used as the standard method in future larger cohorts to explore population-level associations between PRMT6 expression and sociodemographic/clinicopathologic characteristics. We evaluated the ability of a trained artificial intelligence (AI) algorithm to reproduce the PRMT6 immunoreactive scores obtained by pathologists. Our findings showed that tissue segmentation to cancer vs. non-cancer tissues was the most critical parameter, which required training and adjustment of the algorithm to prevent scoring non-cancer tissues or ignoring relevant cancer cells. The trained algorithm showed a high concordance with pathologists with a correlation coefficient of 0.88. The inter-rater agreement was significant, with an intraclass correlation of 0.95 and a scale reliability coefficient of 0.96. In conclusion, we successfully optimized a machine learning algorithm for scoring PRMT6 expression in lung cancer that matches the degree of accuracy of scoring by pathologists.
肺癌是美国癌症死亡的首要原因。因此，亟需寻找用于肺癌早期检测及进展监测的新型生物标志物。PRMT6与肺癌不良预后相关，但人工分析大量样本中的PRMT6表达耗时费力，成为该生物标志物应用的重要限制因素。为解决这一问题，我们训练并验证了一种肺癌组织中PRMT6自动评分方法，该方法可作为未来大规模队列研究的标准化方案，用于探索PRMT6表达与社会人口学/临床病理特征之间的群体关联性。我们评估了训练后人工智能算法复现病理学家PRMT6免疫反应评分的能力。研究发现，组织分割（区分癌组织与非癌组织）是最关键参数，需要对算法进行训练和调整以避免对非癌组织评分或遗漏相关癌细胞。训练后的算法与病理学家评分高度一致，相关系数达0.88。评分者间一致性显著，组内相关系数为0.95，量表信度系数为0.96。综上，我们成功优化了用于肺癌PRMT6表达的机器学习评分算法，其准确度达到病理学家评分水平。
Machine Learning for Digital Scoring of PRMT6 in Immunohistochemical Labeled Lung Cancer
肿瘤(癌症)患者之家