肺癌
病态的
指纹(计算)
癌症
肺
病理
医学
计算生物学
生物
内科学
计算机科学
人工智能
作者
Chongxuan Tian,He Zhu,Xiangchuan Meng,Zijuan Ma,Shuanghu Yuan,Wei Li
标识
DOI:10.1002/jbio.202300174
摘要
The distinctions in pathological types and genetic subtypes of lung cancer have a direct impact on the choice of treatment choices and clinical prognosis in clinical practice. This study used pathological histological sections of surgically removed or biopsied tumor tissue from 36 patients. Based on a small sample size, millions of spectral data points were extracted to investigate the feasibility of employing intracellular fluorescent fingerprint information to diagnose the pathological types and mutational status of lung cancer. The intracellular fluorescent fingerprint information revealed the EGFR gene mutation characteristics in lung cancer, and the area under the curve (AUC) value for the optimal model was 0.98. For the classification of lung cancer pathological types, the macro average AUC value for the ensemble-learning model was 0.97. Our research contributes new idea for pathological diagnosis of lung cancer and offers a quick, easy, and accurate auxiliary diagnostic approach.
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