عنوان انگلیسی مقاله:
Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer
ترجمه فارسی عنوان مقاله:
استراتژی رادیومیک مبتنی بر یادگیری ماشین برای پیش بینی تکثیر سلولی در سرطان ریه سلول غیر کوچک
Sciencedirect - Elsevier - European Journal of Radiology, 118 (2019) 32-37: doi:10:1016/j:ejrad:2019:06:025
Qianbiao Gua,b, Zhichao Fenga, Qi Lianga, Meijiao Lia, Jiao Denga, Mengtian Maa, Wei Wanga, Jianbin Liub, Peng Liub, Pengfei Ronga
Purpose: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the
cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC).
Methods: 245 histopathological confirmed NSCLC patients who underwent CT scans were retrospectively included.
The Ki-67 proliferation index (Ki-67 PI) were measured within 2 weeks after CT scans. A lesion volume of
interest (VOI) was manually delineated and radiomics features were extracted by MaZda software from CT
images. A random forest feature selection algorithm (RFFS) was used to reduce features. Six kinds of machine
learning methods were used to establish radiomics classifiers, subjective imaging feature classifiers and combined
classifiers, respectively. The performance of these classifiers was evaluated by the receiver operating
characteristic curve (ROC) and compared with Delong test.
Results: 103 radiomics features were extracted and 20 optimal features were selected using RFFS. Among the
radiomics classifiers established by six machine learning methods, random forest-based radiomics classifier
achieved the best performance (AUC=0.776) in predicting the Ki-67 expression level with sensitivity and
specificity of 0.726 and 0.661, which was better than that of subjective imaging classifiers (AUC=0.625,
P < 0.05). However, the combined classifiers did not improve the predictive performance (AUC=0.780,
P > 0.05), with sensitivity and specificity of 0.752 and 0.633.
Conclusions: The machine learning-based CT radiomics classifier in NSCLC can facilitate the prediction of the
expression level of Ki-67 and provide a novel non-invasive strategy for assessing the cell proliferation.
Keywords: Non-small cell lung cancer (NSCLC) | Ki-67 | CT | Radiomics | Machine learning