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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach
ترجمه فارسی عنوان مقاله:
تجزیه و تحلیل بافت CT برای پیش بینی وضعیت جهش KRAS در سرطان کولورکتال از طریق یک روش یادگیری ماشین
منبع:
Sciencedirect - Elsevier - European Journal of Radiology, 118 (2019) 38-43: doi:10:1016/j:ejrad:2019:06:028
نویسنده:
Narumi Taguchia, Seitaro Odaa,⁎, Yasuhiro Yokotaa, Sadahiro Yamamurab, Masanori Imutaa, Tadatoshi Tsuchigamec, Yasunori Nagayamaa, Masafumi Kidoha, Takeshi Nakauraa, Shinya Shiraishia, Yoshinori Funamad, Satoru Shinrikie, Yuji Miyamotof, Hideo Babaf, Yasuyuki Yamashitaa
چکیده انگلیسی:
Purpose: This study aimed to investigate whether a machine learning-based computed tomography (CT) texture
analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in
colorectal cancer.
Method: This retrospective study comprised 40 patients with pathologically confirmed colorectal cancer who
underwent KRAS mutation testing, contrast-enhancement CT, and 18F-fluorodeoxyglucose (FDG) positron
emission tomography (PET) before treatment. Of the 40 patients, 20 had mutated KRAS genes, whereas 20 had
wild-type KRAS genes. Fourteen CT texture parameters were extracted from portal venous phase CT images of
primary tumors, and the maximum standard uptake values (SUVmax) on 18F-FDG PET images were recorded.
Univariate logistic regression was used to develop predictive models for each CT texture parameter and SUVmax,
and a machine learning method (multivariate support vector machine) was used to develop a comprehensive set
of CT texture parameters. The area under the receiver operating characteristic (ROC) curve (AUC) of each model
was calculated using five-fold cross validation. In addition, the performance of the machine learning method
with the CT texture parameters was compared with that of SUVmax.
Results: In the univariate analyses, the AUC of each CT texture parameter ranged from 0.4 to 0.7, while the AUC
of the SUVmax was 0.58. Comparatively, the multivariate support vector machine with comprehensive CT texture
parameters yielded an AUC of 0.82, indicating a superior prediction performance when compared to the SUVmax.
Conclusions: A machine learning-based CT texture analysis was superior to the SUVmax for predicting the KRAS
mutation status of a colorectal cancer.
Keywords: Colorectal cancer | CT texture analysis | Machine learning | KRAS mutation | Radiogenomics
قیمت: رایگان
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