دانلود مقاله انگلیسی رایگان:تجزیه و تحلیل بافت CT برای پیش بینی وضعیت جهش KRAS در سرطان کولورکتال از طریق یک روش یادگیری ماشین - 2019
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  • CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach
    CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 650 کیلوبایت

    قیمت: رایگان


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