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    Prediction of anal cancer recurrence after chemoradiotherapy using quantitative image features extracted from serial18 F-FDG PET/CT

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    Author
    Wang, J.
    Zhang, H.
    Chuong, M.
    Tan, S.
    Choi, W.
    Lu, W.
    Date
    2019
    Journal
    Frontiers in Oncology
    Publisher
    Frontiers Media S.A.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3389/fonc.2019.00934
    Abstract
    We extracted image features from serial18 F-labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) / computed tomography (CT) scans of anal cancer patients for the prediction of tumor recurrence after chemoradiation therapy (CRT). Seventeen patients (4 recurrent and 13 non-recurrent) underwent three PET/CT scans at baseline (Pre-CRT), in the middle of the treatment (Mid-CRT) and post-treatment (Post-CRT) were included. For each patient, Mid-CRT and Post-CRT scans were aligned to Pre-CRT scan. Comprehensive image features were extracted from CT and PET (SUV) images within manually delineated gross tumor volume, including geometry features, intensity features and texture features. The difference of feature values between two time points were also computed and analyzed. We employed univariate logistic regression model, multivariate model, and naïve Bayesian classifier to analyze the image features and identify useful tumor recurrent predictors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the accuracy of the prediction. In univariate analysis, six geometry, three intensity, and six texture features were identified as significant predictors of tumor recurrence. A geometry feature of Roundness between Post-CRT and Pre-CRT CTs was identified as the most important predictor with an AUC value of 1.00 by multivariate logistic regression model. The difference of Number of Pixels on Border (geometry feature) between Post-CRT and Pre-CRT SUVs and Elongation (geometry feature) of Post-CRT CT were identified as the most useful feature set (AUC = 1.00) by naïve Bayesian classifier. To investigate the early prediction ability, we used features only from Pre-CRT and Mid-CRT scans. Orientation (geometry feature) of Pre-CRT SUV, Mean (intensity feature) of Pre-CRT CT, and Mean of Long Run High Gray Level Emphasis (LRHGLE) (texture feature) of Pre-CRT CT were identified as the most important feature set (AUC = 1.00) by multivariate logistic regression model. Standard deviation (intensity feature) of Mid-CRT SUV and difference of Mean of LRHGLE (texture feature) between Mid-CRT and Pre-CRT SUVs were identified as the most important feature set (AUC = 0.86) by naïve Bayesian classifier. The experimental results demonstrated the potential of serial PET/CT scans in early prediction of anal tumor recurrence. © 2019 Wang, Zhang, Chuong, Latifi, Tan, Choi, Hoffe, Shridhar and Lu.
    Sponsors
    This work was supported in part through the NIH/NCI Grant R01CA172638 and the NIH/NCI Cancer Center Support Grant P30 CA008748.
    Keyword
    Anal cancer
    Chemoradiation therapy
    Image analysis
    Recurrence prediction
    Serial PET/CT
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073108929&doi=10.3389%2ffonc.2019.00934&partnerID=40&md5=bac474c2265f300be0c936b1cff6075e; http://hdl.handle.net/10713/11180
    ae974a485f413a2113503eed53cd6c53
    10.3389/fonc.2019.00934
    Scopus Count
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