Abstract
We extracted image features from serial 18F-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.
Highlights
Anal cancer is a relatively uncommon malignancy
The Positron emission tomography (PET)/CT scans used in this study were collected from Department of Radiation Oncology of one major cancer center in the United States for patients with non-metastatic squamous cell carcinoma of the anal canal treated with definitive Chemoradiation therapy (CRT) between 2008 and 2010
Using Pre-CRT and MidCRT scans only, Orientation of Pre-CRT standardized uptake value (SUV) and long run high gray level emphasis (LRHGLE) of Pre-CRT CT were selected as the most important feature set with a high AUC of 1.00
Summary
Anal cancer is a relatively uncommon malignancy. In the United States, the National Cancer Institute estimated 8,580 new cases and 1,160 deaths from anal cancer in 20181. Chemoradiation therapy (CRT) is preferred over abdominoperineal resection for the treatment of anal cancer patients because of sphincter preservation, surgery can be an effective salvage option [1,2,3,4]. After CRT, early detection of tumor recurrence is important for initiating salvage surgery and preventing the spread of disease to distant sites [5, 6]. Early detection of residual and progressive disease can sometimes be challenging because of treatment-related mucositis and dermatitis that may limit adequate physical examination [5]. As a non-invasive evaluation tool, anatomical imaging techniques (CT, ultrasound, and MRI) have been widely used in the tumor staging and treatment response evaluation. Because the region of anal tumors has similar intensity to the surrounding normal structures in the anatomical images and tumor margins may blend with surrounding normal tissues [5], these techniques may fail to accurately assess the presence of tumor
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