Encrypted login | home

Program Information

Evaluating the Role of Mid-Treatment and Post-Treatment FDG-PET/CT in Predicting Progression-Free Survival and Distant Metastasis of Anal Cancer Patients Treated with Chemoradiotherapy

H Zhang

H Zhang1*, J Wang1 , M Chuong1 , W D'Souza1 , K Latifi2 , Nadia Saeed3 , S Tan4 , W Choi1 , S Hoffe2 , R Shridhar5 , E Moros2 , W Lu1 , (1) University of Maryland School of Medicine, Baltimore, MD, (2) Moffitt Cancer Center, Tampa, FL, (3) Brwon University, Providence, RI, (4) Huazhong University of Science & Technology , Wuhan, China, (5) Florida Hospital, Orlando, FL


SU-E-J-254 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To evaluate the role of mid-treatment and post-treatment FDG-PET/CT in predicting progression-free survival (PFS) and distant metastasis (DM) of anal cancer patients treated with chemoradiotherapy (CRT).

Methods: 17 anal cancer patients treated with CRT were retrospectively studied. The median prescription dose was 56 Gy (range, 50-62.5 Gy). All patients underwent FDG-PET/CT scans before and after CRT. 16 of the 17 patients had an additional FDG-PET/CT image at 3-5 weeks into the treatment (denoted as mid-treatment FDG-PET/CT). 750 features were extracted from these three sets of scans, which included both traditional PET/CT measures (SUVmax, SUVpeak, tumor diameters, etc.) and spatial-temporal PET/CT features (comprehensively quantify a tumor’s FDG uptake intensity and distribution, spatial variation (texture), geometric property and their temporal changes relative to baseline). 26 clinical parameters (age, gender, TNM stage, histology, GTV dose, etc.) were also analyzed. Advanced analytics including methods to select an optimal set of predictors and a model selection engine, which identifies the most accurate machine learning algorithm for predictive analysis was developed.

Results: Comparing baseline + mid-treatment PET/CT set to baseline + post-treatment PET/CT set, 14 predictors were selected from each feature group. Same three clinical parameters (tumor size, T stage and whether 5-FU was held during any cycle of chemotherapy) and two traditional measures (pre-CRT SUVmin and SUVmedian) were selected by both predictor groups. Different mix of spatial-temporal PET/CT features was selected. Using the 14 predictors and Naive Bayes, mid-treatment PET/CT set achieved 87.5% accuracy (2 PFS patients misclassified, all local recurrence and DM patients correctly classified). Post-treatment PET/CT set achieved 94.0% accuracy (all PFS and DM patients correctly predicted, 1 local recurrence patient misclassified) with logistic regression, neural network or support vector machine model.

Conclusion: Applying radiomics approach to either mid-treatment or post-treatment PET/CT could achieve high accuracy in predicting anal cancer treatment outcomes.

Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grant R01CA172638.

Contact Email: