Program Information

FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

J Oh

J Oh*, A Apte , M Folkerts , Z Kohutek , A Wu , A Rimner , N Lee , J Deasy , Memorial Sloan Kettering Cancer Center, New York, NY

Presentations

WE-E-17A-3 Wednesday 1:45PM - 3:45PM Room: 17A

Purpose:
An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research.

Methods:
We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H&N) cancer patients with stage III-IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV).

Results:
For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H&N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H&N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites.

Conclusion:
PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.


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