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Quantification of Local Metabolic Tumor Volume Changes by Registering Blended 18F-FDG PET/CT Images for Prediction of Pathologic Tumor Response

S Riyahi1*, W Choi2 , C Liu3 , S Nadeem1 , S Tan5 , H Zhong6 , W Chen7 , A Wu1 , J Mechalakos1 , J Deasy1 , W Lu1 , (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) University of Virginia, Charlottesville, VA, (3) National Taiwan University Hospital Yunlin Branch, Yunlin, (5) Huazhong University of Science & Technology, Wuhan, (6) Medical College of Wisconsin, Milwaukee, WI, (7) University of Maryland School of Medicine, Baltimore, MD


(Wednesday, 7/17/2019) 10:15 AM - 12:15 PM

Room: 304ABC

Purpose: Quantification of local metabolic tumor volume changes (∆MTV) after chemo-radiotherapy (CRT) would allow accurate tumor response evaluation. The purpose of this study is to measure local ∆MTV using blended PET/CT registration and to provide an early prediction of pathologic tumor response.

Methods: 61 patients with locally advanced esophageal cancer underwent baseline, post-induction chemotherapy (follow-up) and post-CRT PET/CT. A grayscale blended PET/CT image was generated and follow-up blended PET/CT image was registered to the baseline blended PET/CT image with tumor motion correction. Jacobian map (J) was computed from the transformation which measured local MTV shrinkage (J < 1) or expansion (J > 1). Registration accuracy was evaluated by comparing the net ∆MTV calculated by Jacobian integral [(mean J – 1) x baseline MTV] vs. semi-automatic segmentation. Radiomic features were then extracted from the Jacobian maps and PET/CT images and distinctive features were identified using a hierarchical clustering method. A logistic regression model was constructed using two distinctive features pre-identified from PET/CT images (PET Kurtosis) and Jacobian map (Mean of Cluster Shade) to predict pathologic complete response (pCR). The model was evaluated using 10x5-fold cross-validation.

Results: Qualitatively, Jacobian map by blended PET/CT registration showed smoother local ∆MTV than PET-PET and CT-CT registrations. Quantitatively, ∆MTV calculated by Jacobian integral of blended PET/CT registration (-42.0%) was closer to ∆MTV measured by the semi-automatic segmentation (-50.0%) than by PET-PET (-29.4%) and CT-CT (-8.0%) registrations. Kurtosis in follow-up PET and Jacobian Mean of Cluster Shade represented uniformity of FDG uptake and asymmetry of ∆MTV, respectively and the logistic model built with these two features achieved a high accuracy (AUC=0.82) in predicting pCR.

Conclusion: The novel Blended PET/CT registration led to more accurate quantification of ∆MTV than PET-PET and CT-CT registrations. The model with combination of PET/CT and Jacobian features showed high accuracy in predicting pCR.

Funding Support, Disclosures, and Conflict of Interest: This work was supported in part through the NIH/NCI Grant R01CA172638 and the NIH/NCI Cancer Center Support Grant P30 CA008748.


Registration, Shape Analysis, PET


IM- Multi-modality imaging systems: CT/PET - human

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