Pancreatic ductal adenocarcinoma (PDAC) shows a 5-year survival rate of 8%. This mortality results from a lack of methods to accurately treat patients. PDAC is remarkable for its fibrotic reaction, which is present at early stages of PDAC development. Components of this environment can be measured on clinical images. PET derived parameters, e.g. SUVmax, have not been able to provide prognostic information. In this study we developed an algorithm based on FDG-PET texture features (TF) that classifies heterogeneous or homogeneous tumors and shows a correlation with overall survival.
In total, 121 patients with histologically proven PDAC who underwent 18F-FDG PET/CT (Siemens Biograph mCT, Knoxville, US) were selected from the hospital system. Eighty-six EANM reconstructed scans were visually labeled as 'homogenous' or 'heterogeneous' by experienced Nuclear Medicine physicians and served as training set to develop the classifier . All the 121 scans were used as validation set for the correlation with overall survival (OS). Tumors were delineated using 40% threshold of the SUVmax with manual correction. TF were extracted using the PyRadiomcis toolbox . TF were selected and tested for robustness as described in literature [7-9]. The classifier was build using logistic regression. Prognostic impact was assessed by Kaplan Meier survival analysis and log-rank test.
Optimal performance of the leave-one-out cross-validation classifier in the training set yielded an accuracy of 0.73 and AUC of 0.71 in classifying PDAC as heterogeneous or homogeneous tumors. Of note, two tumors were visually labeled as homogenous but correctly classifier as heterogeneous by the classifier after review. For the 121 patients the OS of PDAC tumors classified as heterogeneous, was significantly worse than for homogeneous tumors; median OS 69 weeks (95%CI 64 to 91 weeks) versus median 95 weeks (95%CI 76 to 114), p= 0.0285). This is in contrast with single standard PET parameters, single TF or manual labeling, which had no significant prognostic impact.
We developed an algorithm that accurately classifies PDAC as heterogeneous or homogeneous, based on a set of 18F-FDG PET derived texture features. We showed that the classification result has prognostic value, improving upon standard PET derived parameters and single texture-features. Further validation of this algorithm in an external cohort of PDAC patients is ongoing.
 Siegel, R.L., K.D. Miller, and A. Jemal, Cancer statistics, 2016. CA Cancer J Clin, 2016. 66(1): p. 7-30.
 Ryan, D.P., T.S. Hong, and N. Bardeesy, Pancreatic adenocarcinoma. N Engl J Med, 2014. 371(11): p. 1039-49.
 Neesse, A., et al., Stromal biology and therapy in pancreatic cancer: a changing paradigm. Gut, 2015. 64(9): p. 1476-84.
 Heid, I., et al., Co-clinical Assessment of Tumor Cellularity in Pancreatic Cancer. Clin Cancer Res, 2017. 23(6): p. 1461-1470.
 Boellaard, R., et al., FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging, 2010. 37(1): p. 181-200.
 van Griethuysen, J.J.M., et al., Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res, 2017. 77(21): p. e104-e107.
 Yan, J., et al., Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET. J Nucl Med, 2015. 56(11): p. 1667-73.
 Leijenaar, R.T., et al., The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep, 2015. 5: p. 11075.
 Grootjans, W., et al., The Impact of Optimal Respiratory Gating and Image Noise on Evaluation of Intratumor Heterogeneity on 18F-FDG PET Imaging of Lung Cancer. J Nucl Med, 2016. 57(11): p. 1692-1698.