Ct radiomic features of pancreatic neuroendocrine neoplasms (panNEN) are robust against delineation uncertainty.

PURPOSE The aim of this study was to quantify the impact of CT delineation uncertainty of pancreatic neuroendocrine neoplasms (panNEN) on Radiomic features (RF). METHODS Thirty-one previously operated patients were considered. Three expert radiologists contoured panNEN lesions on pre-surgical high-resolution contrast-enhanced CT images and contours were transferred onto pre-contrast CT. Volume agreement was quantified by the DICE index. After images resampling and re-binning, 69 RF were extracted and the impact of inter-observer variability was assessed by Intra-Class Correlation (ICC): ICC > 0.80 was considered as a threshold for "very high" inter-observer agreement. RESULTS The median volume was 1.3 cc (range: 0.2-110 cc); a satisfactory inter-observer volume agreement was found (mean DICE = 0.78). Only 4 RF showed ICC < 0.80 (0.48-0.73), including asphericity and three RFs (of five) of the neighborhood intensity difference matrix (NID). CONCLUSIONS The impact of inter-observer variability in delineating panNEN on RF was minimum, with the exception of the NID family and asphericity, showing a moderate agreement. These results support the feasibility of studies aiming to assess CT radiomic biomarkers for panNEN.

[1]  O. Mawlawi,et al.  Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. , 2014, International journal of radiation oncology, biology, physics.

[2]  Peter A Balter,et al.  Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer , 2016 .

[3]  Joon Koo Han,et al.  Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis , 2018, Acta radiologica.

[4]  M. Falconi,et al.  ENETS Consensus Guidelines for the Management of Patients with Digestive Neuroendocrine Neoplasms of the Digestive System: Well-Differentiated Pancreatic Non-Functioning Tumors , 2011, Neuroendocrinology.

[5]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[6]  H. Reber,et al.  Resection of pancreatic neuroendocrine tumors: results of 70 cases. , 2006, Archives of surgery.

[7]  K. Miles,et al.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival , 2012, European Radiology.

[8]  M. McNitt-Gray,et al.  A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. , 1999, Medical physics.

[9]  Xiao‐Yu Yin,et al.  Surgery management for sporadic small (≤2 cm), non-functioning pancreatic neuroendocrine tumors: a consensus statement by the Chinese Study Group for Neuroendocrine Tumors (CSNET). , 2017, International journal of oncology.

[10]  Jinzhong Yang,et al.  Measuring Computed Tomography Scanner Variability of Radiomics Features , 2015, Investigative radiology.

[11]  P. Lambin,et al.  Learning methods in radiation oncology ‘Rapid Learning health care in oncology’ – An approach towards decision support systems enabling customised radiotherapy’ q , 2013 .

[12]  Wolfgang Weber,et al.  Reliability of PET/CT Shape and Heterogeneity Features in Functional and Morphologic Components of Non–Small Cell Lung Cancer Tumors: A Repeatability Analysis in a Prospective Multicenter Cohort , 2016, The Journal of Nuclear Medicine.

[13]  Seung‐Mo Hong,et al.  Recent updates on grading and classification of neuroendocrine tumors. , 2017, Annals of diagnostic pathology.

[14]  Geoffrey G. Zhang,et al.  Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels , 2017, Medical physics.

[15]  Robert J. Gillies,et al.  Developing a classifier model for lung tumors in CT-scan images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[16]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[17]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[18]  Simon Wan,et al.  Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer , 2013, Clinical Cancer Research.

[19]  Geoffrey G. Zhang,et al.  Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[20]  R. Korn,et al.  Noninvasive Image Texture Analysis Differentiates K-ras Mutation from Pan-Wildtype NSCLC and Is Prognostic , 2014, PloS one.

[21]  I. El Naqa,et al.  Beyond imaging: The promise of radiomics. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[22]  T. McDade,et al.  Complications after pancreatectomy for neuroendocrine tumors: a national study. , 2010, The Journal of surgical research.

[23]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[24]  Manal M. Hassan,et al.  One hundred years after "carcinoid": epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[25]  Vicky Goh,et al.  The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer , 2015, European Radiology.

[26]  M. L. Belli,et al.  Quantifying the robustness of [18F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[27]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[28]  D. Sahani,et al.  Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis. , 2017, AJR. American journal of roentgenology.

[29]  Yanqi Huang,et al.  Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. , 2016, Radiology.

[30]  Hung-Ming Wang,et al.  Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images , 2014, BioMed research international.

[31]  R. Jensen,et al.  ENETS Consensus Guidelines Update for the Management of Patients with Functional Pancreatic Neuroendocrine Tumors and Non-Functional Pancreatic Neuroendocrine Tumors , 2016, Neuroendocrinology.

[32]  P. Lambin,et al.  Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability , 2013, Acta oncologica.

[33]  A. Scarpa,et al.  Metastatic and locally advanced pancreatic endocrine carcinomas: analysis of factors associated with disease progression. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.