Literature DB >> 31392481

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation.

Rikiya Yamashita1, Thomas Perrin2, Jayasree Chakraborty2, Joanne F Chou3, Natally Horvat1, Maura A Koszalka2, Abhishek Midya2, Mithat Gonen3, Peter Allen2, William R Jarnagin2, Amber L Simpson2, Richard K G Do4.   

Abstract

OBJECTIVES: This study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans.
METHODS: In this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs.
RESULTS: Among pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively.
CONCLUSIONS: Variations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions. KEY POINTS: • For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC < 0.9) in a clinical heterogeneous dataset. • Variations between CECT scans affected the number of reproducible radiomic features to a greater extent than variations in radiologist segmentation. • A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.

Entities:  

Keywords:  Pancreatic ductal carcinoma; Radiomics; Reproducibility of results; Texture analysis; X-ray computed tomography

Mesh:

Substances:

Year:  2019        PMID: 31392481      PMCID: PMC7127865          DOI: 10.1007/s00330-019-06381-8

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  16 in total

Review 1.  CT and MRI of pancreatic tumors: an update in the era of radiomics.

Authors:  Marion Bartoli; Maxime Barat; Anthony Dohan; Sébastien Gaujoux; Romain Coriat; Christine Hoeffel; Christophe Cassinotto; Guillaume Chassagnon; Philippe Soyer
Journal:  Jpn J Radiol       Date:  2020-10-21       Impact factor: 2.374

2.  Radiomics for CT Assessment of Vascular Contact in Pancreatic Adenocarcinoma.

Authors:  Richard K G Do; Avinash Kambadakone
Journal:  Radiology       Date:  2021-09-07       Impact factor: 29.146

Review 3.  Radiomics: a primer on high-throughput image phenotyping.

Authors:  Kyle J Lafata; Yuqi Wang; Brandon Konkel; Fang-Fang Yin; Mustafa R Bashir
Journal:  Abdom Radiol (NY)       Date:  2021-08-25

4.  Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation.

Authors:  Gerard M Healy; Emmanuel Salinas-Miranda; Rahi Jain; Xin Dong; Dominik Deniffel; Ayelet Borgida; Ali Hosni; David T Ryan; Nwabundo Njeze; Anne McGuire; Kevin C Conlon; Jonathan D Dodd; Edmund Ronan Ryan; Robert C Grant; Steven Gallinger; Masoom A Haider
Journal:  Eur Radiol       Date:  2021-11-10       Impact factor: 7.034

5.  Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy.

Authors:  Jochen Paul Steinacker; Nora Steinacker-Stanescu; Thomas Ettrich; Marko Kornmann; Katharina Kneer; Ambros Beer; Meinrad Beer; Stefan Andreas Schmidt
Journal:  Visc Med       Date:  2020-04-07

6.  Baseline CT-based Radiomic Features Aid Prediction of Nodal Positivity after Neoadjuvant Therapy in Pancreatic Cancer.

Authors:  Sherif B Elsherif; Sanaz Javadi; Ott Le; Nathan Lamba; Matthew H G Katz; Eric P Tamm; Priya R Bhosale
Journal:  Radiol Imaging Cancer       Date:  2022-03

7.  Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases.

Authors:  Francesco Rizzetto; Francesca Calderoni; Cristina De Mattia; Arianna Defeudis; Valentina Giannini; Simone Mazzetti; Lorenzo Vassallo; Silvia Ghezzi; Andrea Sartore-Bianchi; Silvia Marsoni; Salvatore Siena; Daniele Regge; Alberto Torresin; Angelo Vanzulli
Journal:  Eur Radiol Exp       Date:  2020-11-10

8.  Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

Authors:  Reza Reiazi; Colin Arrowsmith; Mattea Welch; Farnoosh Abbas-Aghababazadeh; Christopher Eeles; Tony Tadic; Andrew J Hope; Scott V Bratman; Benjamin Haibe-Kains
Journal:  Cancers (Basel)       Date:  2021-05-08       Impact factor: 6.639

9.  Quality control of radiomic features using 3D-printed CT phantoms.

Authors:  Usman Mahmood; Aditya Apte; Christopher Kanan; David D B Bates; Giuseppe Corrias; Lorenzo Manneli; Jung Hun Oh; Yusuf Emre Erdi; John Nguyen; Joseph O'Deasy; Amita Shukla-Dave
Journal:  J Med Imaging (Bellingham)       Date:  2021-06-29

10.  A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden.

Authors:  Zbigniew Starosolski; Amy N Courtney; Mayank Srivastava; Linjie Guo; Igor Stupin; Leonid S Metelitsa; Ananth Annapragada; Ketan B Ghaghada
Journal:  Contrast Media Mol Imaging       Date:  2021-06-14       Impact factor: 3.161

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