Literature DB >> 29730738

Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging.

Thomas Perrin1, Abhishek Midya1, Rikiya Yamashita2, Jayasree Chakraborty1, Tome Saidon2, William R Jarnagin1, Mithat Gonen3, Amber L Simpson1, Richard K G Do4.   

Abstract

PURPOSE: To evaluate the short-term reproducibility of radiomic features in liver parenchyma and liver cancers in patients who underwent consecutive contrast-enhanced CT (CECT) with intravenous iodinated contrast within 2 weeks by chance.
METHODS: The Institutional Review Board approved this HIPAA-compliant retrospective study and waived the requirement for patients' informed consent. Patients were included if they had a liver malignancy (liver metastasis, n = 22, intrahepatic cholangiocarcinoma, n = 10, and hepatocellular carcinoma, n = 6), had two consecutive CECT within 14 days, and had no prior or intervening therapy. Liver tumors and liver parenchyma were segmented and radiomic features (n = 254) were extracted. The number of reproducible features (with concordance correlation coefficients > 0.9) was calculated for patient subgroups with different variations in contrast injection rate and pixel resolution.
RESULTS: The number of reproducible radiomic features decreased with increasing variations in contrast injection rate and pixel resolution. When including all CECTs with injection rates differences of less than 15% vs. up to 50%, 63/254 vs. 0/254 features were reproducible for liver parenchyma and 68/254 vs. 50/254 features were reproducible for malignancies. When including all CT with pixel resolution differences of 0-5% or 0-15%, 20/254 vs. 0/254 features were reproducible for liver parenchyma; 34/254 liver malignancy features were reproducible with pixel differences up to 15%.
CONCLUSION: A greater number of liver malignancy radiomic features were reproducible compared to liver parenchyma features, but the proportion of reproducible features decreased with increasing variations in contrast injection rates and pixel resolution.

Entities:  

Keywords:  Biomarker; Computed tomography; Liver neoplasms; Repeatability; Texture analysis

Mesh:

Substances:

Year:  2018        PMID: 29730738      PMCID: PMC6209534          DOI: 10.1007/s00261-018-1600-6

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  27 in total

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3.  A concordance correlation coefficient to evaluate reproducibility.

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7.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

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Authors:  Omar S Al-Kadi; D Watson
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  15 in total

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Journal:  Abdom Radiol (NY)       Date:  2019-11

2.  Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma.

Authors:  Joshua S Jolissaint; Tiegong Wang; Kevin C Soares; Joanne F Chou; Mithat Gönen; Linda M Pak; Thomas Boerner; Richard K G Do; Vinod P Balachandran; Michael I D'Angelica; Jeffrey A Drebin; T P Kingham; Alice C Wei; William R Jarnagin; Jayasree Chakraborty
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Review 4.  [A primer on radiomics].

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

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9.  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

10.  Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions.

Authors:  Francesca Gallivanone; Matteo Interlenghi; Daniela D'Ambrosio; Giuseppe Trifirò; Isabella Castiglioni
Journal:  Contrast Media Mol Imaging       Date:  2018-09-10       Impact factor: 3.161

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