Prospective study on the reproducibility of radiomic features in the setting of variable CT contrast timing: initial results

Colorectal cancer is the third most commonly diagnosed cancer in the United States with roughly half of these patients developing liver metastases, of which less than 10% survive past 3 years. Although resection is possible in some cases, only 15–25% of these patients are considered cured at the 10 year mark. Research supported by single-institution data shows that quantitative information taken from contrast-enhanced computed tomography (CECT) scans has the potential to pre-operatively predict patients at high risk for recurrence. CT image acquisition, reconstruction, and contrast timing affect the generalizability of predictive radiomic models in CT. Identification of reproducible features, therefore, is a necessary prerequisite to clinical implementation. We prospectively varied CT acquisition, reconstruction, and contrast timing parameters to quantify variability of radiomic features. Reproducibility analysis was performed using Lin’s concordance correlation coefficient (CCC) with 135 patients from Memorial Sloan Kettering Cancer Center (n = 68) and University of Texas MD Anderson Cancer Center (n = 67). Each patient underwent an additional phase CECT scan within ± 15 seconds of the routine portal venous phase using a controlled protocol, with systematic variations in scan timing, image acquisition, and image reconstruction. Radiomic features were extracted from the liver parenchyma and largest metastasis separately. Features extracted from the liver parenchyma were found to be less reproducible than those extracted from the largest tumor,. Reproducibility of features extracted from the tumor showed a negative correlation with the magnitude of scan delay. When reconstructed with a 5 mm slice thickness, scans with higher levels of adaptive statistical iterative reconstruction showed less reproducibility.

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Published
2025-04-04
DOI
https://doi.org/10.1117/12.3048474
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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Prospective study on the reproducibility of radiomic features in the setting of variable CT contrast timing: initial results

Amber L. Simpson, Yun Shin Chun, Mohammad Hamghalam, Jacob Peoples et al.
Radiomics and Machine Learning in Medical Imaging
article

Prospective study on the reproducibility of radiomic features in the setting of variable CT contrast timing: initial results

Amber L. Simpson, Yun Shin Chun, Mohammad Hamghalam, Jacob Peoples, Christine Kang, Mithat Gönen, Richard Kinh Gian, Natalie Gangai, Maida Wasim, Imani James, John Rong, Joshua Virani-Wall
article en

Abstract

Colorectal cancer is the third most commonly diagnosed cancer in the United States with roughly half of these patients developing liver metastases, of which less than 10% survive past 3 years. Although resection is possible in some cases, only 15–25% of these patients are considered cured at the 10 year mark. Research supported by single-institution data shows that quantitative information taken from contrast-enhanced computed tomography (CECT) scans has the potential to pre-operatively predict patients at high risk for recurrence. CT image acquisition, reconstruction, and contrast timing affect the generalizability of predictive radiomic models in CT. Identification of reproducible features, therefore, is a necessary prerequisite to clinical implementation. We prospectively varied CT acquisition, reconstruction, and contrast timing parameters to quantify variability of radiomic features. Reproducibility analysis was performed using Lin’s concordance correlation coefficient (CCC) with 135 patients from Memorial Sloan Kettering Cancer Center (n = 68) and University of Texas MD Anderson Cancer Center (n = 67). Each patient underwent an additional phase CECT scan within ± 15 seconds of the routine portal venous phase using a controlled protocol, with systematic variations in scan timing, image acquisition, and image reconstruction. Radiomic features were extracted from the liver parenchyma and largest metastasis separately. Features extracted from the liver parenchyma were found to be less reproducible than those extracted from the largest tumor,. Reproducibility of features extracted from the tumor showed a negative correlation with the magnitude of scan delay. When reconstructed with a 5 mm slice thickness, scans with higher levels of adaptive statistical iterative reconstruction showed less reproducibility.

Memorial Sloan Kettering Cancer Center (US), The University of Texas MD Anderson Cancer Center (US), Queen's University (CA)
Gender equality
Openalex Percentile: Top 90%
Radiomics and Machine Learning in Medical Imaging
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