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Guha S, Ibrahim A, Wu Q, Geng P, Chou Y, Yang H, Ma J, Lu L, Wang D, Schwartz LH, Xie CM, Zhao B. Machine learning-based identification of contrast-enhancement phase of computed tomography scans. PLoS One 2024; 19:e0294581. [PMID: 38306329 PMCID: PMC10836663 DOI: 10.1371/journal.pone.0294581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 11/04/2023] [Indexed: 02/04/2024] Open
Abstract
Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.
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Affiliation(s)
- Siddharth Guha
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Qian Wu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Pengfei Geng
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Yen Chou
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Jingchen Ma
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Delin Wang
- Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
| | | | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States of America
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