Journal article
Semiautomatic Segmentation and Radiomics for Dual-Energy CT: A Pilot Study to Differentiate Benign and Malignant Hepatic Lesions
American journal of roentgenology (1976), Vol.215(2), pp.398-405
08/2020
DOI: 10.2214/AJR.19.22164
PMID: 32406776
Abstract
This study assessed a machine learning-based dual-energy CT (DECT) tumor analysis prototype for semiautomatic segmentation and radiomic analysis of benign and malignant liver lesions seen on contrast-enhanced DECT.
This institutional review board-approved study included 103 adult patients (mean age, 65 ± 15 [SD] years; 53 men, 50 women) with benign (60/103) or malignant (43/103) hepatic lesions on contrast-enhanced dual-source DECT. Most malignant lesions were histologically proven; benign lesions were either stable on follow-up CT or had characteristic benign features on MRI. Low- and high-kilovoltage datasets were deidentified, exported offline, and processed with the DECT tumor analysis for semiautomatic segmentation of the volume and rim of each liver lesion. For each segmentation, contrast enhancement and iodine concentrations as well as radiomic features were derived for different DECT image series. Statistical analyses were performed to determine if DECT tumor analysis and radiomics can differentiate benign from malignant liver lesions.
Normalized iodine concentration and mean iodine concentration in the benign and malignant lesions were significantly different (
< 0.0001-0.0084; AUC, 0.695-0.856). Iodine quantification and radiomic features from lesion rims (AUC, ≤ 0.877) had higher accuracy for differentiating liver lesions compared with the values from lesion volumes (AUC, ≤ 0.856). There was no difference in the accuracies of DECT iodine quantification (AUC, 0.91) and radiomics (AUC, 0.90) for characterizing liver lesions.
DECT radiomics were more accurate than iodine quantification for differentiating solid benign and malignant hepatic lesions.
Details
- Title: Subtitle
- Semiautomatic Segmentation and Radiomics for Dual-Energy CT: A Pilot Study to Differentiate Benign and Malignant Hepatic Lesions
- Creators
- Fatemeh Homayounieh - Massachusetts General HospitalRamandeep Singh - Massachusetts General HospitalChayanin Nitiwarangkul - Massachusetts General HospitalFelix Lades - Siemens HealthcareBernhard Schmidt - Siemens HealthcareMartin Sedlmair - Siemens HealthcareSanjay Saini - Massachusetts General HospitalMannudeep K Kalra - Massachusetts General Hospital
- Resource Type
- Journal article
- Publication Details
- American journal of roentgenology (1976), Vol.215(2), pp.398-405
- DOI
- 10.2214/AJR.19.22164
- PMID
- 32406776
- ISSN
- 0361-803X
- eISSN
- 1546-3141
- Language
- English
- Date published
- 08/2020
- Academic Unit
- Radiology
- Record Identifier
- 9984697634302771
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