MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. MRI Acquisition Protocol
2.3. Image Preprocessing and 3D ROIs Segmentations
2.4. Radiomic Analysis
2.4.1. Radiomic Features Extraction
2.4.2. Radiomic Feature Selection
2.4.3. Multivariable Prediction Models Building and Analysis
3. Results
3.1. Radiomic Features Selection
3.2. Multivariable Prediction Models
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Clinical Characteristic | Value |
---|---|
Age (mean ± SD) | 57.8 ± 15.3 |
Sex (n (%)) | |
Male | 26 (68.4) |
Female | 12 (31.6) |
Risk factors (n (%)) | |
HBV | 3 (7.9) |
HBV|tobacco | 2 (5.3) |
HCV | 5 (13.2) |
HCV|tobacco | 2 (5.3) |
HCV|alcohol | 1 (2.63) |
Alcohol | 9 (23.7) |
Tobacco | 1 (2.6) |
Tobacco|BCP | 1 (2.6) |
NAFLD | 2 (5.3) |
Hemochromatosis | 1 (2.6) |
No history of RF | 10 (26.3) |
NA | 1 (2.6) |
PH 1 (n (%)) | |
Y | 9 (23.7) |
N | 29 (76.3) |
Histologic grade (n (%)) | |
G1 | 7 (18.4) |
G2 | 15 (39.5) |
G3 | 16 (42.1) |
AJCC stage 2 (n (%)) | |
I | 15 (39.5) |
II | 12 (31.6) |
III | 10 (26.3) |
IV | 1 (2.6) |
Classification Task | Top 5 Selected Features |
---|---|
HCC/HT | T2 gldm Dependence Non Uniformity Normalized |
T2 glszm Small Area Low Gray Level Emphasis | |
T2 glrlm Long Run High Gray Level Emphasis | |
ART firstorder Minimum | |
ART gldm Large Dependence Low Gray Level Emphasis | |
G1 + G2/G3 | PORT gldm Large Dependence Low Gray Level Emphasis |
ART glszm Size Zone Non Uniformity Normalized | |
PORT glcm Maximum Probability | |
PORT glszm Small Area Low Gray Level Emphasis | |
T2 glszm Low Gray Level Zone Emphasis | |
G1/G2 | PORT ngtdm Strength |
T2 gldm Low Gray Level Emphasis | |
ART firstorder 10Percentile | |
ART firstorder Skewness | |
TARD firstorder Maximum | |
G1/G3 | SHAPE Surface Volume Ratio |
T2 gldm Large Dependence High Gray Level Emphasis | |
PORT glcm Maximum Probability | |
ART glcm Cluster Shade | |
ART firstorder Skewness | |
G2/G3 | PORT gldm Large Dependence Low Gray Level Emphasis |
PORT glszm Zone Percentage | |
PORT ngtdm Complexity | |
PORT glszm Large Area Low Gray Level Emphasis | |
TARD glrlm Long Run Low Gray Level Emphasis |
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Brancato, V.; Garbino, N.; Salvatore, M.; Cavaliere, C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics 2022, 12, 1085. https://doi.org/10.3390/diagnostics12051085
Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics. 2022; 12(5):1085. https://doi.org/10.3390/diagnostics12051085
Chicago/Turabian StyleBrancato, Valentina, Nunzia Garbino, Marco Salvatore, and Carlo Cavaliere. 2022. "MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma" Diagnostics 12, no. 5: 1085. https://doi.org/10.3390/diagnostics12051085