Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design and Patient Selection
2.2. TACE Procedures
2.3. Data Collection and Follow-Up
2.4. Imaging and Radiomics Analysis
2.5. Development of Best TACE Technique Decision Framework
2.6. Statistical Analysis
3. Results
3.1. Patient Characteristics and Response Assessment
3.2. Multivariable Regression Analysis of Predictors of TACE Response
3.3. Development and Evaluation of a Prediction Model Based on Independent Predictors Identified by Regression Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Total | cTACE | DEB | DSM | p | |
---|---|---|---|---|---|
Clinical and laboratory data | |||||
N | 151 (100) | 33 (21.9) | 69 (45.7) | 49 (32.5) | |
Sex | |||||
Male | 120 (79.5) | 27 (81.8) | 52 (75.4) | 41 (83.7) | 0.508 |
Female | 31 (20.5) | 6 (18.2) | 17 (24.6) | 8 (16.3) | |
Age (years), median (IQR) | 64.0 (12.0) | 60.0 (10.0) | 63.0 (17.0) | 65.0 (10.0) | 0.325 |
Etiology of liver disease | |||||
Alcoholic | 63 (41.7) | 17 (51.5) | 26 (37.7) | 20 (40.8) | 0.882 |
Viral hepatitis | 40 (26.5) | 9 (27.3) | 17 (24.6) | 14 (28.6) | |
Biliary disease | 1 (0.7) | 0 (0) | 1 (1.4) | 0 (0) | |
NASH | 14 (9.3) | 2 (6.1) | 7 (10.1) | 5 (10.2) | |
Toxic | 1 (0.7) | 0 (0) | 0 (0) | 1 (2.0) | |
Autoimmune hepatitis | 1 (0.7) | 0 (0) | 1 (1.4) | 0 (0) | |
Hemochromatosis | 1 (0.7) | 0 (0) | 1 (1.4) | 0 (0) | |
Cryptogenic | 30 (19.9) | 5 (15.2) | 16 (23.2) | 9 (18.4) | |
ECOG | |||||
0 | 76 (50.3) | 14 (42.4) | 34 (49.3) | 28 (57.1) | 0.406 |
1 | 61 (40.4) | 13 (39.4) | 30 (43.5) | 18 (36.7) | |
2 | 12 (7.9) | 5 (15.2) | 5 (7.2) | 2 (4.1) | |
3 | 2 (1.3) | 1 (3.0) | 0 (0) | 1 (2.0) | |
BCLC | |||||
A | 79 (52.3) | 14 (42.4) | 33 (47.8) | 32 (65.3) | 0.322 |
B | 51 (33.8) | 13 (39.4) | 24 (34.8) | 14 (28.6) | |
C | 13 (8.6) | 3 (9.1) | 8 (11.6) | 2 (4.1) | |
D | 8 (5.3) | 3 (9.1) | 4 (5.8) | 1 (2.0) | |
Diagnosis of HCC by liver biopsy Previous therapy | 15 (9.9) | 4 (12.1) | 7 (10.1) | 4 (8.2) | 0.634 |
Resection | 8 (5.3) | 2 (6.1) | 4 (5.8) | 2 (4.1) | 0.111 |
Systemic | 7 (4.6) | 2 (6.1) | 4 (5.8) | 1 (2.0) | 0.575 |
Local | |||||
RFA | 2 (1.3) | 1 (3.0) | 1 (1.4) | 0 (0) | 0.350 |
MWA | 1 (0.7) | 0 (0) | 0 (0) | 1 (2.0) | |
TARE | 11 7.3) | 2 (6.1) | 6 (8.7) | 3 (6.1) | |
PEI | 4 (2.6) | 0 (0) | 1 (1.4) | 3 (6.1) | |
INR, median (IQR) | 1.2 (0.2) | 1.2 (0.3) | 1.2 (0.3) | 1.1 (0.2) | 0.168 |
Bilirubin (mg/dL), median (IQR) | 1.0 (0.9) | 1.2 (1.5) | 0.9 (0.9) | 1.0 (0.8) | 0.449 |
ALT (U/L), median (IQR) | 41.5 (38.3) | 39.0 (42.5) | 39.0 (33.0) | 38.0 (33.0) | 0.274 |
AST (U/L), median (IQR) | 57.5 (42.5) | 51.0 (41.0) | 58.0 (48.0) | 50.0 (42.0) | 0.329 |
ALP (U/L), median (IQR) | 128.5 (97.3) | 130.0 (101.0) | 122.0 (95.0) | 117.0 (85.0) | 0.226 |
GGT (U/L), median (IQR) | 140.0 (193.5) | 84.0 (168.5) | 173.0 (151.0) | 117.0 (291.0) | 0.490 |
AFP (ng/mL), median (IQR) | 16.4 (167.6) | 8.4 (133.3) | 17.6 (277.6) | 17.8 (67.5) | 0.486 |
Imaging features and procedural data | |||||
Indication for TACE | |||||
Bridging/down-staging | 87 (57.6) | 18 (54.4) | 36 (52.2) | 33 (67.3) | 0.239 |
Non-bridging/down-staging | 64 (42.4) | 15 (45.5) | 33 (47.8) | 16 (32.7) | |
Hepatic tumor burden | |||||
0–25% | 133 (88.1) | 27 (81.8) | 60 (87.0) | 46 (93.9) | 0.309 |
26–50% | 16 (10.6) | 6 (18.2) | 8 (11.6) | 2 (4.1) | |
>50% | 2 (1.3) | 0 (0) | 1 (1.4) | 1 (2.0) | |
Portal vein invasion | 9 (6.0) | 4 (12.1) | 3 (4.3) | 2 (4.1) | 0.239 |
Within Milan criteria | 49 (32.5) | 6 (18.2) | 22 (31.9) | 21 (42.9) | 0.105 |
Sum of target lesion diameter (mm), median (IQR) | 39.1 (31.5) | 34.4 (19.9) | 44.5 (36.5) | 37.0 (30.3) | 0.137 |
Catheter application position | |||||
Unselective | 73 (48.3) | 14 (42.2) | 31 (44.9) | 28 (57.1) | 0.102 |
Selective | 51 (33.7) | 14 (42.4) | 23 (33.3) | 14 (28.6) | |
Superselective | 27 (17.9) | 5 (15.1) | 15 (21.7) | 7 (14.3) | |
Follow-up | |||||
Response at 4–6-week follow-up | |||||
CR/PR | 72 (47.7) | 10 (30.3) a | 39 (56.5) b | 23 (46.9) a,b | 0.046 |
SD/PD | 79 (52.3) | 23 (69.7) a | 30 (43.5) b | 26 (53.1) a,b |
Parameter | Regression Coefficient | Odds Ratio |
---|---|---|
Contrast | 1.75808882 | 5.8013394 |
BCLC (B) | −0.08530392 | 0.9182332 |
Etiology of liver disease (viral hepatitis) | −0.29781417 | 0.7424393 |
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Masthoff, M.; Irle, M.; Kaldewey, D.; Rennebaum, F.; Morgül, H.; Pöhler, G.H.; Trebicka, J.; Wildgruber, M.; Köhler, M.; Schindler, P. Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma. Cancers 2025, 17, 893. https://doi.org/10.3390/cancers17050893
Masthoff M, Irle M, Kaldewey D, Rennebaum F, Morgül H, Pöhler GH, Trebicka J, Wildgruber M, Köhler M, Schindler P. Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma. Cancers. 2025; 17(5):893. https://doi.org/10.3390/cancers17050893
Chicago/Turabian StyleMasthoff, Max, Maximilian Irle, Daniel Kaldewey, Florian Rennebaum, Haluk Morgül, Gesa Helen Pöhler, Jonel Trebicka, Moritz Wildgruber, Michael Köhler, and Philipp Schindler. 2025. "Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma" Cancers 17, no. 5: 893. https://doi.org/10.3390/cancers17050893
APA StyleMasthoff, M., Irle, M., Kaldewey, D., Rennebaum, F., Morgül, H., Pöhler, G. H., Trebicka, J., Wildgruber, M., Köhler, M., & Schindler, P. (2025). Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma. Cancers, 17(5), 893. https://doi.org/10.3390/cancers17050893