Evaluation of CT vascularization patterns for survival prognosis in patients with hepatocellular carcinoma treated by conventional TACE

Hasdemir, D.; Dávila, Lukas Aguirre; Schweitzer, Nóra; Meyer, Bernhard; Koch, Armin; Vogel, Arndt; Wacker, F; Rodt, Thomas

doi:10.5152/dir.2016.16006

Cited by 15 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Miszczuk et al 16 prospectively enrolled 39 liver cancer patients (n = 22, HCC) treated with TACE and they could show, that high Lipiodol coverage on the 24-h post-TACE CT was associated with response to therapy. Lipiodol retention may serve as a surrogate for arterial hyperenhancement 16 , the vascularization pattern of HCC lesions might have prognostic impact 34 and our results provide quantitative corroboration of these findings. In our model, the GLDM feature LargeDependenceHighGrayLevelEmphasis, which depends on higher gray-level values ( https://pyradiomics.readthedocs.io/ ), had the highest predictive impact.…”

Section: Discussionsupporting

confidence: 77%

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

Bernatz

Elenberger

Ackermann

et al. 2023

Sci Rep

View full text Add to dashboard Cite

We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55–0.67. Clinical scores revealed top AUCs of 0.65–0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41–0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.

show abstract

Section: Discussionsupporting

confidence: 77%

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

Bernatz

Elenberger

Ackermann

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Radiomics analysis on arterial phase image was useful for prognosis prediction. This may be explained by that tumor texture patterns in arterial phase imaging could reflect tumor vascularization patterns, which was helpful for prognosis prediction ( 33 ). There may be two reasons explaining the importance of radiomics features from the portal venous images.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization

Meng

Wang

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort ( n = 108) and a testing cohort ( n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.

show abstract

“…Similarly, in most previous studies laboratory indicators of liver function besides AFP were not found to be significant. In terms of the preoperative pattern of tumor vascularization, Hasdemir et al 30 indicated that the pattern of vascularization of the largest tumor could predict survival after TACE ( P = 0.012). Nevertheless, in our study, this result was not be found.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Dai¹,

Jiang²,

Feng³

et al. 2022

JHC

View full text Add to dashboard Cite

Purpose This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE. Patients and Methods The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test. Results The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively ( P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort. Conclusion Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.

show abstract

Evaluation of CT vascularization patterns for survival prognosis in patients with hepatocellular carcinoma treated by conventional TACE

Abstract: The vascularization pattern of the largest HCC lesion is helpful for survival prognosis under TACE treatment and therefore has the potential to be used as an additional parameter for treatment stratification.

Cited by 15 publications

References 30 publications

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Contact Info

Product

Resources

About