A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker. Issue 11 (21st October 2021)
- Record Type:
- Journal Article
- Title:
- A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker. Issue 11 (21st October 2021)
- Main Title:
- A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker
- Authors:
- Ivanics, Tommy
Salinas-Miranda, Emmanuel
Abreu, Phillipe
Khalvati, Farzad
Namdar, Khashayar
Dong, Xin
Deniffel, Dominik
Gorgen, Andre
Erdman, Lauren
Jhaveri, Kartik
Haider, Masoom
Veit-Haibach, Patrick
Sapisochin, Gonzalo - Abstract:
- Abstract : Supplemental Digital Content is available in the text. Abstract : Background: Despite transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomics model, an imaging-based tool to predict these adverse outcomes. Methods: We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a least absolute shrinkage and selection operator feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models' performance was compared using 5-fold cross-validated area under the receiver operating characteristic curves. Results: . Eighty-eight patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 mo (interquartile range: 3.6–9.3), and the median time for post-LT recurrence was 19.2 mo (interquartile range: 6.1–34.0). Twenty-four patients (27.3%) dropped out and 64 (72.7%) patients were transplanted. OfAbstract : Supplemental Digital Content is available in the text. Abstract : Background: Despite transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomics model, an imaging-based tool to predict these adverse outcomes. Methods: We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a least absolute shrinkage and selection operator feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models' performance was compared using 5-fold cross-validated area under the receiver operating characteristic curves. Results: . Eighty-eight patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 mo (interquartile range: 3.6–9.3), and the median time for post-LT recurrence was 19.2 mo (interquartile range: 6.1–34.0). Twenty-four patients (27.3%) dropped out and 64 (72.7%) patients were transplanted. Of these, 14 (21.9%) had recurrence post-LT. Model performance yielded a mean area under the receiver operating characteristic curves of 0.70 (±0.07), 0.87 (±0.06), and 0.81 (±0.06) for the arterial, venous, and the combined models, respectively. Conclusions: A pre-TACE radiomics model for HCC patients undergoing LT may be a useful tool for outcome prediction. Further external model validation with a larger sample size is required. … (more)
- Is Part Of:
- Transplantation. Volume 105:Issue 11(2021)
- Journal:
- Transplantation
- Issue:
- Volume 105:Issue 11(2021)
- Issue Display:
- Volume 105, Issue 11 (2021)
- Year:
- 2021
- Volume:
- 105
- Issue:
- 11
- Issue Sort Value:
- 2021-0105-0011-0000
- Page Start:
- 2435
- Page End:
- 2444
- Publication Date:
- 2021-10-21
- Subjects:
- Transplantation of organs, tissues, etc -- Periodicals
Transplantation immunology -- Periodicals
617.95 - Journal URLs:
- http://journals.lww.com/pages/default.aspx ↗
- DOI:
- 10.1097/TP.0000000000003605 ↗
- Languages:
- English
- ISSNs:
- 0041-1337
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9024.990000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 19655.xml