Clinical and radiomics parameter prognostication in metastatic uveal melanoma patients treated with hepatic arterial infusion chemotherapy.
Authors
Affiliations (11)
Affiliations (11)
- West German Cancer Center, Department of Medical Oncology, University Hospital Essen, University of Duisburg-Essen, 45122, Essen, Germany.
- Bridge Institute of Experimental Tumor Therapy (BIT) and Division of Solid Tumor Translational Oncology (DKTK), West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, 45122, Essen, Germany.
- Institute for Artificial Intelligence and Informatics in Medicine, Technical University of Munich, 81675, Munich, Germany.
- Institute of Radiochemistry and Experimental Molecular Imaging, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937, Cologne, Germany.
- German Cancer Consortium (DKTK), a partnership between German Cancer Research Center (DKFZ) and University Hospital Essen, University of Duisburg-Essen, 45122, Essen, Germany.
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, 45122, Essen, Germany.
- Institute of Diagnostic Radiology, Interventional Radiology and Nuclear Medicine, Berufsgenossenschaftliches Universitätsklinikum Bergmannsheil GmbH, Ruhr University Bochum, Buerkle-de-la-Camp Platz 1, 44789, Bochum, Germany.
- National Center for Tumor Diseases (NCT) West, 45122, Essen, Germany.
- Department of Ophthalmology, University Hospital Essen, University of Duisburg-Essen, 45122, Essen, Germany.
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675, Munich, Germany.
- German Cancer Consortium (DKTK), a partnership between German Cancer Research Center (DKFZ) and School of Medicine, Technical University of Munich, Munich, Germany.
Abstract
Metastatic uveal melanoma (MUM) has a poor prognosis, but hepatic arterial infusion chemotherapy (HAIC) may improve outcomes in patients with hepatic metastases. To identify reliable prognostic factors for patient stratification and treatment allocation, we analyzed the clinical and imaging data from a large single-center cohort using machine learning (ML) models. Pre- and post first treatment clinical data of 235 patients with MUM treated with HAIC between 2009 and 2019 were retrospectively analyzed using Cox regression to identify prognostic factors for overall survival (OS) and time to change treatment strategy (TTCS). Furthermore, ML models were trained on clinical and computed tomography (CT) data for endpoint prediction. Pre-treatment multivariate analysis identified elevated lactate dehydrogenase (LDH) (OS: 6.5 vs. 16.4 months, hazard ratio (HR)=1.87, p = 0.006) and gamma-glutamyl transpeptidase (GGT) (OS: 7.6 vs. 16.4 months, HR = 1.67, p = 0.012) as prognostic factors for inferior OS. Decreased albumin (TTCS: 1.3 vs. 6.1 months, HR = 6.26, p < 0.001) and elevated LDH (TTCS: 2.9 vs. 7.6 months, HR = 1.72, p = 0.011) and alanine aminotransferase (ALT) (TTCS: 3.7 vs. 6.4 months, HR = 1.65, p = 0.004) predicted shorter TTCS. Scoring enhanced the power of the prognosticators for OS and TTCS. Post first treatment multivariate analysis emphasized the importance of inflammation management and liver protection. ML models incorporating radiomics features from base line CT imaging were not superior to models based on pre-treatment clinical data alone. We identified independent but synergistic prognostic factors for outcome stratification to guide treatment decisions and optimize patient management. ML-based radiomics features did not significantly enhance prognostic performance.