# Clinical and radiomics parameter prognostication in metastatic uveal melanoma patients treated with hepatic arterial infusion chemotherapy

## TL;DR

This study finds clinical markers that predict survival in patients with metastatic uveal melanoma treated with a specific chemotherapy method.

## Contribution

The study introduces a scoring system combining clinical factors to improve outcome prediction in metastatic uveal melanoma patients.

## Key findings

- Elevated LDH and GGT levels are linked to worse survival in metastatic uveal melanoma patients.
- Low albumin and elevated ALT levels predict shorter time to treatment change.
- Radiomics features from CT scans did not improve prognostic accuracy beyond clinical data.

## 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 multi-variate 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 multi-variate analysis emphasized the importance of inflammation management and liver protection. ML models incorporating radiomics features from baseline 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.