Prognostication of metastatic death in uveal melanoma patients: A Markov multi-state model

doi:10.1016/j.compbiomed.2018.09.024

Computers in Biology and Medicine

Volume 102, 1 November 2018, Pages 151-156

https://doi.org/10.1016/j.compbiomed.2018.09.024 Get rights and content

Abstract

Background/aims

Uveal melanoma is fatal in almost 50% of patients. We previously developed a prognostic model to predict all-cause mortality. The aim of this study was to improve our model by predicting metastatic death as a cause-specific event distinct from other causes of death.

Methods

Patients treated in Liverpool were included if they resided in England, Scotland or Wales and if their uveal melanoma involved the choroid. They were flagged at the National Health Service Cancer Registry, which automatically informed us of the date and cause of death of any deceased patients. A semiparametric Markov multi-state model was fitted. Two different baseline hazard rates were assumed, with state transition-specific covariates. For both failure types, age at treatment and sex were used. For the metastatic death case, these factors were added: anterior margin position, largest basal tumour diameter, tumour thickness, extra-ocular extension, presence of epithelioid melanoma cells, presence of closed connective tissue loops, increased mitotic count, chromosome 3 loss, and chromosome 8q gain. Missing data required a multiple-imputation procedure.

Results

The cohort comprised 4161 patients, 893 of whom died of metastastic disease with another 772 dying of other causes. The optimism-corrected, bootstrapped C-index for metastatic death prediction was 0.86, denoting very good discriminative performance. Bootstrapped calibration curves at two and five years also showed very good performance.

Conclusions

Our improved model provides reliable, personalised metastatic death prognostication using clinical, histological and genetic information, and it can be used as a decision support tool to individualize patient care in a clinical environment.

Introduction

Hepatic metastases are the primary cause of death in patients with uveal melanoma; however, tumour dissemination is only rarely detectable at the time of primary ocular treatment. There is a need for prognostic tools to estimate the risk of metastatic death and to predict when this might happen. If sufficiently reliable, such tools would enable medical care to be personalized, so that patients with a low risk of metastasis can be reassured while targeting special measures, such as counselling and systemic surveillance, at those who are likely to succumb to their disease. Since many patients with uveal melanoma are elderly, estimation of time to metastasis helps to predict whether death is likely to be caused by their uveal melanoma or by unrelated disease(s).

We previously developed a prognostic tool, the Liverpool Uveal Melanoma Prognosticator Online (LUMPO) model, that estimates all-cause mortality [1]; however, such an endpoint is not ideal for the following reasons: the cause of death is not usually difficult to ascertain; death from unrelated disease or age is common; and treatment or disease-related factors do not increase the risk of death from other causes [2].

The aim of the present study, therefore, was to develop a prognostic model of metastatic death.

Section snippets

The data

The model was developed with data from 4161 patients treated for uveal melanoma at the Liverpool Ocular Oncology Centre. Patients were included in the study if they resided in England, Scotland or Wales, and if their tumour involved the choroid. Diagnosis was based on clinical findings and, if these were inconclusive, on morphological examination of a biopsy. Tumour location and intraocular spread were determined by ophthalmoscopy and slit-lamp examination. Tumour dimensions were measured by

Results

Fig. 1 shows the cumulative incidence functions of metastatic death versus death from other causes. The curves show that the probability of metastatic death exceeds the probability of death from unrelated disease up to about 18 years post treatment; from this point onward, the latter predominates.

In Table 3, we report the averaged coefficients (over the ten fitted models) for the two causes of death, with Wald statistics p-values and hazard ratios (and attendant 95% confidence intervals.) The

Discussion

The C-index and the calibration curves indicate that our model provides reliable, personalised metastatic death prognostication.

The main strengths of our study are the large number of patients, the abundant genetic and histological data, the long follow-up and the accurate reporting mortality. The main weakness is the missing histological and genetic data in a significant proportion of patients. This is because methods for genetic analysis of small tumours samples and techniques for biopsy of

Conflicts of interest

None declared.

Acknowledgments

Funding: This work was supported by The Eye Tumour Research Fund of the Royal Liverpool University Hospital Trust.

The authors also thank the anonymous referees for their comments, which helped improve the presentation of the manuscript.

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Citation Excerpt :
The metastatic rate of 50% is reached in 10 years in studies using the Kaplan-Meier analysis, which ignore competing causes of death, and in 25 years if competing causes of death are taken into account (Kujala et al., 2003). Therefore, the former studies exaggerate the real-life risk of metastasis-related death (Fig. 6) (Bergman et al., 2003; Eleuteri et al., 2018; Gooley et al., 2001; Kujala et al., 2003). Kaplan-Meier analysis and Cox regression are designed to presume that uveal melanoma would be the only possible cause of death, because their basic assumption is that the risk of dying of metastasis is the same before and after censoring, but obviously, a patient cannot die of metastases when they were censored for already having died of other causes (Aalen et al., 2008).
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Prognostication of metastatic death in uveal melanoma patients: A Markov multi-state model

Abstract

Background/aims

Methods

Results

Conclusions

Introduction

Section snippets

The data

Results

Discussion

Conflicts of interest

Acknowledgments

Prog. Retin. Eye Res.

Ophthalmology

Patterns of BAP1 protein expression provide insights into prognostic significance and the biology of uveal melanoma

J. Pathol.: Clin. Res.

MRI in the detection of hepatic metastases from high-risk uveal melanoma: a prospective study in 188 patients

Br. J. Ophthalmol.

Modeling Survival Data: Extending the Cox Model

Mstate: Data Preparation, Estimation and Prediction in Multi-State Models. Version 0.2.10 [software]

Survival and Event History Analysis: a Process Point of View