Ocular melanoma consists of posterior uveal melanoma, iris melanoma and conjunctival melanoma. These malignancies derive from melanocytes in the uveal tract or conjunctiva. The genetic profiles of these different entities differ from each other. In uveal melanoma, GNAQ and GNA11 gene mutations are frequently found and prognosis is based on mutation status of BAP1, SF3B1 and EIF1AX genes. Iris melanoma, also originating from the uvea, has similarities to the genetic makeups of both posterior uveal melanoma (UM) and conjunctival melanoma since mutations in GNAQ and GNA11 are less common and genes involved in conjunctival melanoma such as BRAF have been described. The genetic spectrum of conjunctival melanoma, however, includes frequent mutations in the BRAF, NRAS and TERT promoter genes, which are found in cutaneous melanoma as well. The BRAF status of the tumor is not correlated to prognosis, whereas the TERT promoter gene mutations are. Clinical presentation, histopathological characteristics and copy number alterations are associated with survival in ocular melanoma. Tissue material is needed to classify ocular melanoma in the different subgroups, which creates a need for the use of noninvasive techniques to prognosticate patients who underwent eye preserving treatment.

IntroductionMany authors have reported on a myopic post-operative refractive prediction error when combining phacoemulsification with pars plana vitrectomy (phacovitrectomy). In this study we evaluate the amount of this error in our facility and try to elucidate the various factors involved.MethodsThis was a retrospective study which included 140 patients who underwent phacovitrectomy (39 with macular holes, 88 with puckers, and 13 with floaters). Post-operative refractive error was defined as the difference between the actual spherical equivalent (SEQ) and expected SEQ based on the SRK/T and Holladay-II formulas. Both univariate (paired t test, independent t test, one-way analysis of variance, or Mann–Whitney test) and multivariate (regression analysis) statistical analyses were performed.ResultsOverall, a refractive error of − 0.13 dpt (p = 0.033) and − 0.26 dpt (p < 0.01) were found in the SRK/T and Holladay-II formulas, respectively. For the independent diagnoses, only macular holes showed a myopic error with the SRK/T (− 0.31 dpt; p < 0.01) and Holladay-II (− 0.44 dpt; p < 0.01) formulas. In univariate analysis, significant factors involved in myopic refractive error were macular hole as diagnosis (p < 0.01 for SRK/T and Holladay-II), gas tamponade (SRK/T p = 0.024; Holladay-II p = 0.025), pre-operative myopia (p < 0.01 for SRK/T), and optical technique for axial length measurement (SRK/T and Holladay-II p < 0.01). In the multivariate analysis, pre-operative axial length (p = 0.026), optical technique for axial length measurement (p < 0.01), and pre-operative SEQ (p < 0.01) were independent predictors for myopic refractive error in the SRK/T formula. For the Holladay-II formula, optical technique for axial length measurement (p < 0.01) and pre-operative SEQ (p = 0.04) were predictive.ConclusionVarious factors are involved in determining the myopic refractive error after phacovitrectomy. Not every factor seems to be as important in each individual patient, suggesting a more tailored approach is warranted to overcome this problem.

Uveal melanoma (UM) is the second most frequent type of melanoma. Therapeutic options for UM favor minimally invasive techniques such as irradiation for vision preservation. As a consequence, no tumor material is obtained. Without available tissue, molecular analyses for gene expression, mutation or copy number analysis cannot be performed. Thus, proper patient stratification is impossible and patients’ uncertainty about their prognosis rises. Minimally invasive techniques have been studied for prognostication in UM. Blood-based biomarker analysis has become more common in recent years; however, no clinically standardized protocol exists. This review summarizes insights in biomarker analysis, addressing new insights in circulating tumor cells, circulating tumor DNA, extracellular vesicles, proteomics, and metabolomics. Additionally, medical imaging can play a significant role in staging, surveillance, and prognostication of UM and is addressed in this review. We propose that combining multiple minimally invasive modalities using tumor biomarkers should be the way forward and warrant more attention in the coming years.