Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15-year follow-up study

Uveal Melanoma: Comprehensive Review of Its Pathophysiology, Diagnosis, Treatment, and Future Perspectives

Uveal melanoma (UM) is the most common intraocular malignancy in adults. Recent advances highlight the role of tumor-derived extracellular vesicles (TEV) and circulating hybrid cells (CHC) in UM tumorigenesis. Bridged with liquid biopsies, a novel technology that has shown incredible performance in detecting cancer cells or products derived from tumors in bodily fluids, it can significantly impact disease management and outcome. The aim of this comprehensive literature review is to provide a summary of current knowledge and ongoing advances in posterior UM pathophysiology, diagnosis, and treatment. The first section of the manuscript discusses the complex and intricate role of TEVs and CHCs. The second part of this review delves into the epidemiology, etiology and risk factors, clinical presentation, and prognosis of UM. Third, current diagnostic methods, ensued by novel diagnostic tools for the early detection of UM, such as liquid biopsies and artificial intelligence-based technologies, are of paramount importance in this review. The fundamental principles, limits, and challenges associated with these diagnostic tools, as well as their potential as a tracker for disease progression, are discussed. Finally, a summary of current treatment modalities is provided, followed by an overview of ongoing preclinical and clinical research studies to provide further insights on potential biomolecular pathway alterations and therapeutic targets for the management of UM. This review is thus an important resource for all healthcare professionals, clinicians, and researchers working in the field of ocular oncology.

Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases

OBJECTIVE

To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases.

BACKGROUND

Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and noninvasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize 3-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverage on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases.

CONCLUSION

This review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Artificial intelligence for ocular oncology

PURPOSE OF REVIEW

The aim of this article is to provide an update on the latest applications of deep learning (DL) and classical machine learning (ML) techniques to the detection and prognostication of intraocular and ocular surface malignancies.

RECENT FINDINGS

Most recent studies focused on using DL and classical ML techniques for prognostication purposes in patients with uveal melanoma (UM).

SUMMARY

DL has emerged as the leading ML technique for prognostication in ocular oncological conditions, particularly in UM. However, the application of DL may be limited by the relatively rarity of these conditions.

Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma

PURPOSE

Since molecular assays are not accessible to all uveal melanoma patients, we aim to identify cost-effective prognostic tool in risk stratification using machine learning models based on routine histologic and clinical variables.

EXPERIMENTAL DESIGN

We identified important prognostic parameters in a discovery cohort of 164 enucleated primary uveal melanomas from 164 patients without prior therapies. We then validated the prognostic prediction of top important parameters identified in the discovery cohort using 80 uveal melanomas from the Tumor Cancer Genome Atlas database with available gene expression prognostic signature (GEPS). The performance of three different survival analysis models (Cox proportional hazards (CPH), random survival forest (RSF), and survival gradient boosting (SGB)) was compared against GEPS using receiver operating curves (ROC).

RESULTS

In all three selection methods, BAP1 status, nucleoli size, age, mitotic rate per 1 mm², and ciliary body infiltration were identified as significant overall survival (OS) predictors; and BAP1 status, nucleoli size, largest basal tumor diameter, tumor-infiltrating lymphocyte density, and tumor-associated macrophage density were identified as significant progression-free survival (PFS) predictors. ROC plots for the median survival time point showed that significant parameters in SGB studied model can predict OS better than GEPS. For PFS, SGB model performed similarly to GEPS. The time-dependent area under the curve (AUC) showed SGB model performing better than GEPS in predicting OS and metastatic risk.

CONCLUSIONS

Our study shows that routine histologic and clinical variables are adequate for patient risk stratification in comparison with not readily accessible GEPS.

TRPM4 and TRPV2 are two novel prognostic biomarkers and promising targeted therapy in UVM

Uveal melanoma (UVM) is the most common primary intraocular malignancy tumor in adults. Almost 50% of UVM patients develop metastatic disease, and is usually fatal within 1 year. However, the mechanism of etiology remains unclear. The lack of prognostic, diagnostic and therapeutic biomarkers is a main limitation for clinical diagnosis and treatment. The transient receptor potential (TRP) channels play important roles in the occurrence and development of tumors, which may have the potential as a therapeutic target for UVM. This current study aimed to identify the potential effect and function of the TRPs that could provide survival prediction and new insight into therapy for UVM. Based on the transcriptome data and potential key genes of UVM were screened using the Cancer Genome Atlas (TCGA) databases, Gene expression analysis showed the expression of TRPM4, TRPV2 and other TRPs was high levels in UVM. Using survival analysis, we screened out that the high expression of TRPM4 and TRPV2 was negatively correlated with the prognosis of UVM patients. Cox regression analysis and functional enrichment analysis further indicated that TRPM4 and TRPV2 were the most convincing therapeutic targets of UVM, and the majority of genes involved in ferroptosis pathways in UVM showed positively correlated with the expression levels of TRPM4 and TRPV2. In conclusion, TRPM4 and TRPV2 were considered as two novel prognostic biomarkers and a promising targeted therapy in UVM.

Characteristics, Treatments, and Survival of Uveal Melanoma: A Comparison between Chinese and American Cohorts

Simple Summary

This retrospective comparative cohort study aimed to determine whether there were racial or national differences in UM, by comparing the demographic and clinical characteristics, such as tumor size, onset age, trend and proportion of treatment modalities, and overall survival. In the two cohorts, we found that Chinese patients have a younger onset age and a better survival rate. The survival advantage was likely secondary to younger onset age. In addition, a greater proportion of Chinese patients received brachytherapy as opposed to enucleation compared with American patients. This study was the first time comparing patients from different countries and races, which may help ophthalmologists better understand the clinical characteristics of the disease and suggests the importance of early diagnosis and treatment.

Abstract

Uveal melanoma (UM) is the most common intraocular malignant carcinoma. This study aimed to compare the clinical features, treatment modalities, and prognosis of UM patients in China with those in America over a 15-year period. In the study, 4088 American patients with primary UM from the Surveillance, Epidemiology, and End Results (SEER) database and 1508 Chinese patients from Tongren-ophthalmology Research Association of Clinical Evaluation (TRACE) were included. Univariable and multivariable analyses were performed to determine prognostic factors and propensity score matching (PSM) and sensitivity analyses were applied to adjust for confounders and identify independent prognostic factors. Chinese patients were diagnosed at a younger age (mean ± SD, 47.3 ± 12.5 years vs. 59.7 ± 14.8 years) and tumors at diagnosis were larger (diameter: 12.0 ± 3.54 mm vs. 11.3 ± 8.27 mm; thickness: 7.13 ± 3.28 mm vs. 4.91 ± 3.01 mm). Chinese patients were more likely to undergo brachytherapy than American patients. Chinese patients had better overall survival than American patients while no significant differences exhibited after adjusting for age through PSM. In conclusion, compared with American patients, Chinese patients had younger onset age, larger tumors at diagnosis and better prognosis, mainly because of their younger age.

Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning

INTRODUCTION

Uveal melanoma (UM) is the most common intraocular malignancy in adults. Plaque brachytherapy remains the dominant eyeball-conserving therapy for UM. Tumor regression in UM after plaque brachytherapy has been reported as a valuable prognostic factor. The present study aimed to develop an accurate machine-learning model to predict the 4-year risk of metastasis and death in UM based on ocular ultrasound data.

MATERIAL AND METHODS

A total of 454 patients with UM were enrolled in this retrospective, single-center study. All patients were followed up for at least 4 years after plaque brachytherapy and underwent ophthalmologic evaluations before the therapy. B-scan ultrasonography was used to measure the basal diameters and thickness of tumors preoperatively and postoperatively. Random Forest (RF) algorithm was used to construct two prediction models: whether a patient will survive for more than 4 years and whether the tumor will develop metastasis within 4 years after treatment.

RESULTS

Our predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.708 for predicting death using only a one-time follow-up record. Including the data from two additional follow-ups increased the AUC of the model to 0.883. We attained AUCs of 0.730 and 0.846 with data from one and three-time follow-up, respectively, for predicting metastasis. The model found that the amount of postoperative follow-up data significantly improved death and metastasis prediction accuracy. Furthermore, we divided tumor treatment response into four patterns. The D(decrease)/S(stable) patterns are associated with a significantly better prognosis than the I(increase)/O(other) patterns.

CONCLUSIONS

The present study developed an RF model to predict the risk of metastasis and death from UM within 4 years based on ultrasound follow-up records following plaque brachytherapy. We intend to further validate our model in prospective datasets, enabling us to implement timely and efficient treatments.