Benign Melanocytic Tumors of the Uvea

Materin, Miguel A.; Singh, Arun D.

doi:10.1007/978-3-030-17879-6_3

Cited by 4 publications

(6 citation statements)

References 129 publications

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“…The lack of a well-defined size-based classification system is the primary reason for inconsistent estimates of risk of malignant growth among studies as they have all used varying inclusion criteria [20, 21]. Another problem is bias caused by retrospective data collection, except for studies by Gass and the COMS group [22, 23].…”

Section: Lack Of Universal Size Criteria/definitionmentioning

confidence: 99%

“…The presence of orange pigment and subretinal fluid as markers of acute change favors a diagnosis of melanoma, whereas drusen and intraretinal cystic spaces, being signs of chronicity associated with a long-standing stable lesion, are likely to indicate a benign lesion (such as nevus) [21]. Risk factors are more informative when assessed in combination than when they are considered individually [26, 27].…”

Section: Secondary Effects On Adjacent Tissuesmentioning

confidence: 99%

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What’s in a Name? Large Choroidal Nevus, Small Choroidal Melanoma, or Indeterminate Melanocytic Tumor

Singh

Grossniklaus

2021

Ocul Oncol Pathol

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Section: Lack Of Universal Size Criteria/definitionmentioning

confidence: 99%

Section: Secondary Effects On Adjacent Tissuesmentioning

confidence: 99%

What’s in a Name? Large Choroidal Nevus, Small Choroidal Melanoma, or Indeterminate Melanocytic Tumor

Singh

Grossniklaus

2021

Ocul Oncol Pathol

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“…The presence of orange pigment and subretinal fluid (SRF) favors a diagnosis of a small choroidal melanoma, whereas drusen and retinal pigment epithelium (RPE) changes are likely to indicate a benign lesion (such as nevus). Although these “risk factors” have consistently been identified as significant predictors of growth [15], it is worth emphasizing that the growth “risk factors” carry externally unvalidated probabilities that have limited their clinical application in prediction of small choroidal melanoma [8].…”

Section: Introductionmentioning

confidence: 99%

“…In absence of diagnostic biopsy or documented growth [16, 17], the diagnosis is generally relied upon presence of “risk factors” predictive of growth in the future [7-12, 15]. What is pertinent however is the presence or absence of malignant growth at initial presentation.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model to Discriminate Small Choroidal Melanoma from Choroidal Nevus

et al. 2021

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Objective: To develop a validated machine learning model to diagnose small choroidal melanoma. Design: Cohort study Subjects, Participants, and/or Controls: The training data included 123 patients diagnosed as small choroidal melanocytic tumor (5.0-16.0 mm in largest basal diameter and 1.0 mm to 2.5 mm in height; Collaborative Ocular Melanoma Study criteria). Those diagnosed as melanoma (n=61) had either documented growth or pathologic confirmation. 62 patients with stable lesions classified as choroidal nevus, were used as negative controls. The external validation data set included 240 patients managed at a different tertiary clinic, also with small choroidal melanocytic tumor, observed for malignant growth. Methods: In the training data, lasso logistic regression was used to select variables for inclusion in the final model for the association with melanoma versus choroidal nevus. Internal and external validation were performed to assess model performance. Main Outcome Measures: Predicted probability of small choroidal melanoma Results: Distance to optic disc ≥3mm and drusen were associated with decreased odds of melanoma whereas male versus female sex, increased height, subretinal fluid, and orange pigment were associated with increased odds of choroidal melanoma. The area under the receiver operating characteristic (AUROC) “discrimination value” for this model was 0.880. The top four variables that were most frequently selected for inclusion in the model on internal validation, implying their importance as predictors of melanoma, were subretinal fluid, height, distance to optic disc, and orange pigment. When tested against the validation data, the prediction model could distinguish between choroidal nevus and melanoma with high discrimination of 0.861. The final prediction model was converted into an online calculator to generate predicted probability of melanoma. Conclusions: To minimize diagnostic uncertainty, a machine learning based diagnostic prediction calculator can be readily applied for decision making and counselling patients with small choroidal melanoma.

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“…The majority of tumors (69%, Kaplan-Meier estimate at 5 years) labeled as “SCM” within the COMS Small Tumor Study included tumors with clinical behavior that was compatible with the diagnosis of choroidal nevus [4, 5]. Therefore, growth over time is commonly used to differentiate SCM from choroidal nevus [6-9].…”

Section: Introductionmentioning

confidence: 99%

Small Choroidal Melanoma: Correlation of Growth Rate with Pathology

Raval¹,

Luo²,

Zabor³

et al. 2021

Ocul Oncol Pathol

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Purpose: The aim of the study was to evaluate equivalence of growth rate and pathologic confirmation in small choroidal melanoma (SCM). Design: This study is a case series. Subjects, Participants, and Controls: A total of 61 patients with a choroidal melanocytic tumor of size 5.0–16.0 mm in the largest basal diameter and 1.0–2.5 mm in thickness were classified into the pathology-confirmed group (n = 19), growth-confirmed group (n = 30), and with combined observations (n = 12). Methods: Distribution of clinical variables (age, gender, laterality, tumor dimensions, tumor location, and presence of orange pigment, subretinal fluid, drusen, and retinal pigment epithelial [RPE] atrophy) between the groups was analyzed. Patient and disease characteristics were summarized as the median and interquartile range for continuous variables and the frequency and percentage for categorical variables. Comparisons were made using the Wilcoxon rank sum test for continuous variables and either Fisher’s exact test or the χ2 test for categorical variables with a p value threshold of 0.05 for statistical significance. Growth rate (change in basal dimension/12 months) diagnostic of SCM was quantified. Main Outcome Measures: The primary aim of this study was to test the hypothesis that “growth” was diagnostic of SCM with the secondary aim of quantifying the malignant “growth rate” (growth rate of SCM). Results: The clinical characteristics among all 3 groups were similar except more patients with symptoms (68 vs. 20 vs. 42%, p = 0.004) and juxtapapillary location (p = 0.03) were in the pathology group than in the growth-confirmed group. Those in the combined and growth-confirmed groups had more patients with drusen (11 vs. 60 vs. 50%, p = 0.003) and RPE atrophy (11 vs. 23 vs. 67%, p = 0.003), respectively, than in the pathology group. The median time to detect growth was 9 months (range 3–26 months). The mean growth rate in basal dimension was 1.8 mm/12 months (range, 0.0–7.4 mm; [95% CI: 1.32–2.28]). Conclusions and Relevance: Choroidal melanocytic lesions exhibiting a defined growth rate can be clinically diagnosed as SCM without a need for biopsy.

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Benign Melanocytic Tumors of the Uvea

Cited by 4 publications

References 129 publications

What’s in a Name? Large Choroidal Nevus, Small Choroidal Melanoma, or Indeterminate Melanocytic Tumor

What’s in a Name? Large Choroidal Nevus, Small Choroidal Melanoma, or Indeterminate Melanocytic Tumor

A Prediction Model to Discriminate Small Choroidal Melanoma from Choroidal Nevus

Small Choroidal Melanoma: Correlation of Growth Rate with Pathology

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