Purpose: To evaluate the clinical features of choroidal nevi based on patient age at presentation and to investigate features of the nevi that are predictive of patient symptoms.
Design: Observational case series.
Participants: Three thousand four hundred twenty-two consecutive eyes of 3187 patients.
Methods: Retrospective clinic-based study of clinical features at referral. Cox proportional hazards regressions were used for evaluation of factors predictive of patient symptoms.
Main Outcome Measures: Nevus features as related to patient age group at diagnosis (young [< or =20 years], mid-adult [21-50 years], older adult [>50 years]) and factors predictive of patient symptoms secondary to the nevus.
Results: Of the 3422 eyes with choroidal nevus, 63 (2%) were in young patients, 795 (23%) in mid-adults, and 2564 (75%) in older adults. The following factors showed no substantial increase or decrease by age category (young, mid-adult, older adult) at presentation: symptoms (14%, 12%, 13%), mean nevus base (5.6, 4.7, 5.2 mm), intrinsic nevus pigmentation (89%, 74%, 77%), related subretinal fluid (SRF) (11%, 15%, 9%), overlying orange pigment (6%, 10%, 6%), retinal pigment epithelial hyperplasia (0%, 9%, 7%), and retinal pigment epithelial atrophy (2%, 13%, 10%). The following factors statistically increased with age category: multiple nevi per eye (2%, 8%, 10%) (P = 0.0001), mean nevus thickness (1.2, 1.5, 1.6 mm) (P<0.0001), and overlying drusen (11%, 40%, 58%) (P<0.0001). Using multivariate analysis of the entire group, factors predictive of any symptom included nonpigmented nevus (P<0.001), location < or = 3 mm to foveola (P = 0.001), subfoveolar fluid (P = 0.002), any SRF (P = 0.02), and subfoveolar nevus (P = 0.027).
Conclusions: Choroidal nevi show similar clinical features regardless of age of presentation, with the exception of increasing number of nevi per eye, slightly increasing thickness, and increasing drusen in adults versus younger patients. Symptomatic nevi are more likely to be nonpigmented, beneath the foveola, and with subfoveolar fluid.
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http://dx.doi.org/10.1016/j.ophtha.2007.07.009 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Ann Surg Oncol
January 2025
Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Anaplastic thyroid cancer (ATC) is a highly lethal disease, often diagnosed with advanced locoregional and distant metastases, resulting in a median survival of just 3-5 months. This study determines the stratified effectiveness of baseline treatments in all combinations, enabling precise prognoses prediction and establishing benchmarks for advanced therapeutic options.
Methods: The study extracted a cohort of pathologically confirmed ATC patients from the Surveillance, Epidemiology, and End Results program.
Ann Surg Oncol
January 2025
Department of Surgery, National Defense Medical College, Tokorozawa, Saitama, Japan.
Background: Tumor size (TS) in pancreatic ductal adenocarcinoma (PDAC) is one of the most important prognostic factors. However, discrepancies between TS on preoperative images (TSi) and pathological specimens (TSp) have been reported. This study aims to evaluate the factors associated with the differences between TSi and TSp.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFMed Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
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