AIMC Topic: Melanoma, Cutaneous Malignant
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Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning.
Medical physics
Sep 18, 2023
BACKGROUND: Cutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.
A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.
Journal of cancer research and clinical oncology
Sep 7, 2023
PURPOSE: Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM.
Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma.
Photodiagnosis and photodynamic therapy
Jul 22, 2023
BACKGROUND: Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becom...
Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.
Current oncology (Toronto, Ont.)
Jun 23, 2023
Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a...
Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases.
EBioMedicine
Jun 7, 2023
BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic a...
Gene-environment interaction analysis via deep learning.
Genetic epidemiology
Feb 19, 2023
Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In...
Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma.
Sensors (Basel, Switzerland)
Jan 13, 2023
This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutiona...
A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients.
Scientific reports
Nov 27, 2022
The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival...
Application of Deep Learning on the Prognosis of Cutaneous Melanoma Based on Full Scan Pathology Images.
BioMed research international
Aug 28, 2022
INTRODUCTION: The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous m...
Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model.
The American Journal of dermatopathology
Jul 19, 2022
OBJECTIVE: The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocytic lesions, directly patient care. The objective of this study was to create and evaluate the perfo...
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