Defining the high-risk category of patients with cutaneous melanoma: a practical tool based on prognostic modeling

The developed model, which relies on routine histological features, allows the identification of individuals at high risk of CM recurrence to tailor their further management.

Authors

Institutions

Publication Details

Journal
Frontiers in Molecular Biosciences
Published
2025-02-07
DOI
https://doi.org/10.3389/fmolb.2025.1543148
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Defining the high-risk category of patients with cutaneous melanoma: a practical tool based on prognostic modeling

Sofiia Livshun, О. П. Мінцер, Oleksandr Dudin, Vitaliy Gurianov et al.
Frontiers in Molecular Biosciences
Cutaneous Melanoma Detection and Management
article

Defining the high-risk category of patients with cutaneous melanoma: a practical tool based on prognostic modeling

Sofiia Livshun, О. П. Мінцер, Oleksandr Dudin, Vitaliy Gurianov, Denys Kozakov, Nazarii M. Kobyliak, Oksana Sulaieva
article en

Abstract

The developed model, which relies on routine histological features, allows the identification of individuals at high risk of CM recurrence to tailor their further management.

Frontiers in Molecular BiosciencesVol. 12
Shupyk National Healthcare University of Ukraine (UA), Kyiv Medical University (UA), Bogomolets National Medical University (UA)
Peace, Justice and strong institutions
Openalex Percentile: Top 97%
Cutaneous Melanoma Detection and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.