Login / Signup

Operational classification of cutaneous squamous cell carcinomas based on unsupervised clustering of real cases by experts.

C Gaudy-MarquesteJ J GrobClaus GarbeP A AsciertoS ArronN Basset-SeguinA S BohneC LenoirReinhard DummerMaria-Concetta FargnoliA GuminskiA HauschildRoland KaufmannAimillios LallasV Del MarmolM MigdenM PenicaudA RembielakA StratigosL TagliaferriI ZalaudekA AranceD BadinandPaolo BossiAmarnath ChallapalliM ClementiA Di StefaniC Ferrándiz-PulidoRoberta GiuffridaG L GravinaP HaL HeinzerlingS MalletA ParadisiP MohrAlfredo PiccerilloD RutkowskiP SaiagP SollenaMyrto TrakatelliP WojcieszekS S YomEnrico ZelinKetty PerisJ Malvehy
Published in: Journal of the European Academy of Dermatology and Venereology : JEADV (2024)
Given the methodology based on the convergence of unguided intuitive clustering of cases by experts, this new classification is relevant for clinical practice. It does not compete with staging systems, but they may complement each other, whether the objective is to select the best therapeutic approach in tumour boards or to design homogeneous groups for trials.
Keyphrases
  • machine learning
  • squamous cell
  • clinical practice
  • deep learning
  • single cell
  • rna seq
  • lymph node
  • high grade
  • pet ct