RT Book, Section
A1 Navarrete-Dechent, Cristian
A1 Kose, Kivanc
A1 Reiter, Ofer
A1 Yamada, Miko
A1 Prow, Tarl
A1 Balu, Mihaela
A1 Wortsman, Ximena
A1 Wu, Yu-Hung
A1 Jain, Manu
A2 Nouri, Keyvan
SR Print(0)
ID 1194729268
T1 New Approaches in the Diagnosis of Skin Cancer
T2 Skin Cancer: A Comprehensive Guide
YR 2023
FD 2023
PB McGraw-Hill Education
PP New York, NY
SN 9781260453003
LK dermatology.mhmedical.com/content.aspx?aid=1194729268
RD 2024/04/18
AB SUMMARYTraditionally,  the  diagnosis  of  skin  cancer  relies  on  physical  examination  (ie,  with  the  unaided  eye)  and  is  often  followed  by  a  biopsy  of  suspicious  lesions.  Although  histopathological  examination  is  the  gold  standard  for  skin  cancer  diagnosis,  it  is  an  invasive  procedure  associated  with  potential  complications,  such  as  pain,  infection,  bleeding,  scarring,  and  hypo-  and  hyperpigmentation,  among  others.Recently,  many  noninvasive  imaging  techniques  have  been  developed  to  overcome  the  existing  limitations.  These  techniques  include  optical/light-based  imaging  techniques  (such  as  dermoscopy,  reflectance  confocal  microscopy  [RCM],  and  optical  coherence  tomography  [OCT])  and  nonoptical  imaging  techniques  (such  as  ultrasonography  and  magnetic  resonance  imaging),  among  others.In  this  chapter,  we  will  first  review  noninvasive  imaging  devices  that  are  being  used  in  daily  clinical  practice.  Second,  we  will  discuss  imaging  modalities  that  are  being  currently  investigated  and  that  we  anticipate  will  be  used  in  the  future.Major  limitations  for  the  widespread  adaptation  of  noninvasive  imaging  devices  are  their  cost,  the  learning  curve  for  reading  the  images,  and  the  lack  of  teaching-training  programs.  In  the  future,  the  use  of  artificial  intelligence  (AI)  algorithms  might  be  an  aid  towards  global  utilization  and  acceptance.  We  envision  that  a  combination  of  noninvasive  imaging  aided  by  AI  diagnosis  has  the  potential  to  create  a  major  paradigm  shift  in  dermatology  practice  and  patient  care.