Papadakis M, Paschos A, Papazoglou AS, Manios A, Zirngibl H, Manios G, Koumaki D. Computer-aided clinical image analysis as a predictor of sentinel lymph node positivity in cutaneous melanoma. World J Clin Oncol 2022; 13(8): 702-711 [PMID: 36160464 DOI: 10.5306/wjco.v13.i8.702]
Corresponding Author of This Article
Marios Papadakis, MD, MSc, PhD, Research Scientist, Surgeon, Department of Surgery II, University of Witten-Herdecke, Heusnerstrasse 40, Wuppertal 42283, Germany. marios_papadakis@yahoo.gr
Research Domain of This Article
Dermatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Oncol. Aug 24, 2022; 13(8): 702-711 Published online Aug 24, 2022. doi: 10.5306/wjco.v13.i8.702
Computer-aided clinical image analysis as a predictor of sentinel lymph node positivity in cutaneous melanoma
Marios Papadakis, Alexandros Paschos, Andreas S Papazoglou, Andreas Manios, Hubert Zirngibl, Georgios Manios, Dimitra Koumaki
Marios Papadakis, Hubert Zirngibl, Department of Surgery II, University of Witten-Herdecke, Wuppertal 42283, Germany
Alexandros Paschos, Department of Dermatology, Helios St. Elisabeth Hospital Oberhausen, Oberhausen 46045, Germany
Andreas S Papazoglou, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
Andreas Manios, Department of Surgical Oncology, University Hospital of Heraklion, Heraklion 71110, Greece
Georgios Manios, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia 35131, Greece
Dimitra Koumaki, Department of Dermatology, University Hospital of Heraklion, Heraklion 71110, Greece
Author contributions: Papadakis M designed the research, performed the research, analyzed the data and wrote the paper; Paschos A participated in the data collection; Papazoglou A analyzed the data, Manios A contributed a new software used for the study; Zirngibl H contributed literature sources; Manios G analyzed the data; Koumaki D designed the research, performed the research, analyzed the data and wrote the paper; All authors reviewed and approved the final manuscript.
Institutional review board statement: The study was approved from the Ethics Committee of University Witten/Herdecke and was performed in accordance with institutional guidelines. Written informed consent was waived for retrospective study participation.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All authors declare no conflicts of interest.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Marios Papadakis, MD, MSc, PhD, Research Scientist, Surgeon, Department of Surgery II, University of Witten-Herdecke, Heusnerstrasse 40, Wuppertal 42283, Germany. marios_papadakis@yahoo.gr
Received: February 28, 2022 Peer-review started: February 28, 2022 First decision: May 31, 2022 Revised: June 24, 2022 Accepted: July 26, 2022 Article in press: July 26, 2022 Published online: August 24, 2022 Processing time: 176 Days and 0.2 Hours
ARTICLE HIGHLIGHTS
Research background
Computer-aided clinical image analysis is used to improve diagnostic accuracy for skin melanoma.
Research motivation
To the best of our knowledge, there is no study investigating the possible association of computer-assisted objectively obtained color, color texture, sharpness and geometry variables with sentinel lymph node positivity (SLN+).
Research objectives
To investigate a possible association of computer-assisted objectively obtained color, color texture, sharpness and geometry variables with SLN+.
Research methods
The study included patients with histologically confirmed melanomas with Breslow > 0.75 mm who underwent lesion excision and SLN biopsy during the 3-year study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion. All the color images obtained underwent digital processing with an almost fully automated noncommercial software developed by one of the authors for study purposes.
Research results
Ninety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis. The study group consisted of 20 patients with SLN+ biopsy who were compared to 79 patients with tumor-negative SLN biopsy specimen (control group). The study group patients showed significantly higher eccentricity (i.e. distance between color and geometrical midpoint) as well as higher sharpness (i.e. these lesions were more discrete from the surrounding normal skin, P < 0.05). Regarding color variables, SLN+ patients demonstrated higher range in all four color intensities (gray, red, green, blue) and significantly higher skewness in three color intensities (gray, red, blue), P < 0.05. Color texture variables, i.e. lacunarity, were comparable in both groups.
Research conclusions
Computer-aided image analysis can facilitate the prediction of SLN+. SLN+ patients demonstrated significantly higher eccentricity, higher sharpness and higher range in all four color intensities (gray, red, green, blue) as well as significantly higher skewness in three color intensities (gray, red, blue).
Research perspectives
Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+ melanoma patients.
