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Nervil GG, Ternov NK, Vestergaard T, Sølvsten H, Chakera AH, Tolsgaard MG, Hölmich LR. Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial. JMIR Dermatol 2023; 6:e48357. [PMID: 37624707 PMCID: PMC10448292 DOI: 10.2196/48357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/03/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Skin cancer diagnostics is challenging, and mastery requires extended periods of dedicated practice. OBJECTIVE The aim of the study was to determine if self-paced pattern recognition training in skin cancer diagnostics with clinical and dermoscopic images of skin lesions using a large-scale interactive image repository (LIIR) with patient cases improves primary care physicians' (PCPs') diagnostic skills and confidence. METHODS A total of 115 PCPs were randomized (allocation ratio 3:1) to receive or not receive self-paced pattern recognition training in skin cancer diagnostics using an LIIR with patient cases through a quiz-based smartphone app during an 8-day period. The participants' ability to diagnose skin cancer was evaluated using a 12-item multiple-choice questionnaire prior to and 8 days after the educational intervention period. Their thoughts on the use of dermoscopy were assessed using a study-specific questionnaire. A learning curve was calculated through the analysis of data from the mobile app. RESULTS On average, participants in the intervention group spent 2 hours 26 minutes quizzing digital patient cases and 41 minutes reading the educational material. They had an average preintervention multiple choice questionnaire score of 52.0% of correct answers, which increased to 66.4% on the postintervention test; a statistically significant improvement of 14.3 percentage points (P<.001; 95% CI 9.8-18.9) with intention-to-treat analysis. Analysis of participants who received the intervention as per protocol (500 patient cases in 8 days) showed an average increase of 16.7 percentage points (P<.001; 95% CI 11.3-22.0) from 53.9% to 70.5%. Their overall ability to correctly recognize malignant lesions in the LIIR patient cases improved over the intervention period by 6.6 percentage points from 67.1% (95% CI 65.2-69.3) to 73.7% (95% CI 72.5-75.0) and their ability to set the correct diagnosis improved by 10.5 percentage points from 42.5% (95% CI 40.2%-44.8%) to 53.0% (95% CI 51.3-54.9). The diagnostic confidence of participants in the intervention group increased on a scale from 1 to 4 by 32.9% from 1.6 to 2.1 (P<.001). Participants in the control group did not increase their postintervention score or their diagnostic confidence during the same period. CONCLUSIONS Self-paced pattern recognition training in skin cancer diagnostics through the use of a digital LIIR with patient cases delivered by a quiz-based mobile app improves the diagnostic accuracy of PCPs. TRIAL REGISTRATION ClinicalTrials.gov NCT05661370; https://classic.clinicaltrials.gov/ct2/show/NCT05661370.
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Affiliation(s)
- Gustav Gede Nervil
- Department of Plastic Surgery, Herlev-Gentofte Hospital, Herlev, Denmark
| | | | - Tine Vestergaard
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | | | | | - Martin Grønnebæk Tolsgaard
- Copenhagen Academy for Medical Education and Simulation, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Obstetrics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lisbet Rosenkrantz Hölmich
- Department of Plastic Surgery, Herlev-Gentofte Hospital, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Gillstedt M, Segerholm K, Mannius L, Paoli J, Polesie S. How Does a Convolutional Neural Network Trained to Differentiate between Invasive Melanoma and Melanoma In situ Generalize when Assessing Dysplastic Naevi? Acta Derm Venereol 2023; 103:adv00891. [PMID: 36916955 PMCID: PMC10026012 DOI: 10.2340/actadv.v103.4822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/20/2023] [Indexed: 03/16/2023] Open
Abstract
Abstract is missing (Short communication)
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Affiliation(s)
- Martin Gillstedt
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
| | - Klara Segerholm
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ludwig Mannius
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
| | - Sam Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden.
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Polesie S, Gillstedt M, Kittler H, Rinner C, Tschandl P, Paoli J. Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study. J Eur Acad Dermatol Venereol 2022; 36:2002-2007. [PMID: 35841304 PMCID: PMC9796258 DOI: 10.1111/jdv.18436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 06/14/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND Preoperative assessment of whether a melanoma is invasive or in situ (MIS) is a common task that might have important implications for triage, prognosis and the selection of surgical margins. Several dermoscopic features suggestive of melanoma have been described, but only a few of these are useful in differentiating MIS from invasive melanoma. OBJECTIVE The primary aim of this study was to evaluate how accurately a large number of international readers, individually as well as collectively, were able to discriminate between MIS and invasive melanomas as well as estimate the Breslow thickness of invasive melanomas based on dermoscopy images. The secondary aim was to compare the accuracy of two machine learning convolutional neural networks (CNNs) and the collective reader response. METHODS We conducted an open, web-based, international, diagnostic reader study using an online platform. The online challenge opened on 10 May 2021 and closed on 19 July 2021 (71 days) and was advertised through several social media channels. The investigation included, 1456 dermoscopy images of melanomas (788 MIS; 474 melanomas ≤1.0 mm and 194 >1.0 mm). A test set comprising 277 MIS and 246 invasive melanomas was used to compare readers and CNNs. RESULTS We analysed 22 314 readings by 438 international readers. The overall accuracy (95% confidence interval) for melanoma thickness was 56.4% (55.7%-57.0%), 63.4% (62.5%-64.2%) for MIS and 71.0% (70.3%-72.1%) for invasive melanoma. Readers accurately predicted the thickness in 85.9% (85.4%-86.4%) of melanomas ≤1.0 mm (including MIS) and in 70.8% (69.2%-72.5%) of melanomas >1.0 mm. The reader collective outperformed a de novo CNN but not a pretrained CNN in differentiating MIS from invasive melanoma. CONCLUSIONS Using dermoscopy images, readers and CNNs predict melanoma thickness with fair to moderate accuracy. Readers most accurately discriminated between thin (≤1.0 mm including MIS) and thick melanomas (>1.0 mm).
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Affiliation(s)
- S. Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Department of Dermatology and Venereology, Region Västra GötalandSahlgrenska University HospitalGothenburgSweden
| | - M. Gillstedt
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Department of Dermatology and Venereology, Region Västra GötalandSahlgrenska University HospitalGothenburgSweden
| | - H. Kittler
- Department of DermatologyMedical University of ViennaViennaAustria
| | - C. Rinner
- Center of Medical Statistics, Informatics and Intelligent Systems (CeMSIIS)Medical University of ViennaViennaAustria
| | - P. Tschandl
- Department of DermatologyMedical University of ViennaViennaAustria
| | - J. Paoli
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Department of Dermatology and Venereology, Region Västra GötalandSahlgrenska University HospitalGothenburgSweden
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Bozsányi S, Varga NN, Farkas K, Bánvölgyi A, Lőrincz K, Lihacova I, Lihachev A, Plorina EV, Bartha Á, Jobbágy A, Kuroli E, Paragh G, Holló P, Medvecz M, Kiss N, Wikonkál NM. Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma. J Clin Med 2021; 11:jcm11010189. [PMID: 35011930 PMCID: PMC8745435 DOI: 10.3390/jcm11010189] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 12/21/2021] [Accepted: 12/26/2021] [Indexed: 12/20/2022] Open
Abstract
Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.
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Affiliation(s)
- Szabolcs Bozsányi
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
- Selye János Doctoral College for Advanced Studies, Clinical Sciences Research Group, 1085 Budapest, Hungary
| | - Noémi Nóra Varga
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Klára Farkas
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - András Bánvölgyi
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Kende Lőrincz
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Ilze Lihacova
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 1004 Riga, Latvia; (I.L.); (A.L.); (E.V.P.)
| | - Alexey Lihachev
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 1004 Riga, Latvia; (I.L.); (A.L.); (E.V.P.)
| | - Emilija Vija Plorina
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 1004 Riga, Latvia; (I.L.); (A.L.); (E.V.P.)
| | - Áron Bartha
- Department of Bioinformatics, Semmelweis University, 1085 Budapest, Hungary;
- 2nd Department of Pediatrics, Semmelweis University, 1085 Budapest, Hungary
| | - Antal Jobbágy
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Enikő Kuroli
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, 1085 Budapest, Hungary
| | - György Paragh
- Department of Dermatology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA;
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USA
| | - Péter Holló
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Márta Medvecz
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Norbert Kiss
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
| | - Norbert M. Wikonkál
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, 1085 Budapest, Hungary; (S.B.); (N.N.V.); (K.F.); (A.B.); (K.L.); (A.J.); (E.K.); (P.H.); (M.M.); (N.K.)
- Correspondence:
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Polesie S, Gillstedt M, Ahlgren G, Ceder H, Dahlén Gyllencreutz J, Fougelberg J, Johansson Backman E, Pakka J, Zaar O, Paoli J. Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network. Front Med (Lausanne) 2021; 8:723914. [PMID: 34595193 PMCID: PMC8476836 DOI: 10.3389/fmed.2021.723914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/17/2021] [Indexed: 12/02/2022] Open
Abstract
Background: Melanomas are often easy to recognize clinically but determining whether a melanoma is in situ (MIS) or invasive is often more challenging even with the aid of dermoscopy. Recently, convolutional neural networks (CNNs) have made significant and rapid advances within dermatology image analysis. The aims of this investigation were to create a de novo CNN for differentiating between MIS and invasive melanomas based on clinical close-up images and to compare its performance on a test set to seven dermatologists. Methods: A retrospective study including clinical images of MIS and invasive melanomas obtained from our department during a five-year time period (2016–2020) was conducted. Overall, 1,551 images [819 MIS (52.8%) and 732 invasive melanomas (47.2%)] were available. The images were randomized into three groups: training set (n = 1,051), validation set (n = 200), and test set (n = 300). A de novo CNN model with seven convolutional layers and a single dense layer was developed. Results: The area under the curve was 0.72 for the CNN (95% CI 0.66–0.78) and 0.81 for dermatologists (95% CI 0.76–0.86) (P < 0.001). The CNN correctly classified 208 out of 300 lesions (69.3%) whereas the corresponding number for dermatologists was 216 (72.0%). When comparing the CNN performance to each individual reader, three dermatologists significantly outperformed the CNN. Conclusions: For this classification problem, the CNN was outperformed by the dermatologist. However, since the algorithm was only trained and validated on 1,251 images, future refinement and development could make it useful for dermatologists in a real-world setting.
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Affiliation(s)
- Sam Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Martin Gillstedt
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Gustav Ahlgren
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Hannah Ceder
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Johan Dahlén Gyllencreutz
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Julia Fougelberg
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Eva Johansson Backman
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jenna Pakka
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Oscar Zaar
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - John Paoli
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
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