Can Dermoscopy Be Used to Predict if a Melanoma Is In Situ or Invasive?

Polesie, Sam; Jergéus, Edvin; Gillstedt, Martin; Ceder, Hannah; Gyllencreutz, Johan Dahlén; Fougelberg, Julia; Bäckman, E.; Pakka, Jenna; Zaar, Oscar; Paoli, John

doi:10.5826/dpc.1103a79

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…It is fast and easy-to-perform, the imaging takes 20 s whereas running the algorithm needs one minute. It is comparable to other modalities, such as HFUS, which could estimate the required surgical margins of melanomas (1, 2, or 3 cm) in 26 of the 31 subjects [ 13 ]. Moreover, preoperative HFUS was found to be a potential tool aid for the excision of melanoma in one step [ 58 ].…”

Section: Discussionmentioning

confidence: 90%

“…Both can help the specialists estimate melanoma tumor thickness and can aid the differentiation of nevi from melanomas. However, dermoscopy is a tool designed for healthcare professionals with dedicated training [ 13 , 51 , 69 ]. In contrast, MSI requires no previous training and may be used as smartphones attachment to estimate tumor thickness.…”

Section: Discussionmentioning

confidence: 99%

“…Dermoscopy is the most widely used skin imaging tool in dermatology. Among various other applications, it has also been applied to predict Breslow thickness [ 13 ]. Specific dermoscopic patterns can be helpful in predicting thickness.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Bozsányi

Varga

Farkas

et al. 2021

JCM

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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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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

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Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Bozsányi

Varga

Farkas

et al. 2021

JCM

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“…Preoperative assessment of whether a melanoma is invasive or MIS is a common task that might have important implications for triage, prognosis and the selection of surgical margins. 4 Previous studies in this field focused on the description of dermoscopy criteria that differentiate between invasive melanoma and MIS, 2,3 but the accuracy of this approach has not been assessed in a larger setting involving multiple readers. Recently, gamification and crowdsourcing have proven useful to harness data for the assessment of skin tumours from multiple readers all over the world.…”

Section: Introductionmentioning

confidence: 99%

Assessment of melanoma thickness based on dermoscopy images: an open, web‐based, international, diagnostic study

Polesie

Gillstedt

Kittler

et al. 2022

Acad Dermatol Venereol

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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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“…Nonetheless, for MIS, we advocate that the first diagnostic excision should preferably also be the only one needed to provide the cure. Contrarily, if invasive melanoma is the primary suspicion, a narrower excision margin may be selected since a subsequent excision with wider margins and potentially a sentinel node biopsy will be required ( 3 ). Finally, predicting if a melanoma is invasive or MIS preoperatively could also have relevance for urgent referral and triaging purposes.…”

Section: Introductionmentioning

confidence: 99%

Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network

et al. 2021

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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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Can Dermoscopy Be Used to Predict if a Melanoma Is In Situ or Invasive?

Cited by 11 publications

References 20 publications

Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Assessment of melanoma thickness based on dermoscopy images: an open, web‐based, international, diagnostic study

Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network

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