Slow-growing melanoma: Report of five cases

Savarese, Imma; Martino, Ascanio; Martino, D. Di; Annese, Pietro; Capoluongo, Patrizio; Mordente, Ines; Nicolino, Rachele; Zalaudek, Iris; Argenziano, Giuseppe

doi:10.3315/jdcr.2007.1.1001

Cited by 7 publications

(5 citation statements)

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“…Melanoma is one of the most aggressive tumors in humans [1] and it can be lethal, if not diagnosed on time. The incidence of melanoma among all dermatologic cancers is 4%, while melanoma-induced mortality accounts for about 80% of deaths from skin cancer; only 14% of patients with metastatic melanoma survive for five years [2].…”

Section: Introductionmentioning

confidence: 99%

Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

Pennisi

Bloisi

Nardi

et al. 2016

Computerized Medical Imaging and Graphics

187

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Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.

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Section: Introductionmentioning

confidence: 99%

Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

Pennisi

Bloisi

Nardi

et al. 2016

Computerized Medical Imaging and Graphics

187

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“…Moreover, heavily vascularized melanomas tended to have a higher percentage of CN‐altered genome. A subgroup of five melanomas (Figure S1B) was dermoscopically classified as slow‐growing melanoma (SGM) (Argenziano et al., ; Roma et al., ) according to the presence of regression features and a pigment network, as previously shown by Argenziano et al. (Argenziano et al., ).…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, dermoscopy has provided evidence that clinical behaviors are associated with specific morphological phenotypes. In other words, dermoscopy may help predict the potential outcome of the lesions (Argenziano et al., ; Roma et al., ; Terushkin et al., ). Here, we demonstrated for the first time that the lesions displaying dermoscopic features of aggressiveness are genetically different from the less aggressive forms and are characterized by a strikingly higher degree of genomic damage.…”

Section: Discussionmentioning

confidence: 99%

“…Cutaneous melanoma is the most aggressive form of skin cancer (Curtin et al., ) and encompasses a heterogeneous family of tumors that differ in terms of clinical aspects and biologic behavior, ranging from indolent tumors with a slow growth rate, very low aggressive potential and good prognosis, to highly aggressive tumors with a fast growth rate, high metastatic potential and possible fatal outcome (Argenziano et al., ; Liu et al., ; Roma et al., ; Terushkin et al., ). The reasons for this heterogeneity are still not completely defined.…”

Section: Introductionmentioning

confidence: 99%

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The extent of whole‐genome copy number alterations predicts aggressive features in primary melanomas

Gandolfi¹,

Longo

Moscarella

et al. 2016

Pigment Cell Melanoma Res

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Recent evidence indicates that melanoma comprises distinct types of tumors and suggests that specific morphological features may help predict its clinical behavior. Using a SNP-array approach, we quantified chromosomal copy number alterations (CNA) across the whole genome in 41 primary melanomas and found a high degree of heterogeneity in their genomic asset. Association analysis correlating the number and relative length of CNA with clinical, morphological, and dermoscopic attributes of melanoma revealed that features of aggressiveness were strongly linked to the overall amount of genomic damage. Furthermore, we observed that melanoma progression and survival were mainly affected by a low number of large chromosome losses and a high number of small gains. We identified the alterations most frequently associated with aggressive melanoma, and by integrating our data with publicly available gene expression profiles, we identified five genes which expression was found to be necessary for melanoma cells proliferation. In conclusion, this work provides new evidence that the phenotypic heterogeneity of melanoma reflects a parallel genetic diversity and lays the basis to define novel strategies for a more precise prognostic stratification of patients.

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“…Melanoma is an aggressive tumor and it can be lethal, if not diagnosed in time [1]. Early diagnosis is today the main way to prevent mortality from melanoma.…”

Section: Introductionmentioning

confidence: 99%

Melanoma Detection Using Delaunay Triangulation

Pennisi

Bloisi²,

Nardi³

et al. 2015

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

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The detection of malignant lesions in dermoscopic images by using automatic diagnostic tools can help in reducing mortality from melanoma. In this paper, we describe a fully-automatic algorithm for skin lesion segmentation in dermoscopic images. The proposed approach is highly accurate when dealing with benign lesions, while the detection accuracy significantly decreases when melanoma images are segmented. This particular behavior lead us to consider geometrical and color features extracted from the output of our algorithm for classifying melanoma images, achieving promising results.

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Slow-growing melanoma: Report of five cases

Abstract: Dermoscopy and digital follow up might be the key factors to improve the knowledge about the natural evolution of nevi and melanoma and the spectrum of undefined melanocytic proliferations.

Cited by 7 publications

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Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

The extent of whole‐genome copy number alterations predicts aggressive features in primary melanomas

Melanoma Detection Using Delaunay Triangulation

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