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Melanoma: HELP
Articles by Jonathan C. Bowling
Based on 6 articles published since 2010
(Why 6 articles?)

Between 2010 and 2020, J. Bowling wrote the following 6 articles about Melanoma.
+ Citations + Abstracts
1 Article Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. 2018

Haenssle, H A / Fink, C / Schneiderbauer, R / Toberer, F / Buhl, T / Blum, A / Kalloo, A / Hassen, A Ben Hadj / Thomas, L / Enk, A / Uhlmann, L / Anonymous1181156 / Alt, Christina / Arenbergerova, Monika / Bakos, Renato / Baltzer, Anne / Bertlich, Ines / Blum, Andreas / Bokor-Billmann, Therezia / Bowling, Jonathan / Braghiroli, Naira / Braun, Ralph / Buder-Bakhaya, Kristina / Buhl, Timo / Cabo, Horacio / Cabrijan, Leo / Cevic, Naciye / Classen, Anna / Deltgen, David / Fink, Christine / Georgieva, Ivelina / Hakim-Meibodi, Lara-Elena / Hanner, Susanne / Hartmann, Franziska / Hartmann, Julia / Haus, Georg / Hoxha, Elti / Karls, Raimonds / Koga, Hiroshi / Kreusch, Jürgen / Lallas, Aimilios / Majenka, Pawel / Marghoob, Ash / Massone, Cesare / Mekokishvili, Lali / Mestel, Dominik / Meyer, Volker / Neuberger, Anna / Nielsen, Kari / Oliviero, Margaret / Pampena, Riccardo / Paoli, John / Pawlik, Erika / Rao, Barbar / Rendon, Adriana / Russo, Teresa / Sadek, Ahmed / Samhaber, Kinga / Schneiderbauer, Roland / Schweizer, Anissa / Toberer, Ferdinand / Trennheuser, Lukas / Vlahova, Lyobomira / Wald, Alexander / Winkler, Julia / Wölbing, Priscila / Zalaudek, Iris. ·Department of Dermatology, University of Heidelberg, Heidelberg, Germany. Electronic address: Holger.Haenssle@med.uni-heidelberg.de. · Department of Dermatology, University of Heidelberg, Heidelberg, Germany. · Department of Dermatology, University of Göttingen, Göttingen, Germany. · Office Based Clinic of Dermatology, Konstanz, Germany. · Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA. · Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany. · Department of Dermatology, Lyons Cancer Research Center, Lyon 1 University, Lyon, France. · Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany. ·Ann Oncol · Pubmed #29846502.

ABSTRACT: Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

2 Article Validation of a Skin-Lesion Image-Matching Algorithm Based on Computer Vision Technology. 2016

Chen, Raymond H / Snorrason, Magnus / Enger, Shelley M / Mostafa, Eslam / Ko, Justin M / Aoki, Valeria / Bowling, Jonathan. ·1 Lūbax, Inc., Los Angeles, California. · 2 Department of Dermatology, Stanford Medical School , Redwood City, California. · 3 Department of Dermatology, University of Sao Paulo Medical School , Sao Paulo, Brazil . · 4 The Manor Hospital , Oxford, United Kingdom . ·Telemed J E Health · Pubmed #26218353.

ABSTRACT: BACKGROUND: Melanoma incidence is increasing globally, but consistently accurate skin-lesion classification methods remain elusive. We developed a simple software system to classify potentially all types of skin lesions. In the current study, we evaluated the system's ability to identify melanomas with a diameter of 10 mm or larger. MATERIALS AND METHODS: The skin-lesion classification system is composed of a proprietary database of nearly 12,000 diagnosed skin-lesion images and a computer algorithm based on the principles of content-based image retrieval. The algorithm compares characteristics of new skin-lesion images with images in the database to identify the nearest-match diagnosis. RESULTS: Nearly all classification accuracy measures for this new system exceeded 90%, with results for sensitivity of 90.4% (95% confidence interval, 85.6-93.7%), specificity of 91.5% (85.4-95.2%), positive predictive value of 94.5% (90.4-96.9%), negative predictive value of 85.5% (78.7-90.4%), and overall classification accuracy of 90.8% (87.2-93.4%). CONCLUSIONS: The image-matching algorithm performed with high accuracy for the classification of larger melanomas. Furthermore, the system does not require a dermoscope or any other specialized hardware; any close-focusing camera will do. This system has the potential to be an inexpensive and accurate tool for the evaluation of skin lesions in ethnically and geographically diverse populations.

3 Article Residents' corner February 2014. Clues in DeRmosCopy: bloody parallel ridges. 2014

Esdaile, Ben A / Matin, Rubeta N / Bowling, Jonathan C. ·Dermatology Department, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ. United Kingdom. ·Eur J Dermatol · Pubmed #24691246.

ABSTRACT: -- No abstract --

4 Article Diagnosing melanoma: how do we assess how good we are? 2014

Esdaile, B / Mahmud, I / Palmer, A / Bowling, J. ·Dermatology Department, Churchill Hospital, Oxford, UK. ·Clin Exp Dermatol · Pubmed #24524557.

ABSTRACT: BACKGROUND: Evaluating and improving diagnostic accuracy in identification of melanomas is important for both conservation of healthcare resources and reduction in patient morbidity. Useful indicators in assessing this accuracy include the number needed to treat (NNT) and the benign:malignant (B:M) ratio. Both of these methods lack sensitivity, as they do not account for the ability to detect early or in situ melanomas. AIM: To assess the NNT and B:M ratio for a busy hospital serving a population of 650,000 over a 5-year period, and to assess a new ratio of diagnostic accuracy by calculating the ratio of invasive (malignant) melanomas to melanoma in situ (MM:MMIS) as a marker of sensitivity. METHODS: This was a retrospective analysis of data on all melanocytic lesions excised during two separate years (2006 and 2011) with a 5-year interval between them. The lesions were divided into benign naevi (BN), dysplastic naevi (DN), MMIS and MM. RESULTS: In 2006, 650 melanocytic lesions were excised (462 BN/DN, 45 MMIS, 143 MM). The NNT was 3.46, the B:M ratio was 2.46 and the MM:MMIS ratio was 3.18. In 2011, 730 melanocytic lesions were excised (464 BN/DN, 99 MMIS, 167 MM). The NNT was 2.74, the B:M ratio was 1.74 and the MM:MMIS ratio was 1.69. CONCLUSIONS: The NNT and B:M ratios from our study compare favourably with those in the published literature. The fall in the MM:MMIS and B:M ratios over this 5-year study appears to be an indicator of the ability to detect early disease and is probably secondary to the changes to our skin cancer service. This study may encourage physicians to aim not only for low B:M ratios but also low MM:MMIS ratios.

5 Article Dermoscopic evaluation of nodular melanoma. 2013

Menzies, Scott W / Moloney, Fergal J / Byth, Karen / Avramidis, Michelle / Argenziano, Giuseppe / Zalaudek, Iris / Braun, Ralph P / Malvehy, Josep / Puig, Susana / Rabinovitz, Harold S / Oliviero, Margaret / Cabo, Horacio / Bono, Riccardo / Pizzichetta, Maria A / Claeson, Magdalena / Gaffney, Daniel C / Soyer, H Peter / Stanganelli, Ignazio / Scolyer, Richard A / Guitera, Pascale / Kelly, John / McCurdy, Olivia / Llambrich, Alex / Marghoob, Ashfaq A / Zaballos, Pedro / Kirchesch, Herbert M / Piccolo, Domenico / Bowling, Jonathan / Thomas, Luc / Terstappen, Karin / Tanaka, Masaru / Pellacani, Giovanni / Pagnanelli, Gianluca / Ghigliotti, Giovanni / Ortega, Blanca Carlos / Crafter, Greg / Ortiz, Ana María Perusquía / Tromme, Isabelle / Karaarslan, Isil Kilinc / Ozdemir, Fezal / Tam, Anthony / Landi, Christian / Norton, Peter / Kaçar, Nida / Rudnicka, Lidia / Slowinska, Monika / Simionescu, Olga / Di Stefani, Alessandro / Coates, Elliot / Kreusch, Juergen. ·Sydney Melanoma Diagnostic Centre, Sydney Cancer Centre, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia. scott.menzies@sswahs.nsw.gov.au ·JAMA Dermatol · Pubmed #23553375.

ABSTRACT: IMPORTANCE: Nodular melanoma (NM) is a rapidly progressing potentially lethal skin tumor for which early diagnosis is critical. OBJECTIVE: To determine the dermoscopy features of NM. DESIGN: Eighty-three cases of NM, 134 of invasive non-NM, 115 of nodular benign melanocytic tumors, and 135 of nodular nonmelanocytic tumors were scored for dermoscopy features using modified and previously described methods. Lesions were separated into amelanotic/hypomelanotic or pigmented to assess outcomes. SETTING: Predominantly hospital-based clinics from 5 continents. MAIN OUTCOME MEASURES: Sensitivity, specificity, and odds ratios for features/models for the diagnosis of melanoma. RESULTS: Nodular melanoma occurred more frequently as amelanotic/hypomelanotic (37.3%) than did invasive non-NM (7.5%). Pigmented NM had a more frequent (compared with invasive non-NM; in descending order of odds ratio) symmetrical pigmentation pattern (5.8% vs 0.8%), large-diameter vessels, areas of homogeneous blue pigmentation, symmetrical shape, predominant peripheral vessels, blue-white veil, pink color, black color, and milky red/pink areas. Pigmented NM less frequently displayed an atypical broadened network, pigment network or pseudonetwork, multiple blue-gray dots, scarlike depigmentation, irregularly distributed and sized brown dots and globules, tan color, irregularly shaped depigmentation, and irregularly distributed and sized dots and globules of any color. The most important positive correlating features of pigmented NM vs nodular nonmelanoma were peripheral black dots/globules, multiple brown dots, irregular black dots/globules, blue-white veil, homogeneous blue pigmentation, 5 to 6 colors, and black color. A model to classify a lesion as melanocytic gave a high sensitivity (>98.0%) for both nodular pigmented and nonnodular pigmented melanoma but a lower sensitivity for amelanotic/hypomelanotic NM (84%). A method for diagnosing amelanotic/hypomelanotic malignant lesions (including basal cell carcinoma) gave a 93% sensitivity and 70% specificity for NM. CONCLUSIONS AND RELEVANCE: When a progressively growing, symmetrically patterned melanocytic nodule is identified, NM needs to be excluded.

6 Article Melanotan-associated melanoma in situ. 2012

Ong, Suyin / Bowling, Jonathan. ·Department of Dermatology, Churchill Hospital, Oxford, UK. suyin.ong@gmail.com ·Australas J Dermatol · Pubmed #22724573.

ABSTRACT: Injectable synthetic melanotropic peptides (often called melanotan) to enhance tanning are available over the Internet despite being unlicensed compounds with an unproven safety record. There have been reports of dysplastic naevi and melanoma associated with the use of melanotropic peptides. We report a case of melanotan-associated melanoma in situ.