Exploration of historical data and potential solution for the missing information in the Iberian sardine DEPM survey (SAREVA 0320) caused by the COVID-19 pandemic crisis.

This working document provides a description of methods explored to compensate the lack of data in ICES areas 9aN and 8c caused by the cancelation of sardine DEPM survey “SAREVA 0320”. After checking that sardine egg data obtained from the anchovy DEPM survey, delivered in a partial area of division 8c during May by AZTI Tecnalia (BIOMAN), were not adequate for extrapolating to the SAREVA surveyed area, alternative analysis were presented based on i) sardine eggs historic data from the CUFES sampler used in the acoustic Spanish surveys PELACUS and ii) spawning stock biomass data from the Portuguese survey PT-DEPM-PIL and the Spanish survey SAREVA. A methodological approach similar to those adopted in the last sardine assessment (ICES, 2020) to face the problem of the acoustic lack of data in 2020 in subdivisions 9aN and division 8c, is described, reasoned and suggested as a solution to face the lack of Spanish data for sardine stock assessment in 2020

Machine Learning in Melanoma Diagnosis. Limitations About to be Overcome

[spa] Antecedentes: La clasificación automática de imágenes es una rama prometedora del aprendi-zaje automático (de sus siglas en inglés Machine Learning [ML]), y es una herramienta útil enel diagnóstico de cáncer de piel. Sin embargo, poco se ha estudiado acerca de las limitacionesde su uso en la práctica clínica diaria.Objetivo: Determinar las limitaciones que existen en cuanto a la selección de imágenes usadaspara el análisis por ML de las neoplasias cutáneas, en particular del melanoma.Métodos: Se dise ̃nó un estudio de cohorte retrospectivo, donde se incluyeron de forma conse-cutiva 2.849 imágenes dermatoscópicas de alta calidad de tumores cutáneos para su valoraciónpor un sistema de ML, recogidas entre los a ̃nos 2010 y 2014. Cada imagen dermatoscópica fueclasificada según las características de elegibilidad para el análisis por ML.Resultados: De las 2.849 imágenes elegidas a partir de nuestra base de datos, 968 (34%) cum-plieron los criterios de inclusión. De los 528 melanomas, 335 (63,4%) fueron excluidos. Laausencia de piel normal circundante (40,5% de todos los melanomas de nuestra base de datos)y la ausencia de pigmentación (14,2%) fueron las causas más frecuentes de exclusión para elanálisis por ML.Discusión: Solo el 36,6% de nuestros melanomas se consideraron aceptables para el análisispor sistemas de ML de última generación. Concluimos que los futuros sistemas de ML deberánser entrenados a partir de bases de datos más grandes que incluyan imágenes representativasde la práctica clínica habitual. Afortunadamente, muchas de estas limitaciones están siendosuperadas gracias a los avances realizados recientemente por la comunidad científica, como seha demostrado en trabajos recientes. [eng] Background: Automated image classification is a promising branch of machine learning (ML)useful for skin cancer diagnosis, but little has been determined about its limitations for generalusability in current clinical practice.Objective: To determine limitations in the selection of skin cancer images for ML analysis,particularly in melanoma.Methods: Retrospective cohort study design, including 2,849 consecutive high-quality dermos-copy images of skin tumors from 2010 to 2014, for evaluation by a ML system. Each dermoscopyimage was assorted according to its eligibility for ML analysis.Results: Of the 2,849 images chosen from our database, 968 (34%) met the inclusion criteriafor analysis by the ML system. Only 64.7% of nevi and 36.6% of melanoma met the inclusioncriteria. Of the 528 melanomas, 335 (63.4%) were excluded. An absence of normal surroundingskin (40.5% of all melanomas from our database) and absence of pigmentation (14.2%) were themost common reasons for exclusion from ML analysis.Discussion: Only 36.6% of our melanomas were admissible for analysis by state-of-the-art MLsystems. We conclude that future ML systems should be trained on larger datasets which includerelevant non-ideal images from lesions evaluated in real clinical practice. Fortunately, many ofthese limitations are being overcome by the scientific community as recent works show

Melanocortin-1 receptor (MC1R) genotypes do not correlate with size in two cohorts of medium-to-giant congenital melanocytic nevi

Congenital melanocytic nevi (CMN) are cutaneous malformations whose prevalence is inversely correlated with projected adult size. CMN are caused by somatic mutations, but epidemiological studies suggest that germline genetic factors may influence CMN development. In CMN patients from the U.K., genetic variants in MC1R, such as p.V92M and loss-of-function variants, have been previously associated with larger CMN. We analyzed the association of MC1R variants with CMN characteristics in two distinct cohorts of medium-to-giant CMN patients from Spain (N = 113) and from France, Norway, Canada, and the United States (N = 53), similar at the clinical and phenotypical level except for the number of nevi per patient. We found that the p.V92M or loss-of-function MC1R variants either alone or in combination did not correlate with CMN size, in contrast to the U.K. CMN patients. An additional case-control analysis with 259 unaffected Spanish individuals showed a higher frequency of MC1R compound heterozygous or homozygous variant genotypes in Spanish CMN patients compared to the control population (15.9% vs. 9.3%; p = .075). Altogether, this study suggests that MC1R variants are not associated with CMN size in these non-UK cohorts. Additional studies are required to define the potential role of MC1R as a risk factor in CMN development.© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma: A Web-Based International Dermoscopy Society Study.

IMPORTANCE: The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied. OBJECTIVES: To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets. MAIN OUTCOMES AND MEASURES: Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: the ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity). RESULTS: A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95% CI, 5.6-7.8), pattern asymmetry (OR, 4.9; 95% CI, 4.1-5.8), nonorganized pattern (OR, 3.3; 95% CI, 2.9-3.7), border score of 6 (OR, 3.3; 95% CI, 2.5-4.3), and contour asymmetry (OR, 3.2; 95% CI, 2.7-3.7) (P < .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95% CI, 0.40-0.49), absence of vessels (ICC, 0.46; 95% CI, 0.42-0.51), dark brown color (ICC, 0.40; 95% CI, 0.35-0.44), and architectural disorder (ICC, 0.43; 95% CI, 0.39-0.48). The Menzies method had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P < .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves. CONCLUSIONS AND RELEVANCE: Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor

Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma aweb-based international dermoscopy society study

IMPORTANCE The comparative diagnostic performance of dermoscopic algorithms and their individual criteria are not well studied. OBJECTIVES To analyze the discriminatory power and reliability of dermoscopic criteria used in melanoma detection and compare the diagnostic accuracy of existing algorithms. DESIGN, SETTING, AND PARTICIPANTS Thiswas a retrospective, observational study of 477 lesions (119 melanomas [24.9%] and 358 nevi [75.1%]), which were divided into 12 image sets that consisted of 39 or 40 images per set. A link on the International Dermoscopy Society website from January 1, 2011, through December 31, 2011, directed participants to the study website. Data analysis was performed from June 1, 2013, through May 31, 2015. Participants included physicians, residents, and medical students, and there were no specialty-Type or experience-level restrictions. Participants were randomly assigned to evaluate 1 of the 12 image sets. MAIN OUTCOMES AND MEASURES Associations with melanoma and intraclass correlation coefficients (ICCs) were evaluated for the presence of dermoscopic criteria. Diagnostic accuracy measures were estimated for the following algorithms: The ABCD rule, the Menzies method, the 7-point checklist, the 3-point checklist, chaos and clues, and CASH (color, architecture, symmetry, and homogeneity). RESULTS A total of 240 participants registered, and 103 (42.9%) evaluated all images. The 110 participants (45.8%) who evaluated fewer than 20 lesions were excluded, resulting in data from 130 participants (54.2%), 121 (93.1%) of whom were regular dermoscopy users. Criteria associated with melanoma included marked architectural disorder (odds ratio [OR], 6.6; 95%CI, 5.6-7.8), pattern asymmetry (OR, 4.9; 95%CI, 4.1-5.8), nonorganized pattern (OR, 3.3; 95%CI, 2.9-3.7), border score of 6 (OR, 3.3; 95%CI, 2.5-4.3), and contour asymmetry (OR, 3.2; 95%CI, 2.7-3.7) (P &lt; .001 for all). Most dermoscopic criteria had poor to fair interobserver agreement. Criteria that reached moderate levels of agreement included comma vessels (ICC, 0.44; 95%CI, 0.40-0.49), absence of vessels (ICC, 0.46; 95%CI, 0.42-0.51), dark brown color (ICC, 0.40; 95%CI, 0.35-0.44), and architectural disorder (ICC, 0.43; 95%CI, 0.39-0.48). The Menziesmethod had the highest sensitivity for melanoma diagnosis (95.1%) but the lowest specificity (24.8%) compared with any other method (P &lt; .001). The ABCD rule had the highest specificity (59.4%). All methods had similar areas under the receiver operating characteristic curves. CONCLUSIONS AND RELEVANCE Important dermoscopic criteria for melanoma recognition were revalidated by participants with varied experience. Six algorithms tested had similar but modest levels of diagnostic accuracy, and the interobserver agreement of most individual criteria was poor

ICES. 2020. Working Group on Acoustic and Egg Surveys for Sardine and Anchovy in ICES areas 7, 8 and 9

he Working Group on Acoustic and Egg Surveys (WGACEGG) coordinates pelagic surveys for a number of stocks and provides monitoring for the two major sardine and anchovy stocks in ICES areas 6, 7, 8, and 9. The group evaluated small pelagic fish biomass indices derived from acoustic and Daily Egg Production Method (DEPM) surveys in ICES areas 6, 7, 8 and 9. These indices have been provided to the ICES Working Group on Southern Horse Mackerel, Anchovy and Sardine (WGHANSA), the Working Group on Widely Distributed Stocks (WGWIDE) and the Herring Assessment Working Group for the Area South of 62ºN (HAWG) stock assessment group, to serve as fishery-independent input for analytical assessment purposes. DEPM and acoustic indices were derived based on data collected using independent methods. Acoustic- and DEPM-derived biomass indices from quasi-synoptic surveys conducted in the Bay of Biscay in spring were compared, to assess the presence of potential bias and to improve the precision of fish stock biomass estimates. The DEPM-based anchovy biomass index was 22% higher than the acoustic index in 2019. Unusual concentrations of anchovy in Eastern Cantabrian Sea, an area not covered by the acoustic survey, and the presence near the sea surface of actively spawning individuals possibly under-sampled by acoustics in central Bay of Bay had been postulated as potential causes of this discrepancy. No significant difference was found between sardine biomass indices derived from DEPM and acoustics in 2019. The group has updated its database of standard gridded maps covering the European Atlantic area. This initiative continues to inform on the spatial dynamics of various parameters collected during the surveys coordinated under the auspices of the group (fish acoustic densities, anchovy and sardine egg abundance, surface temperature and salinity). Results of an analysis of the time series of gridded maps (anchovy and sardine acoustic density, surface salinity and temperature) showed quantitative changes in the spatial and temporal distribution of anchovy and sardine over the last 15 years, and further define their habitats in European Atlantic waters in spring. The timing and spatial coverage of DEPM and acoustic surveys that will be conducted by group members in 2020 were planned to optimise the monitoring of anchovy and sardine populations and their pelagic environment in the European Atlantic area. The synoptic nature of the survey components has been assessed for each target species. A manual describing the protocols used during the DEPM surveys coordinated by the WGACEGG group was reviewed, and writing of a manual of WGACEGG acoustic surveys continued. Both manuals will be available in 2020. The final results of the 2017 sardine DEPM assessment were endorsed by the group

Preliminary results from the ECOCADIZ 2020-07 Spanish acoustic survey (01 – 14 August 2020)

The present working document summarises a part of the main results obtained from the Spanish (pelagic ecosystem-) acoustic survey conducted by IEO between 01st and 14th August 2020 in the Portuguese and Spanish shelf waters (20-200 m isobaths) off the Gulf of Cadiz (GoC) onboard the R/V Miguel Oliver. The 21 foreseen acoustic transects were sampled. A total of 26 valid fishing hauls were carried out for echo-trace ground-truthing purposes. Four additional night trawls were conducted to collect anchovy hydrated females (DEPM). This working document only provides abundance and biomass estimates for anchovy, sardine and chub mackerel, which are presented without age structure. The distribution of all the mid-sized and small pelagic fish species susceptible of being acoustically assessed is also shown from the mapping of their back-scattering energies. GoC anchovy acoustic estimates in summer 2020 were of 5153 million fish and 44 877 tones, with the bulk of the population occurring in the Spanish waters. The current biomass estimate becomes in the second historical maximum within the time-series. The estimates of sardine abundance and biomass in summer 2020 were 1923 million fish and 50 721 t, estimates close to the historical average, but lower than the values estimated last year and the most recent maxima reached in 2018. A total of 32 854 t and 448 million fish were estimated for Chub mackerel, estimates similar to the most recent ones and very close to the time-series average