Cost-Effectiveness of Skin Surveillance Through a Specialized Clinic for Patients at High Risk of Melanoma

Purpose Clinical guidelines recommend that people at high risk of melanoma receive regular surveillance to improve survival through early detection. A specialized High Risk Clinic in Sydney, Australia was found to be effective for this purpose; however, wider implementation of this clinical service requires evidence of cost-effectiveness and data addressing potential overtreatment of suspicious skin lesions. Patients and Methods A decision-analytic model was built to compare the costs and benefits of specialized surveillance compared with standard care over a 10-year period, from a health system perspective. A high-risk standard care cohort was obtained using linked population data, comprising the Sax Institute’s 45 and Up cohort study, linked to Medicare Benefits Schedule claims data, the cancer registry, and hospital admissions data. Benefits were measured in quality-adjusted life-years gained. Sensitivity analyses were undertaken for all model parameters. Results Specialized surveillance through the High Risk Clinic was both less expensive and more effective than standard care. The mean saving was A$6,828 (95$5,564 to $8,092) per patient, and the mean quality-adjusted life-year gain was 0.31 (95% CI, 0.27 to 0.35). The main drivers of the differences were detection of melanoma at an earlier stage resulting in less extensive treatment and a lower annual mean excision rate for suspicious lesions in specialized surveillance (0.81; 95% CI, 0.72 to 0.91) compared with standard care (2.55; 95% CI, 2.34 to 2.76). The results were robust when tested in sensitivity analyses. Conclusion Specialized surveillance was a cost-effective strategy for the management of individuals at high risk of melanoma. There were also fewer invasive procedures in specialized surveillance compared with standard care in the community

Conversion of sub-tropical native vegetation to introduced conifer forest: Impacts on below-ground and above-ground carbon pools

Land-use change can have a major influence on soil organic carbon (SOC) and above-ground C pools. We assessed a change from native vegetation to introduced Pinus species plantations on C pools using eight paired sites. At each site we determined the impacts on 0–50 cm below-ground (SOC, charcoal C, organic matter C, particulate organic C, humic organic C, resistant organic C) and above-ground (litter, coarse woody debris, standing trees and woody understorey plants) C pools. In an analysis across the different study sites there was no significant difference (P > 0.05) in SOC or above-ground tree C stocks between paired native vegetation and pine plantations, although significant differences did exist at specific sites. SOC (calculated based on an equivalent soil mass basis) was higher in the pine plantations at two sites, higher in the native vegetation at two sites and did not differ for the other four sites. The site to site variation in SOC across the landscape was far greater than the variation observed with a change from native vegetation to introduced Pinus plantation. Differences between sites were not explained by soil type, although tree basal area was positively correlated with 0–50 cm SOC. In fact, in the native vegetation there was a significant linear relationship between above-ground biomass and SOC that explained 88.8% of the variation in the data. Fine litter C (0–25 mm diameter) tended to be higher in the pine forest than in the adjacent native vegetation and was significantly higher in the pine forest at five of the eight paired sites. Total litter C (0–100 mm diameter) increased significantly with plantation age (R2 = 0.64). Carbon stored in understorey woody plants (2.5–10 cm DBH) was higher in the native vegetation than in the adjacent pine forest. Total site C varied greatly across the study area from 58.8 Mg ha−1 at a native heathland site to 497.8 Mg ha−1 at a native eucalypt forest site. Our findings suggest that the effects of change from native vegetation to introduced Pinus sp. forest are highly site-specific and may be positive, negative, or have no influence on various C pools, depending on local site characteristics (e.g. plantation age and type of native vegetation)

Detection of primary melanoma in individuals at extreme high risk: a prospective 5-year follow-up study

IMPORTANCE: The clinical phenotype and certain predisposing genetic mutations that confer increased melanoma risk are established; however, no consensus exists regarding optimal screening for such individuals. Early identification remains the most important intervention in reducing melanoma mortality. OBJECTIVE: To evaluate the impact of full-body examinations every 6 months supported by dermoscopy and total-body photography (TBP) on all patients and sequential digital dermoscopy imaging (SDDI), when indicated, on detecting primary melanoma in an extreme-risk population. DESIGN, SETTING, AND PARTICIPANTS: Prospective observational study from February 2006 to February 2011, with patients recruited from Sydney Melanoma Diagnostic Centre and Melanoma Institute Australia who had a history of invasive melanoma and dysplastic nevus syndrome, history of invasive melanoma and at least 3 first-degree or second-degree relatives with prior melanoma, history of at least 2 primary invasive melanomas, or a CDKN2A or CDK4 gene mutation. EXPOSURES: Six-month full-body examination compared with TBP. For equivocal lesions, SDDI short term (approximately 3 months) or long term (≥6 months), following established criteria, was performed. Atypical lesions were excised. MAIN OUTCOMES AND MEASURES: New primary melanoma numbers, characteristics, and cumulative incidence in each patient subgroup; effect of diagnostic aids on new melanoma identification. RESULTS: In 311 patients with a median (interquartile range [IQR]) follow-up of 3.5 (2.4-4.2) years, 75 primary melanomas were detected, 14 at baseline visit. Median (IQR) Breslow thickness of postbaseline incident melanomas was in situ (in situ to 0.60 mm). Thirty-eight percent were detected using TBP and 39% with SDDI. Five melanomas were greater than 1 mm Breslow thickness, 3 of which were histologically desmoplastic; the other 2 had nodular components. The benign to malignant excision ratio was 1.6:1 for all lesions excised and 4.4:1 for melanocytic lesions. Cumulative risk of developing a novel primary melanoma was 12.7% by year 2, with new primary melanoma incidence during the final 3 years of follow-up half of that observed during the first 2 years (incidence density ratio, 0.43 [95% CI, 0.25-0.74]; P = .002). CONCLUSIONS AND RELEVANCE: Monitoring patients at extreme risk with TBP and SDDI assisted with early diagnosis of primary melanoma. Hypervigilance for difficult-to-detect thick melanoma subtypes is crucial. Copyright 2014 American Medical Association. All rights reserved

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS)and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green- blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints

Fast and Accurate Border Detection in Dermoscopy Images Using Statistical Region Merging

Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist-determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.http://dx.doi.org/10.1117/12.70907

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

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept

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