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PURPOSEMajor progress has occurred in multiple myeloma (MM) treatment in recent years, but this is not seen in low- and middle-income countries.MATERIALS AND METHODSWe retrospectively assessed the efficacy and safety of cyclophosphamide, thalidomide, and dexamethasone (cyclophosphamide 400 mg/m2 for 5 days, thalidomide 100 mg once daily, if tolerated, and dexamethasone 40 mg once weekly; in 28-day cycles) in patients with newly diagnosed MM treated at our institution between April 2008 and December 2012. Survival outcomes were estimated by the Kaplan-Meier method.RESULTSFifty-nine patients were found to meet the selection criteria. Median age was 56 years (27-78). Fifty-nine percent (n = 35) were male. International Staging System three was found in 24%. The median number of treatment cycles was 11 (range 4-12). After a median of 81-month follow-up (range 5-138 months), the overall response rate was 69.5%. The complete response and very good partial response were 5% and 32%, respectively. Median progression-free survival (PFS) was 35 months (95% CI, 18 to 41). The 3-year PFS was 47.4% (95% CI, 34.5 to 59.6) and 5-year PFS was 24.9% (95% CI, 14.4 to 36.9). The median of overall survival (OS) was 81 months (95% CI, 33 to not reached). The 3-year OS was 63.4% (95% CI, 49.2 to 74.6), and 5-year OS was 57.5% (95% CI, 43.2 to 69.4). The most common adverse event was neutropenia (grade 3 and 4, 30.5%). Out of 23 patients eligible for stem-cell transplantation, 10 (43.5%) proceeded with autologous transplantation. Treatment-related deaths occurred in four patients (6.7%).CONCLUSIONCyclophosphamide, thalidomide, and dexamethasone achieves good response rates with tolerable toxicity, especially in patients age 65 years or younger representing a feasible approach for patients with MM in low-income health care settings.

Abstract Purpose Describe the characteristics of patients seen at the Cancer Prevention and Control Service at a Peruvian private cancer clinic in 2014. Patients and methods This retrospective clinical study analyzed the prevalence of 10 cancers and characteristics of patients seen at a private cancer center located in Lima, Peru. The study sample included 7680 adults, and data were collected from de-identified medical records. Results The average age of the patients was 44.71 years and 98,82% of them had private insurance. The majority of patients were women (67.69%). Our gross incidence rate of cancer was 35.16 per 100,000 in the Cancer Prevention and Control Service in 2014. Only 0.35% had cancer, and most of those diagnosed with cancer (77.78%) were diagnosed in the early stages, stages I and II. The two most common cancers observed were breast and thyroid cancer. Conclusions The high rates of early, rather than late-stage diagnoses at this clinic are dramatically different than national rates. This difference may be because we are analyzing data from a prevention service seeing mainly patients with private insurance as opposed to national data, which consists primarily of patients seen in oncologic services with national insurance.

Abstract: Background: Primary cutaneous T-cell lymphomas constitute a heterogeneous and rare group of diseases with regional particularities in Latin America. Objective: To determine the clinicopathological features, relative frequency and survival among patients from a Peruvian institution. Methods: Primary cutaneous T-cell lymphomas were defined based on the absence of extracutaneous disease at diagnosis. Classification was performed following the 2008 World Health Organization Classification of Neoplasms of the Hematopoietic and Lymphoid tissues. Risk groups were established according to the 2005 World Health Organization-EORTC classification for cutaneous lymphomas. Data of patients admitted between January 2008 and December 2012 were analyzed. Results: 74 patients were included. Mean age was 49.5 years. In order of frequency, diagnoses were: mycosis fungoides (40.5%), peripheral T-cell lymphoma not otherwise specified (22.95%), adult T-cell lymphoma/leukemia (18.9%), CD30+ lymphoproliferative disorders (6.8%), hydroa vacciniforme-like lymphoma (5.4%), extranodal NK/T-cell lymphoma (4.1%) and Sézary syndrome (1.4%). Predominant clinical patterns were observed across different entities. Mycosis fungoides appeared mainly as plaques (93%). Peripheral T-cell lymphoma not otherwise specified and adult T-cell lymphoma/leukemia presentation was polymorphic. All patients with hydroa vacciniforme-like lymphoma presented with facial edema. All cases of extranodal NK/T-cell lymphoma appeared as ulcerated nodules/tumors. Disseminated cutaneous involvement was found in 71.6% cases. Forty-six percent of patients were alive at 5 years. Five-year overall survival was 76.4% and 19.2%, for indolent and high-risk lymphomas, respectively (p

In the original publication of this article [1] several figures and tables were incorrectly displayed. In this correction article the correct overview of the tables and figures are published. The original article has been updated. The publisher apologizes to the readers & authors for the inconvenience.

Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.

The Simultaneous Nadir Overpass (SNO) method was developed by the NOAA/NESDIS to improve the consistency and quality of climate data acquired by different meteorological satellites. Taking advantage of the reduced impact induced by the Bidirectional Reflectance Distribution Function (BRDF), atmospheric effects, illumination and viewing geometries during an SNO, we created a sensor comparison methodology for all spectral targets. The method is illustrated by applying it to the assessment of data acquired by the Landsat 8 (L8), Sentinel-2A (S2A), and Sentinel-2B (S2B) optical sensors. Multiple SNOs were identified and selected without the need for orbit propagators. Then, by locating spatially homogeneous areas, it was possible to assess, for a wide range of Top-of-Atmosphere reflectance values, the relationship between the L8 bands and the corresponding ones of S2A and S2B. The results yield high coefficients of determination for S2 A/B with respect to L8. All are higher than 0.980 for S2A and 0.984 for S2B. If the S2 band 8 (wide near-infrared, NIR) is excluded then the lowest coefficients of determination become 0.997 and 0.999 from S2A and S2B, respectively. This methodology can be complementary to those based on Pseudo-Invariant Calibration Sites (PICS) due to its simplicity, highly correlated results and the wide range of compared reflectances and spectral targets.

Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.

Featured Application: The proposed model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution. Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution. [ABSTRACT FROM AUTHOR]

In the original publication of this article [1] several figures and tables were incorrectly displayed. In this correction article the correct overview of the tables and figures are published. The original article has been updated. The publisher apologizes to the readers & authors for the inconvenience. [ABSTRACT FROM AUTHOR]

Purpose: Describe the characteristics of patients seen at the Cancer Prevention and Control Service at a Peruvian private cancer clinic in 2014. Patients and methods: This retrospective clinical study analyzed the prevalence of 10 cancers and characteristics of patients seen at a private cancer center located in Lima, Peru. The study sample included 7680 adults, and data were collected from de-identified medical records. Results: The average age of the patients was 44.71 years and 98,82% of them had private insurance. The majority of patients were women (67.69%). Our gross incidence rate of cancer was 35.16 per 100,000 in the Cancer Prevention and Control Service in 2014. Only 0.35% had cancer, and most of those diagnosed with cancer (77.78%) were diagnosed in the early stages, stages I and II. The two most common cancers observed were breast and thyroid cancer. Conclusions: The high rates of early, rather than late-stage diagnoses at this clinic are dramatically different than national rates. This difference may be because we are analyzing data from a prevention service seeing mainly patients with private insurance as opposed to national data, which consists primarily of patients seen in oncologic services with national insurance. [ABSTRACT FROM AUTHOR]

Abstract Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG $$\ge$$ ≥ 2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. At a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist’s PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. The full code for the ProstateX-trained model is openly available at https://github.com/OscarPellicer/prostate_lesion_detection . We hope that this will represent a landmark for future research to use, compare and improve upon.

The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There have been two regional temperature trends in the study period: cooling (1973–1979) and warming (1980–2018). Our results indicate a time lag of about 20 years between the onset of glacier retreat (−53 m/year since 2000) and the inception of the warming period. Seasonally, the velocity time series suggest acceleration during the summer melt season since 2016, whereas glacier velocities during accumulation months were constant. The highest velocities were observed at high elevations where the ice-surface slope is the steepest. We tested several scenarios to assess the hydrological time response to glacier accelerations, with the highest correlations being found between one and 30 days after the velocity estimates. Monthly correlation analyses indicated inter-annual and intra-annual variability in the glacier dynamics. Additionally, we investigate the linkage between glacier velocities and meltwater outflow parameters as they provide useful information about internal processes in the glacier. Velocity estimates positively correlate with water level and negatively correlate with water conductivity between April and August. There is also a disruption in the correlation trend between water conductivity and ice velocity in June, potentially due to a seasonal release of geothermal water.

Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1−2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.

Sand dune advances poses a major threat to inhabitants and local authorities in the area of Nouakchott, Mauritania. Despite efforts to control dune mobility, accurate and adequate local studies are still needed to tackle sand encroachment. We have developed a Sand Dune Encroachment Vulnerability Index (SDEVI) to assess Nouakchott’s vulnerability to sand dune encroachment. Said index is based on the geo-physical characteristics of the area (wind direction and intensity, slope and surface height, land use, vegetation or soil properties) with Geographic Information System (GIS) techniques that can support local authorities and decision-makers in implementing preventive measures or reducing impact on the population and urban infrastructures. In order to validate this new index, we use two remote sensing approaches: optical-Sentinel 2 and Synthetic Aperture Radar (SAR)–Sentinel 1 data. Results show that the greatest vulnerability is located in the north-eastern part of Nouakchott, where local conditions favor the advance of sand in the city, although medium to high values are also found in the eastern part. Optical images enabled us to distinguish desert sand using the ratio between near infrared/blue bands, and SAR Coherence Change Detection (CCD) imagery was used to assess the degree of stability of those sand bodies. The nature of the SDEVI index allows us to currently assess which areas are vulnerable to sand encroachment since we use long data records. Nevertheless, optical and SAR remote sensing allow sand evolution to be monitored on a near real-time basis.

Considerar ciertos factores físicos y geográficos puede suponer un ahorro importante a la hora de instalar un sistema domótico, además de contribuir a la eficacia del sistema y a su mantenimiento. Cuestiones como la localización del edificio, la consideración de parámetros meteorológico-climáticos, la radiación solar e incluso el análisis social mediante un SIG (Sistema de Información Geográfica) pueden ser importantes a la hora de diseñar la implementación de un proyecto domótico y han de ser ponderados, ante lo necesariamente limitado del presupuesto.

Se ha efectuado la calibración de las imágenes HRPT y LAC originales de los canales 1 y 2 del sensor AVHRR del satélite en órbita porlar NOAA-14 con la finalidad de estimar el Índice de Vegetación de Diferencia Normalizada (NDVI) sobre Perú. Los datos imagen utilizados fueron obtenidos de IMARPE, ABTEMA, INPE y SAA, y están en un formato binario de 16 bits por píxel para los 5 canales. Se ha obtenido las imágenes de reflectancia para las imágenes de los canales 1 y 2 en 16 bits (tipo de dato: entero). Luego, se ontiene el NDVI usando una ecuación matemática en la que se utiliza información de las imágenes de reflectancia. Se obtiene datos imagen productos de los índices de vvegetación para cada día de los meses de enero y febrero de 1999. Se pretende conseguir imagenes mensuales y de 10 días para conseguir un NDVI promedio usando el método de los compuestos máximos. Finalemente, está en preparación la obención de una serie de tiempo de este parámetro desde enero de 1980 hasta junio 2001.

Purpose: We report the 5-year analysis from the phase III Dasatinib Versus Imatinib Study in Treatment-Naïve Chronic Myeloid Leukemia Patients (DASISION) trial, evaluating long-term efficacy and safety outcomes of patients with chronic myeloid leukemia (CML) in chronic phase (CP) treated with dasatinib or imatinib., Patients and Methods: Patients with newly diagnosed CML-CP were randomly assigned to receive dasatinib 100 mg once daily (n = 259) or imatinib 400 mg once daily (n = 260)., Results: At the time of study closure, 61% and 63% of dasatinib- and imatinib-treated patients remained on initial therapy, respectively. Cumulative rates of major molecular response and molecular responses with a 4.0- or 4.5-log reduction in BCR-ABL1 transcripts from baseline by 5 years remained statistically significantly higher for dasatinib compared with imatinib. Rates for progression-free and overall survival at 5 years remained high and similar across treatment arms. In patients who achieved BCR-ABL1 ≤ 10% at 3 months (dasatinib, 84%; imatinib, 64%), improvements in progression-free and overall survival and lower rates of transformation to accelerated/blast phase were reported compared with patients with BCR-ABL1 greater than 10% at 3 months. Transformation to accelerated/blast phase occurred in 5% and 7% of patients in the dasatinib and imatinib arms, respectively. Fifteen dasatinib-treated and 19 imatinib-treated patients had BCR-ABL1 mutations identified at discontinuation. There were no new or unexpected adverse events identified in either treatment arm, and pleural effusion was the only drug-related, nonhematologic adverse event reported more frequently with dasatinib (28% v 0.8% with imatinib). First occurrences of pleural effusion were reported with dasatinib, with the highest incidence in year 1. Arterial ischemic events were uncommon in both treatment arms., Conclusion: These final results from the DASISION trial continue to support dasatinib 100 mg once daily as a safe and effective first-line therapy for the long-term treatment of CML-CP., Competing Interests: AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Disclosures provided by the authors are available with this article at www.jco.org. AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or jco.ascopubs.org/site/ifc. Jorge E. Cortes Consulting or Advisory Role: ARIAD, Bristol-Myers Squibb, Novartis, Pfizer Research Funding: ARIAD (Inst), Bristol-Myers Squibb (Inst), Novartis (Inst), Pfizer (Inst), Teva (Inst) Giuseppe Saglio Consulting or Advisory Role: Bristol-Myers Squibb, Novartis, ARIAD, Pfizer Hagop M. Kantarjian Research Funding: Pfizer (Inst), ARIAD, Amgen (Inst), Bristol-Myers Squibb (Inst), Novartis (Inst) Michele Baccarani Honoraria: Bristol-Myers Squibb Consulting or Advisory Role: Bristol-Myers Squibb Speakers’ Bureau: Bristol-Myers Squibb Jiří Mayer Consulting or Advisory Role: Novartis, Bristol-Myers Squibb Research Funding: Novartis, Bristol-Myers Squibb Concepción Boqué Honoraria: Novartis, Bristol-Myers Squibb Consulting or Advisory Role: Novartis Travel, Accommodations, Expenses: Novartis, Bristol-Myers Squibb, Celgene Neil P. Shah Research Funding: Bristol-Myers Squibb, ARIAD, Pfizer, Daiichi Sankyo, Plexxikon Charles Chuah Honoraria: Bristol-Myers Squibb, Novartis Luis Casanova No relationship to disclose Brigid Bradley-Garelik Employment: Bristol-Myers Squibb Stock or Other Ownership: Bristol-Myers Squibb George Manos Employment: Bristol-Myers Squibb Stock or Other Ownership: Bristol-Myers Squibb Andreas Hochhaus Honoraria: Bristol-Myers Squibb, Novartis, Pfizer, ARIAD, Omniamed Consulting or Advisory Role: Novartis, Bristol-Myers Squibb, Pfizer, ARIAD Research Funding: Novartis (Inst), Bristol-Myers Squibb (Inst), Pfizer (Inst), ARIAD (Inst), MSD Travel, Accommodations, Expenses: Novartis, Bristol-Myers Squibb, Pfizer, ARIAD, (© 2016 by American Society of Clinical Oncology.)