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Introduction: The retropubic tension-free vaginal tape procedure has been the preferred method for primary surgical treatment of stress and stress-dominant mixed urinary incontinence in women for more than 20 years. In this study, we assessed associations between surgeon's experience with the primary tension-free vaginal tape procedure and both perioperative complications and recurrence rates.Material and Methods: Using a consecutive case-series design, we assessed 596 patients treated with primary retropubic tension-free vaginal tape surgery performed by 18 surgeons from 1998 through 2012, with follow up through 2015 (maximum follow-up time: 10 years per patient). Data on perioperative complications and recurrence of stress urinary incontinence from medical records was transferred to a case report form. Surgeon's experience with the tension-free vaginal tape procedure was defined as number of such procedures performed as lead surgeon (1-19 ["beginners"], 20-49 and ≥50 procedures). All analyses were done with a 5% level of statistical significance. We applied the Chi-square test in the assessment of perioperative complications. The regression analyses of recurrence rate by number of tension-free vaginal tape procedures performed were restricted to the three surgeons who performed ≥50 procedures.Results: We found a significantly higher rate of bladder perforations (P = .03) and a higher rate of urinary retentions among patients whose tension-free vaginal tape procedures were performed by "beginners" (P = .06). We observed a significant reduction in recurrence rates with increasing number of tension-free vaginal tape procedures for one surgeon (P = .03).Conclusions: Surgeon's experience with the tension-free vaginal tape procedure is associated with the risk of bladder perforation and urinary retention, and may be associated with the long-term effectiveness of the procedure. [ABSTRACT FROM AUTHOR]

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is not available. The original study used non-public fundus images from EyePACS and three hospitals in India for training. We used a different EyePACS data set from Kaggle. The original study used the benchmark data set Messidor-2 to evaluate the algorithm's performance. We used another distribution of the Messidor-2 data set, since the original data set is no longer available. In the original study, ophthalmologists re-graded all images for diabetic retinopathy, macular edema, and image gradability. We have one diabetic retinopathy grade per image for our data sets, and we assessed image gradability ourselves. We were not able to reproduce the original study's results with publicly available data. Our algorithm's area under the receiver operating characteristic curve (AUC) of 0.951 (95% CI, 0.947-0.956) on the Kaggle EyePACS test set and 0.853 (95% CI, 0.835-0.871) on Messidor-2 did not come close to the reported AUC of 0.99 on both test sets in the original study. This may be caused by the use of a single grade per image, or different data. This study shows the challenges of reproducing deep learning method results, and the need for more replication and reproduction studies to validate deep learning methods, especially for medical image analysis. Our source code and instructions are available at: . [ABSTRACT FROM AUTHOR]

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure. [ABSTRACT FROM AUTHOR]

Commercially available clinical decision support systems (CDSSs) for skin cancer have been designed for the detection of melanoma only. Correct use of the systems requires expert knowledge, hampering their utility for nonexperts. Furthermore, there are no systems to detect other common skin cancer types, that is, nonmelanoma skin cancer (NMSC). As early diagnosis of skin cancer is essential, there is a need for a CDSS that is applicable to all types of skin lesions and is suitable for nonexperts. Nevus Doctor (ND) is a CDSS being developed by the authors. We here investigate ND’s ability to detect both melanoma and NMSC and the opportunities for improvement. An independent test set of dermoscopic images of 870 skin lesions, including 44 melanomas and 101 NMSCs, were analysed by ND. Its sensitivity to melanoma and NMSC was compared to that of Mole Expert (ME), a commercially available CDSS, using the same set of lesions. ND and ME had similar sensitivity to melanoma. For ND at 95% melanoma sensitivity, the NMSC sensitivity was 100%, and the specificity was 12%. The melanomas misclassified by ND at 95% sensitivity were correctly classified by ME, and vice versa. ND is able to detect NMSC without sacrificing melanoma sensitivity. [ABSTRACT FROM AUTHOR]

Background: Skin cancer is among the most common types of cancer. Melanoma is the most fatal of all skin cancer types. The only effective treatment is early excision. Recognising melanoma is challenging both for general physicians and for expert dermatologists. A computer-aided diagnostic system improving diagnostic accuracy would be of great importance. Segmenting the lesion from the skin is the first step in this process. Methods: The present segmentation algorithm uses a multiscale approach for density analysis. Only the skin mode is found by density analysis and then the location of the lesion mode is estimated. The density estimates are attained by Gaussian kernel smoothing with several bandwidths. A new algorithm for hair recognition based on morphological operations on binary images is incorporated into the segmentation algorithm. Results: The algorithm provides correct segmentation for both unimodal and multimodal densities. The segmentation is totally unsupervised, with a digital image as the only input. The algorithm has been tested on an independent set of images collected in dermatological practice, and the segmentation is verified by three dermatologists. Conclusion: The present segmentation algorithm is fast and intuitive. It gives correct segmentation for most types of skin lesions, but fails when the lesion is brighter than the surrounding skin. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall verdict on systems based on clinical and dermoscopic images captured with conventional RGB (red, green, and blue) cameras is that they do not outperform dermatologists. Computer‐based systems based on conventional RGB images seem to have reached an upper limit in their performance, while emerging technologies such as hyperspectral and multispectral imaging might possibly improve the results. These types of images can explore spectral regions beyond the human eye capabilities. Feature selection and dimensionality reduction are crucial parts of extracting salient information from this type of data. It is necessary to extend current classification methodologies to use all of the spatiospectral information, and deep learning models should be explored since they are capable of learning robust feature detectors from data. There is a lack of large, high‐quality datasets of hyperspectral skin lesion images, and there is a need for tools that can aid with monitoring the evolution of skin lesions over time. To understand the rich information contained in hyperspectral images, further research using data science and statistical methodologies, such as functional data analysis, scale‐space theory, machine learning, and so on, are essential. This article is categorized under:Applications of Computational Statistics > Health and Medical Data/Informatics [ABSTRACT FROM AUTHOR]

Previous studies have reported that cod caught using a newly developed dual sequential codend showed significant reduction in external catch-related damage compared to cod caught by a conventional codend. In this study, it was investigated whether this new codend affects residual blood in cod fillets and/or mitigates the effect of postponed bleeding. Residual blood was assessed by measuring hemoglobin using VIS/NIR hyperspectral imaging. No significant differences in hemoglobin content between fish caught by the two different codends were proven, and this was true for whole fillets and fillet compartments (loin, belly, and tail). However, a significant effect of postponed bleeding on the hemoglobin concentration in cod was demonstrated, and this effect was similar whether the fish was caught using the conventional or the sequential codend. Fish bled 40 min after catch had significantly higher levels of hemoglobin compared to fish bled immediately after catch. When comparing the hemoglobin content in the different fillet sections, significantly higher levels of hemoglobin were found in the belly compared to the loin and tail. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor tissues sampled from patients contain prognostic and predictive information beyond what is currently being used in clinical practice. Large-scale digitization enables new ways of exploiting this information. The most promising analysis pipelines include deep learning/artificial intelligence (AI). However, to ensure success, AI often requires a time-consuming curation of data. In our approach, we repurposed AI pipelines and training data for cell segmentation and classification to identify tissue-infiltrating lymphocytes (TILs) in lung cancer tissue. We showed that our approach is able to identify TILs and provide prognostic information in an unseen dataset from lung cancer patients. Our methods can be adapted in myriad ways and may help pave the way for the large-scale deployment of digital pathology. Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17–0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15–0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14–0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting. [ABSTRACT FROM AUTHOR]

Background: Increases in the proportion of the population with increased likelihood of cesarean section (CS) have been postulated as a driving force behind the rise in CS rates worldwide. The aim of the study was to assess if changes in selected maternal risk factors for CS are associated with changes in CS births from 1999 to 2016 in Norway.Methods and Findings: This national population-based registry study utilizes data from 1,055,006 births registered in the Norwegian Medical Birth Registry from 1999 to 2016. The following maternal risk factors for CS were included: nulliparous/≥35 years, multiparous/≥35 years, pregestational diabetes, gestational diabetes, hypertensive disorders, previous CS, assisted reproductive technology, and multiple births. The proportion of CS births in 1999 was used to predict the number of CS births in 2016. The observed and predicted numbers of CS births were compared to determine the number of excess CS births, before and after considering the selected risk factors, for all births, and for births stratified by 0, 1, or >1 of the selected risk factors. The proportion of CS births increased from 12.9% to 16.1% (+24.8%) during the study period. The proportion of births with 1 selected risk factor increased from 21.3% to 26.3% (+23.5%), while the proportion with >1 risk factor increased from 4.5% to 8.8% (+95.6%). Stratification by the presence of selected risk factors reduced the number of excess CS births observed in 2016 compared to 1999 by 67.9%. Study limitations include lack of access to other important maternal risk factors and only comparing the first and the last year of the study period.Conclusions: In this study, we observed that after an initial increase, proportions of CS births remained stable from 2005 to 2016. Instead, both the size of the risk population and the mean number of risk factors per birth continued to increase. We observed a possible association between the increase in size of risk population and the additional CS births observed in 2016 compared to 1999. The increase in size of risk population and the stable CS rate from 2005 and onward may indicate consistent adherence to obstetric evidence-based practice in Norway. [ABSTRACT FROM AUTHOR]

Background: Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case–control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case–control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. Results: Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. Conclusions: In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes. [ABSTRACT FROM AUTHOR]

Abstract: Background: It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. Objective: Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. Methods and materials: We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. Results: With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. Conclusion: We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists. [Copyright &y& Elsevier]