Neutron diffraction and NMR relaxation studies of structural variation and phase transformations for water/ice in SBA-15 silica: I. The over-filled case

Neutron diffraction and NMR relaxation measurements have been made of water/ice in SBA-15, a mesoporous silica constituting an ordered array of cylindrical mesopores of pore diameter similar to 86 angstrom, over the temperature range 180-300 K in a cooling and heating cycle. The over-filled sample shows the initial formation of hexagonal ice on the outside of the silica grains, followed by the nucleation of cubic ice inside the pores at a lower temperature. Neutron scattering profiles for the cubic ice peaks are significantly broadened and indicate a defective structure, as observed in previous experiments on ice formation in sol-gel and MCM-type silicas. Below the pore freezing temperature the intensity of the cubic ice peaks exhibit a significant increase, down to the lowest experimental temperature, indicating a reversible conversion of defective ice to ordered ice crystals. The peak profile analysis for the two ice patterns indicates a systematic variation in the position as a function of temperature, giving values of the expansion coefficients that are slightly lower than other measurements for the bulk phase. NMR results on proton relaxation as a function of temperature indicate the presence of a mobile phase for temperatures below pore freezing that supports the view that there is interconversion between brittle and plastic phases of ice

Diagnostic, Structured Classification and Therapeutic Approach in Cystic Pancreatic Lesions: Systematic Findings with Regard to the European Guidelines

Due to the increasing use of cross-sectional imaging techniques and new technical possibilities, the number of incidentally detected cystic lesions of the pancreas is rapidly increasing in everyday radiological routines. Precise and rapid classification, including targeted therapeutic considerations, is of essential importance. The new European guideline should also support this. This review article provides information on the spectrum of cystic pancreatic lesions, their appearance, and a comparison of morphologic and histologic characteristics. This is done in the context of current literature and clinical value. The recommendations of the European guidelines include statements on conservative management as well as relative and absolute indications for surgery in cystic lesions of the pancreas. The guidelines suggest surgical resection for mucinous cystic neoplasm (MCN) ≥ 40 mm; furthermore, for symptomatic MCN or imaging signs of malignancy, this is recommended independent of its size (grade IB recommendation). For main duct IPMNs (intraductal papillary mucinous neoplasms), surgical therapy is always recommended; for branch duct IPMNs, a number of different risk criteria are applicable to evaluate absolute or relative indications for surgery. Based on imaging characteristics of the most common cystic pancreatic lesions, a precise diagnostic classification of the tumor, as well as guidance for further treatment, is possible through radiology

Diagnostic, Structured Classification and Therapeutic Approach in Cystic Pancreatic Lesions: Systematic Findings with Regard to the European Guidelines

Due to the increasing use of cross-sectional imaging techniques and new technical possibilities, the number of incidentally detected cystic lesions of the pancreas is rapidly increasing in everyday radiological routines. Precise and rapid classification, including targeted therapeutic considerations, is of essential importance. The new European guideline should also support this. This review article provides information on the spectrum of cystic pancreatic lesions, their appearance, and a comparison of morphologic and histologic characteristics. This is done in the context of current literature and clinical value. The recommendations of the European guidelines include statements on conservative management as well as relative and absolute indications for surgery in cystic lesions of the pancreas. The guidelines suggest surgical resection for mucinous cystic neoplasm (MCN) ≥ 40 mm; furthermore, for symptomatic MCN or imaging signs of malignancy, this is recommended independent of its size (grade IB recommendation). For main duct IPMNs (intraductal papillary mucinous neoplasms), surgical therapy is always recommended; for branch duct IPMNs, a number of different risk criteria are applicable to evaluate absolute or relative indications for surgery. Based on imaging characteristics of the most common cystic pancreatic lesions, a precise diagnostic classification of the tumor, as well as guidance for further treatment, is possible through radiology

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours

Aim: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non invasively from digitised H&E slides of primary melanoma tumours. Methods: A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status. Results: The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% +/- 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% +/- 3.5%) AUROC or less. Conclusion: Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts. (C) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Model soups improve performance of dermoscopic skin cancer classifiers

Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. Objective: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. Methods: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. Results: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. Conclusions: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency. (c) 2022 The Authors. Published by Elsevier Ltd

Explainable artificial intelligence in skin cancer recognition: A systematic review

Background: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decisionmaking by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic Methods: Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used isting XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using Conclusion: XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking. 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the C