An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features

Non–small cell lung cancer contributes toward 85% of all lung cancer burden. Tumor histology (squamous cell carcinoma, large cell carcinoma, and adenocarcinoma and “not otherwise specified”) has prognostic significance, and it is therefore imperative to identify tumor histology for personalized medicine; however, biopsies are not always possible and carry significant risk of complications. Here, we have used Radiomics, which provides an exhaustive number of informative features, to aid in diagnosis and therapeutic outcome of tumor characteristics in a noninvasive manner. This study evaluated radiomic features of non–small cell lung cancer to identify tumor histopathology. We included 317 subjects and classified the underlying tumor histopathology into its 4 main subtypes. The performance of the current approach was determined to be 20% more accurate than that of an approach considering only the volumetric- and shape-based features

An Approach Toward Automatic Classification of Tumor Histopathology of Non-Small Cell Lung Cancer Based on Radiomic Features

Non-small cell lung cancer contributes toward 85% of all lung cancer burden. Tumor histology (squamous cell carcinoma, large cell carcinoma, and adenocarcinoma and "not otherwise specified") has prognostic significance, and it is therefore imperative to identify tumor histology for personalized medicine; however, biopsies are not always possible and carry significant risk of complications. Here, we have used Radiomics, which provides an exhaustive number of informative features, to aid in diagnosis and therapeutic outcome of tumor characteristics in a noninvasive manner. This study evaluated radiomic features of non-small cell lung cancer to identify tumor histopathology. We included 317 subjects and classified the underlying tumor histopathology into its 4 main subtypes. The performance of the current approach was determined to be 20% more accurate than that of an approach considering only the volumetric- and shape-based features

Artificial intelligence-based clinical decision support in modern medical physics:Selection, acceptance, commissioning, and quality assurance

Background: Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.Aim: In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance.Results: A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well-trained end-users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved.Conclusion: We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success. (C) 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</p

Not Available

Not AvailableNitrogen is one of the most important mineral nutrients required for plant development especially for tillering and vegetative growth. Management of nitrogenous fertilizers poses a significant challenge in sugarcane cropping system as the efficiency of utilization of nitrogen is very poor. Improving the Nitrogen Use Efficiency (NUE) is imperative to achieve the maximum cane yield with less N inputs. In this study, 32diverse sugarcane pre-breeding genetic stocks were evaluated with two levels of nitrogen (N0 and N100) for agronomic, juice quality, biomass traits and Agronomic Nitrogen Use Efficiency (AgNUE). Significant genetic variability was observed among levels of nitrogen and genotypes. Wider differences were observed between phenotypic coefficient of variability (PCV) and genotypic coefficient of variability(GCV) indicating the role of nitrogen levels (N0 and N100) in trait expression. Maximum agronomic efficiency was observed for interspecific hybrids of Saccharum spontaneum (77.92 kg of dry biomass/kg of nitrogen) followed by intergeneric hybrid derivatives of Erianthus procerus (52.61 kg of dry biomass/kg of nitrogen).The study also revealed the early generation hybrids of S. spontaneum and E. procerus recorded maximum AgNUE could be the potential sources for developing nitrogen efficient varieties in sugarcane. Therefore, these genotypes further considered for utilization in crop improvement programmes for development of elite breeding pools for nitrogen use efficiency.ICA