Bioresonance (in)consistency factors

The European Physical Journal Conferences

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Nature Reviews Clinical Oncology, 2022

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefitrisk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in Nature Reviews Clinical Oncology Review article Despite a dramatic increase in research output over the past two decades (Fig. 1), the vast majority of radiomic studies have not yet led to clinically useful tests. Across all medical indications, 343 artificial intelligence and machine learning-based tests currently have FDA clearance, only a small proportion of which are based on radiomics 6. This lack of clinical translation might be attributable to several factors. The vast majority of radiomic studies assess correlations between certain radiomic features and a biological or clinical end point of interest; therefore, the added value of the radiomic test (such as improved clinical performance or reduced invasiveness) is often neglected as is clinical utility, namely that acting upon the information provided leads to a favourable benefit-risk balance for the patient. Additionally, as established in the statistical and machine learning literature, analyses of high-throughput data, such as those obtained using radiomics, are fraught with potential issues, including insufficient data for development and validation and improper application of statistical methodology for the specific purpose of the test. Furthermore, different studies have used widely varying protocols for image acquisition and feature extraction. Several studies have shown the effects of differences in data acquisition, image reconstruction and image post-processing on downstream analyses; different software platforms or even different versions of the same software can produce widely varying results regarding the strength and direction of the associations between features and outcomes 7. Existing guidelines on the acquisition and analysis of radiomic data include a radiomic quality score to evaluate the completeness and appropriateness of such an analysis 8 , computational procedures for commonly used types of features 9 , and protocols for image acquisition, feature extraction and statistical analysis 10,11. However, radiomics would also benefit from a roadmap for the entire process of translating radiomic data into clinically useful tools for guiding clinical care, encompassing not only recommendations for image acquisition and processing, feature extraction, and statistical analysis but also aspects such as test lockdown and demonstrating clinical utility. Such a roadmap has yet to be published for radiomics, although similar criteria and guidelines have been compiled for other omics technologies 12. Herein, we present a 16-point list of criteria for the translation of radiomics into clinically useful tests. These criteria (Box 1) were developed by radiologists, physicists and statisticians with extensive experience with radiomics and other omics technologies, and are based on analogous recommendations developed for other omics technologies 12. These criteria are also adapted to accommodate issues that are unique to radiomics, such as vendor-driven changes in imaging technology and software and the dynamic nature of certain models, and are intended to help researchers to navigate the translation process and catalyse an increase in the number of clinically useful radiomic tests. Clinical application Prior to any formal development and validation, the intended clinical use of the radiomic test and the target population should be established (criterion 1). The use of the test in clinical care should be expected to guide disease assessment and management decisions in a way that leads to a favourable benefit-risk tradeoff and offers advantages over other tests designed to serve the target population in the same role (criterion 2). The intended clinical use will have important implications for the subsequent stages of development and validation, including which features to extract from the imaging data, the optimal imaging time points and the design of the clinical trial to directly assess the performance of the test in its intended role. Key points • Despite tens of thousands of radiomic studies, the number of settings in which radiomics is used to guide clinical decision-making is limited, in part owing to a lack of standardization of the radiomic measurement extraction processes and the lack of evidence demonstrating adequate clinical validity and utility. • Processes to acquire and process source images and extract radiomic measurements should be established and harmonized. • A radiomic model should be tested on external data not used for its development or, if no such dataset is available, tested using proper internal validation techniques. • Model outputs should be shown to guide disease management decisions in a way that leads to a favourable risk-benefit balance for patients. • Clinical performance should be assessed periodically in its intended clinical setting (task and population) after model lockdown. • A list of 16 criteria for the optimal development of a radiomic test has been compiled herein and should hopefully guide the implementation of future radiomic analyses.

Journal of Toxicology and Environmental Health Part B: Critical Reviews, 2001

Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 2010

Invasive and non-invasive measurement sensors and systems perform vital roles in medical care. Devices are based on various principles, including optics, photonics, and plasmonics, electro-analysis, magnetics, acoustics, bio-recognition, etc. Sensors are used for the direct insertion into the human body, for example to be in contact with blood, which constitutes Invasive Measurement. This approach is very challenging technically, as sensor performance (sensitivity, response time, linearity) can deteriorate due to interactions between the sensor materials and the biological environment, such as blood or interstitial fluid. Invasive techniques may also be potentially hazardous. Alternatively, sensors or devices may be positioned external to the body surface, for example to analyse respired breath, thereby allowing safer Non-Invasive Measurement. However, such methods, which are inherently less direct, often requiring more complex calibration algorithms, perhaps using chemometric principles. This paper considers and reviews the issue of calibration in both invasive and non-invasive biomedical measurement systems. Systems in current use usually rely upon periodic calibration checks being performed by clinical staff against a variety of laboratory instruments and QC samples. These procedures require careful planning and overall management if reliable data are to be assured.

Journal of Toxicology and Environmental Health-part B-critical Reviews, 2007

Handbook of Evidence-Based Radiation Oncology, 2018

The Four Rs of Radiobiology (rationale for fractionation of radiation) Repair-refers to DNA repair in response to sublethal or potentially lethal radiation damage. Fractionation of radiation allows normal tissues time to repair. Reassortment-refers to the redistribution of cells into a more radiosensitive phase of the cell cycle due to cell cycle checkpoints after a fraction of radiation. Repopulation-refers to tumor cell proliferation during the course of radiation therapy; this can be problematic with prolonged radiation treatment durations. Reoxygenation-refers to the oxygenation of hypoxic cells after a fraction of radiation. Tumors consist of a mixture of oxygenated and hypoxic cells. The oxygenated cells are more radiosensitive, and therefore oxygenation of hypoxic cells during fractionated therapy increases the sensitivity of tumors to ionizing radiation. A Fifth R has been added to account for in vivo differences in tissue sensitivity Radiosensitivity-accounts for differences in cell metabolism, maturity, and microenvironment of cells in vivo that when combined explain the differences in the sensitivities of different tissues.

Oleh : Syahri Muharom NRP. 2212 204 012 PROGRAM PASCA SARJANA JURUSAN TEKNIK ELEKTRO BIDANG STUDI ELEKTRONIKA INSTITUT TEKNOLOGI SEPULUH NOPEMBER SURABAYA 2015 QUESTION:

Physical and Engineering Sciences in Medicine, 2020

Introduction In order for a population based screening program to be justified, the benefits must outweigh the risks. Routine quality control (QC) testing ensures that systems meet minimum performance requirements and maintain an appropriate risk-benefit ratio. The aim of this work was to determine if QC results were directly correlated with the clinical outcome in terms of cancer detection. Method Routine radiographer and physics QC results (including automatic exposure control (AEC) function, mean glandular dose (MGD), signal difference to noise ratio, and detector response) were retrospectively gathered for 71 digital mammography systems within BreastScreen NSW from the time of initial installation (starting 2008) to June 2016. Major software and hardware changes were also reviewed. The interrelationship between these QC metrics and cancer detection data for the same period of time was investigated. Results Compared with the Sectra C100 AEC mode, C120 had 50% higher MGDs, a statistically significantly higher invasive cancer detection rate, small invasive cancer detection rate, recall rate, and lower interval cancer rate. No significant differences were observed in the DCIS detection rate and the false positive recall rate. For the 0.04 increase in potentially radiation induced cancers per 10,000 women, 8.6 additional invasive cancers were detected. Vendors varied in terms of MGD and cancer detection rates with results ranging from 220 to 550 invasive cancers being detected for each potentially radiation induced cancer. Conclusion The significant differences in cancer detection outcomes between Sectra C100 and Sectra C120 AEC modes are indicative of the interrelationship between QC metrics and cancer detection. Poorer performing systems could be identified and optimised such that the increase in cancer detection outweighs the risks of cancer induction. References/ Acknowledgements

Bulletin of the New York Academy of Medicine, 1979

2016