How to develop a meaningful radiomic signature for clinical use in oncologic patients.

During the last decade, there is an increasing usage of quantitative methods in Radiology in an effort to reduce the diagnostic variability associated with a subjective manner of radiological interpretation. Combined approaches where visual assessment made by the radiologist is augmented by quantitative imaging biomarkers are gaining attention. Advances in machine learning resulted in the rise of radiomics that is a new methodology referring to the extraction of quantitative information from medical images. Radiomics are based on the development of computational models, referred to as "Radiomic Signatures", trying to address either unmet clinical needs, mostly in the field of oncologic imaging, or to compare radiomics performance with that of radiologists. However, to explore this new technology, initial publications did not consider best practices in the field of machine learning resulting in publications with questionable clinical value. In this paper, our effort was concentrated on how to avoid methodological mistakes and consider critical issues in the workflow of the development of clinically meaningful radiomic signatures

Optimisation of b-values for the accurate estimation of the apparent diffusion coefficient (ADC) in whole-body diffusion-weighted MRI in patients with metastatic melanoma.

OBJECTIVE: To establish optimised diffusion weightings ('b-values') for acquisition of whole-body diffusion-weighted MRI (WB-DWI) for estimation of the apparent diffusion coefficient (ADC) in patients with metastatic melanoma (MM). Existing recommendations for WB-DWI have not been optimised for the tumour properties in MM; therefore, evaluation of acquisition parameters is essential before embarking on larger studies. METHODS: Retrospective clinical data and phantom experiments were used. Clinical data comprised 125 lesions from 14 examinations in 11 patients with multifocal MM, imaged before and/or after treatment with immunotherapy at a single institution. ADC estimates from these data were applied to a model to estimate the optimum b-value. A large non-diffusing phantom was used to assess eddy current-induced geometric distortion. RESULTS: Considering all tumour sites from pre- and post-treatment examinations together, metastases exhibited a large range of mean ADC values, [0.67-1.49] × 10-3 mm2/s, and the optimum high b-value (bhigh) for ADC estimation was 1100 (10th-90th percentile: 740-1790) s/mm2. At higher b-values, geometric distortion increased, and longer echo times were required, leading to reduced signal. CONCLUSIONS: Theoretical optimisation gave an optimum bhigh of 1100 (10th-90th percentile: 740-1790) s/mm2 for ADC estimation in MM, with the large range of optimum b-values reflecting the wide range of ADC values in these tumours. Geometric distortion and minimum echo time increase at higher b-values and are not included in the theoretical optimisation; bhigh in the range 750-1100 s/mm2 should be adopted to maintain acceptable image quality but performance should be evaluated for a specific scanner. KEY POINTS: • Theoretical optimisation gave an optimum high b-value of 1100 (10th-90th percentile: 740-1790) s/mm2 for ADC estimation in metastatic melanoma. • Considering geometric distortion and minimum echo time (TE), a b-value in the range 750-1100 s/mm2 is recommended. • Sites should evaluate the performance of specific scanners to assess the effect of geometric distortion and minimum TE

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Otalgia and eschar in the external auditory canal in scrub typhus complicated by acute respiratory distress syndrome and multiple organ failure

<p>Abstract</p> <p>Background</p> <p>Scrub typhus, a mite-transmitted zoonosis caused by <it>Orientia tsutsugamushi</it>, is an endemic disease in Taiwan and may be potentially fatal if diagnosis is delayed.</p> <p>Case presentations</p> <p>We encountered a 23-year-old previously healthy Taiwanese male soldier presenting with the right ear pain after training in the jungle and an eleven-day history of intermittent high fever up to 39°C. Amoxicillin/clavulanate was prescribed for otitis media at a local clinic. Skin rash over whole body and abdominal cramping pain with watery diarrhea appeared on the sixth day of fever. He was referred due to progressive dyspnea and cough for 4 days prior to admission in our institution. On physical examination, there were cardiopulmonary distress, icteric sclera, an eschar in the right external auditory canal and bilateral basal rales. Laboratory evaluation revealed thrombocytopenia, elevation of liver function and acute renal failure. Chest x-ray revealed bilateral diffuse infiltration. Doxycycline was prescribed for scrub typhus with acute respiratory distress syndrome and multiple organ failure. Fever subsided dramatically the next day and he was discharged on day 7 with oral tetracycline for 7 days.</p> <p>Conclusion</p> <p>Scrub typhus should be considered in acutely febrile patients with multiple organ involvement, particularly if there is an eschar or a history of environmental exposure in endemic areas. Rapid and accurate diagnosis, timely administration of antibiotics and intensive supportive care are necessary to decrease mortality of serious complications of scrub typhus.</p

Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. METHODS AND ANALYSIS: This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. ETHICS AND DISSEMINATION: MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. TRIAL REGISTRATION NUMBER: NCT03574454

MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

An evaluation of the performance in the UK Royal College of Anaesthetists primary examination by UK medical school and gender

<p>Abstract</p> <p>Background</p> <p>There has been comparatively little consideration of the impact that the changes to undergraduate curricula might have on postgraduate academic performance. This study compares the performance of graduates by UK medical school and gender in the Multiple Choice Question (MCQ) section of the first part of the Fellowship of the Royal College of Anaesthetists (FRCA) examination.</p> <p>Methods</p> <p>Data from each sitting of the MCQ section of the primary FRCA examination from June 1999 to May 2008 were analysed for performance by medical school and gender.</p> <p>Results</p> <p>There were 4983 attempts at the MCQ part of the examination by 3303 graduates from the 19 United Kingdom medical schools. Using the standardised overall mark minus the pass mark graduates from five medical schools performed significantly better than the mean for the group and five schools performed significantly worse than the mean for the group. Males performed significantly better than females in all aspects of the MCQ – physiology, mean difference = 3.0% (95% CI 2.3, 3.7), p < 0.001; pharmacology, mean difference = 1.7% (95% CI 1.0, 2.3), p < 0.001; physics with clinical measurement, mean difference = 3.5% (95% CI 2.8, 4.1), p < 0.001; overall mark, mean difference = 2.7% (95% CI 2.1, 3.3), p < 0.001; and standardised overall mark minus the pass mark, mean difference = 2.5% (95% CI 1.9, 3.1), p < 0.001. Graduates from three medical schools that have undergone the change from Traditional to Problem Based Learning curricula did not show any change in performance in any aspects of the MCQ pre and post curriculum change.</p> <p>Conclusion</p> <p>Graduates from each of the medical schools in the UK do show differences in performance in the MCQ section of the primary FRCA, but significant curriculum change does not lead to deterioration in post graduate examination performance. Whilst females now outnumber males taking the MCQ, they are not performing as well as the males.</p

Vascular disrupting agent for neovascular age related macular degeneration: a pilot study of the safety and efficacy of intravenous combretastatin A-4 phosphate

BACKGROUND: This study was designed to assess the safety, tolerability, and efficacy of intravenous infusion of CA4P in patients with neovascular age-related macular degeneration (AMD). METHODS: Prospective, interventional, dose-escalation clinical trial. Eight patients with neovascular AMD refractory to at least 2 sessions of photodynamic therapy received CA4P at a dose of 27 or 36 mg/m2 as weekly intravenous infusion for 4 consecutive weeks. Safety was monitored by vital signs, ocular and physical examinations, electrocardiogram, routine laboratory tests, and collection of adverse events. Efficacy was assessed using retinal fluorescein angiography, optical coherence tomography, and best corrected visual acuity (BCVA). RESULTS: The most common adverse events were elevated blood pressure (46.7%), QTc prolongation (23.3%), elevated temperature (13.3%), and headache (10%), followed by nausea and eye injection (6.7%). There were no adverse events that were considered severe in intensity and none resulted in discontinuation of treatment. There was reduction of the excess foveal thickness by 24.15% at end of treatment period and by 43.75% at end of the two-month follow-up (p = 0.674 and 0.161, respectively). BCVA remained stable throughout the treatment and follow-up periods. CONCLUSIONS: The safety profile of intravenous CA4P was consistent with that reported in oncology trials of CA4P and with the class effects of vascular disruptive agents; however, the frequency of adverse events was different. There are evidences to suggest potential efficacy of CA4P in neovascular AMD. However, the level of systemic safety and efficacy indicates that systemic CA4P may not be suitable as an alternative monotherapy to current standard-of-care therapy

Predicting postoperative complications for gastric cancer patients using data mining

Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

Homologous and heterologous desensitization of guanylyl cyclase-B signaling in GH3 somatolactotropes

The guanylyl cyclases, GC-A and GC-B, are selective receptors for atrial and C-type natriuretic peptides (ANP and CNP, respectively). In the anterior pituitary, CNP and GC-B are major regulators of cGMP production in gonadotropes and yet mouse models of disrupted CNP and GC-B indicate a potential role in growth hormone secretion. In the current study, we investigate the molecular and pharmacological properties of the CNP/GC-B system in somatotrope lineage cells. Primary rat pituitary and GH3 somatolactotropes expressed functional GC-A and GC-B receptors that had similar EC50 properties in terms of cGMP production. Interestingly, GC-B signaling underwent rapid homologous desensitization in a protein phosphatase 2A (PP2A)-dependent manner. Chronic exposure to either CNP or ANP caused a significant down-regulation of both GC-A- and GC-B-dependent cGMP accumulation in a ligand-specific manner. However, this down-regulation was not accompanied by alterations in the sub-cellular localization of these receptors. Heterologous desensitization of GC-B signaling occurred in GH3 cells following exposure to either sphingosine-1-phosphate or thyrotrophin-releasing hormone (TRH). This heterologous desensitization was protein kinase C (PKC)-dependent, as pre-treatment with GF109203X prevented the effect of TRH on CNP/GC-B signaling. Collectively, these data indicate common and distinct properties of particulate guanylyl cyclase receptors in somatotropes and reveal that independent mechanisms of homologous and heterologous desensitization occur involving either PP2A or PKC. Guanylyl cyclase receptors thus represent potential novel therapeutic targets for treating growth-hormone-associated disorders