Statistical significance : p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach

Here, we summarise the unresolved debate about p value and its dichotomisation. We present the statement of the American Statistical Association against the misuse of statistical significance as well as the proposals to abandon the use of p value and to reduce the significance threshold from 0.05 to 0.005. We highlight reasons for a conservative approach, as clinical research needs dichotomic answers to guide decision-making, in particular in the case of diagnostic imaging and interventional radiology. With a reduced p value threshold, the cost of research could increase while spontaneous research could be reduced. Secondary evidence from systematic reviews/meta-analyses, data sharing, and cost-effective analyses are better ways to mitigate the false discovery rate and lack of reproducibility associated with the use of the 0.05 threshold. Importantly, when reporting p values, authors should always provide the actual value, not only statements of \u201cp < 0.05\u201d or \u201cp 65 0.05\u201d, because p values give a measure of the degree of data compatibility with the null hypothesis. Notably, radiomics and big data, fuelled by the application of artificial intelligence, involve hundreds/thousands of tested features similarly to other \u201comics\u201d such as genomics, where a reduction in the significance threshold, based on well-known corrections for multiple testing, has been already adopted

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too

Bringing radiology to patient's home using mobile equipment : a weapon to fight COVID-19 pandemic

Because of coronavirus disease 2019 (COVID-19) high contagiousness, it is crucial to identify and promptly isolate COVID-19 patients. In this context, chest imaging examinations, in particular chest x-ray (CXR), can play a pivotal role in different settings, to triage in case of unavailability, delay of or first negative result of reverse transcriptase-polymerase chain reaction (RT-PCR), and to stratify disease severity. Considering the need to reduce, as much as possible, hospital admission of patients with suspected or confirmed infection, the use of mobile x-ray equipment could represent a safe approach. We picture a potential sequence of events, involving a team composed by a radiographer and a nurse, going to patient's home to perform CXR, nasopharyngeal swab (and, if needed, also a blood sample), with fast radiologist tele-reporting, and resulting patient management approach (home isolation or emergency room admission, when needed). This approach brings healthcare to patient's home, reducing the risk of infected subjects referring to family doctors' office or emergency departments, and strengthening community medicine while maintaining a strong connection with radiology departments

Trends in radiology and experimental research

European Radiology Experimental, the new journal launched by the European Society of Radiology, is placed in the context of three general and seven radiology-specific trends. After describing the impact of population aging, personalized/precision medicine, and information technology development, the article considers the following trends: the tension between subspecialties and the unity of the discipline; attention to patient safety; the challenge of reproducibility for quantitative imaging; standardized and structured reporting; search for higher levels of evidence in radiology (from diagnostic performance to patient outcome); the increasing relevance of interventional radiology; and continuous technological evolution. The new journal will publish not only studies on phantoms, cells, or animal models but also those describing development steps of imaging biomarkers or those exploring secondary end-points of large clinical trials. Moreover, consideration will be given to studies regarding: computer modelling and computer aided detection and diagnosis; contrast materials, tracers, and theranostics; advanced image analysis; optical, molecular, hybrid and fusion imaging; radiomics and radiogenomics; three-dimensional printing, information technology, image reconstruction and post-processing, big data analysis, teleradiology, clinical decision support systems; radiobiology; radioprotection; and physics in radiology. The journal aims to establish a forum for basic science, computer and information technology, radiology, and other medical subspecialties

The future of radiology is now: the first 100 articles published in European Radiology Experimental

European Radiology Experimental reached the first 100 articles published in two years. Rejection rate was 30%, publication rate increased from 3.5/month in the first 12-month period to 4.8/month in the second 12-month period. The journal metrics were: 25 days from submission to first decision, 96 days from submission to acceptance, and 69 days from acceptance to publication. At the end of May 2019, we accumulated a total of 82,367 article accesses, 541 Altmetric score, and 110 citations for 92 published articles. Europe accounted for 85% of article origin. One third of corresponding authors were not radiologists/radiology residents, but were rather mainly physicists, engineers, or computer scientists. The distribution among subspecialties/body parts was well balanced; 9% of the topics regarded patient's safety, radioprotection, or contrast media. Magnetic resonance imaging (MRI) and computed tomography (CT) accounted for 71% of the articles. Twenty-two percent of original articles/technical notes reported on animal models, 15% on phantoms, 3% on in silico, 2% on human cadavers, and 2% on cells. Nine articles regarded artificial intelligence and/or radiomics, and 2 regarded augmented reality. Of 100 articles, 57 declared funding sources. A total of 517 independent reviews were performed by 92 reviewers. The five articles quoted the most regarded augmented reality, spectral photon-counting CT, artificial intelligence, MRI radiomics, and diffusion tensor imaging of the musculoskeletal and peripheral nerve systems. The journal is complying with aims and scope of its "experimental" profile

Novel imaging biomarkers: epicardial adipose tissue evaluation

Epicardial adipose tissue (EAT) is a metabolically activated beige adipose tissue, non-homogeneously surrounding the myocardium. Physiologically, EAT regulates toxic fatty acids, protects the coronary arteries against mechanical strain, regulates proinflammatory cytokines, stimulates the production of nitric oxide, reduces oxidative stress, and works as a thermogenic source against hypothermia. Conversely, EAT has pathologic paracrine interactions with the surrounded vessels, and might favour the onset of atrial fibrillation. In addition, initial atherosclerotic lesions can promote inflammation and trigger the EAT production of cytokines increasing vascular inflammation, which, in turn, may help the development of collateral vessels but also of self-stimulating, dysregulated inflammatory process, increasing coronary artery disease severity. Variations in EAT were also linked to metabolic syndrome. Echocardiography first estimated EAT measuring its thickness on the free wall of the right ventricle but does not allow accurate volumetric EAT estimates. Cardiac CT (CCT) and cardiac MR (CMR) allow for three-dimensional EAT estimates, the former showing higher spatial resolution and reproducibility but being limited by radiation exposure and long segmentation times, the latter being radiation-free but limited by lower spatial resolution and reproducibility, higher cost, and difficulties for obese patients. EAT radiodensity at CCT could to be related to underlying metabolic processes. The correlation between EAT and response to certain pharmacological therapies has also been investigated, showing promising results. In the future, semi-automatic or fully automatic techniques, machine/deep-learning methods, if validated, will facilitate research for various EAT measures and may find a place in CCT/CMR reporting

Artificial intelligence in medical imaging: threat or opportunity? : radiologists again at the forefront of innovation in medicine

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams

Ultrasound semiautomatic versus manual estimation of carotid intima-media thickness : reproducibility and cardiovascular risk stratification

Aims: Carotid intima-media thickness (CIMT) is used increasingly as an imaging biomarker of cardiovascular risk (CVR). Our aim was to compare semiautomatic CIMT (sCIMT) versus manual CIMT (mCIMT) for reproducibility and prediction of CVR. Materials and methods: Two independent readers measured sCIMT and mCIMT on previously acquired images of the right common carotid artery of 200 consecutive patients. Measurements were performed twice, four weeks apart; sCIMT was reported along with an image quality index (IQI) provided by the software. CVR stratification was compared for thresholds established by mCIMT studies, adapted for sCIMT according to a regression model. Results: sCIMT (median 0.67 mm, interquartile range [IQR] 0.57\u20120.76 mm) was significantly lower (p<0.001) than mCIMT (median 0.76 mm, IQR 0.63\u20120.84 mm; \u3c1=0.832, p<0.001, slope 0.714, intercept 0.124). Overall, intra-reader reproducibility was 76% for sCIMT and 83% for mCIMT (p=0.002), inter-reader reproducibility 75% and 76%, respectively (p=0.316). In 129 cases with IQI 650.65, reproducibility was significantly higher (p 640.004) for sCIMT than for mCIMT (intra-reader 85% versus 83%, inter-reader 80% versus 77%,). The agreement between sCIMT and mCIMT for CVR stratification was fair both overall (\u3ba=0.270) and for IQI 650.65 (\u3ba=0.345), crude concordance being 79% and 88%, respectively. Conclusions: Reproducibility of sCIMT was not higher than mCIMT overall but sCIMT was significantly more reproducible than mCIMT for high-IQI cases. sCIMT cannot be used for CVR stratification due to fair concordance with mCIMT, even for high IQI. More research is required to improve image quality and define sCIMT-based thresholds for stratification of CVR

Image quality of late gadolinium enhancement in cardiac magnetic resonance with different doses of contrast material in patients with chronic myocardial infarction

Background: Contrast-enhanced cardiac magnetic resonance (CMR) is pivotal for evaluating chronic myocardial infarction (CMI). Concerns about safety of gadolinium-based contrast agents favour dose reduction. We assessed image quality of scar tissue in CMRs performed with different doses of gadobutrol in CMI patients. Methods: Informed consent was waived for this Ethics Committee-approved single-centre retrospective study. Consecutive contrast-enhanced CMRs from CMI patients were retrospectively analysed according to the administered gadobutrol dose (group A, 0.10 mmol/kg; group B, 0.15 mmol/kg; group C, 0.20 mmol/kg). We calculated the signal-to-noise ratio for scar tissue (SNRscar) and contrast-to-noise ratio between scar and either remote myocardium (CNRscar-rem) or blood (CNRscar-blood). Results: Of 79 CMRs from 79 patients, 22 belonged to group A, 26 to group B, and 31 to group C. The groups were homogeneous for age, sex, left ventricular morpho-functional parameters, and percentage of scar tissue over whole myocardium (p 65 0.300). SNRscar was lower in group A (46.4; 40.3\u201365.1) than in group B (70.1; 52.2\u2013111.5) (p = 0.013) and group C (72.1; 59.4\u2013100.0) (p = 0.002), CNRscar-rem was lower in group A (62.9; 52.2\u201387.4) than in group B (96.5; 73.1\u2013152.8) (p = 0.008) and in group C (103.9; 83.9\u2013132.0) (p = 0.001). No other significant differences were found (p 65 0.335). Conclusions: Gadobutrol at 0.10 mmol/kg provides inferior scar image quality of CMI than 0.15 and 0.20 mmol/kg; the last two dosages seem to provide similar LGE. Thus, for CMR of CMI, 0.15 mmol/kg of gadobutrol can be suggested instead of 0.20 mmol/kg, with no hindrance to scar visualisation. Dose reduction would not impact on diagnostic utility of CMR examinations