The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the ...forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients. (1) SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. (2) SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. (3) Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.
A deep look into radiomics Scapicchio, Camilla; Gabelloni, Michela; Barucci, Andrea ...
La Radiologia medica,
10/2021, Letnik:
126, Številka:
10
Journal Article
Recenzirano
Odprti dostop
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The ...purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.
The aim of the paper is to find an answer to the question “Who or what is responsible for the benefits and harms of using artificial intelligence in radiology?” When human beings make decisions, the ...action itself is normally connected with a direct responsibility by the agent who generated the action. You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. The manuscript addresses the following statements: (1) using AI, the radiologist is responsible for the diagnosis; (2) radiologists must be trained on the use of AI since they are responsible for the actions of machines; (3) radiologists involved in R&D have the responsibility to guide the respect of rules for a trustworthy AI; (4) radiologist responsibility is at risk of validating the unknown (black box); (5) radiologist decision may be biased by the AI automation; (6)risk of a paradox: increasing AI tools to compensate the lack of radiologists; (7) need of informed consent and quality measures. Future legislation must outline the contours of the professional’s responsibility, with respect to the provision of the service performed autonomously by AI, balancing the professional’s ability to influence and therefore correct the machine, limiting the sphere of autonomy that instead technological evolution would like to recognize to robots.
Purpose
To report the results of a nationwide online survey on artificial intelligence (AI) among radiologist members of the Italian Society of Medical and Interventional Radiology (SIRM).
Methods ...and materials
All members were invited to the survey as an initiative by the Imaging Informatics Chapter of SIRM. The survey consisted of 13 questions about the participants’ demographic information, perceived advantages and issues related to AI implementation in radiological practice, and their overall opinion about AI.
Results
In total, 1032 radiologists (equaling 9.5% of active SIRM members for the year 2019) joined the survey. Perceived AI advantages included a lower diagnostic error rate (750/1027, 73.0%) and optimization of radiologists’ work (697/1027, 67.9%). The risk of a poorer professional reputation of radiologists compared with non-radiologists (617/1024, 60.3%), and increased costs and workload due to AI system maintenance and data analysis (399/1024, 39.0%) were seen as potential issues. Most radiologists stated that specific policies should regulate the use of AI (933/1032, 90.4%) and were not afraid of losing their job due to it (917/1032, 88.9%). Overall, 77.0% of respondents (794/1032) were favorable to the adoption of AI, whereas 18.0% (186/1032) were uncertain and 5.0% (52/1032) were unfavorable.
Conclusions
Radiologists had a mostly positive attitude toward the implementation of AI in their working practice. They were not concerned that AI will replace them, but rather that it might diminish their professional reputation.
Objective:
To evaluate the consistency of the quantitative imaging decision support (QIDSTM) tool and radiomic analysis using 594 metrics in lung carcinoma on chest CT scan.
Materials and Methods:
We ...included, retrospectively, 150 patients with histologically confirmed lung cancer who underwent chemotherapy and baseline and follow-ups CT scans. Using the QIDSTM platform, 3 radiologists segmented each lesion and automatically collected the longest diameter and the density mean value. Inter-observer variability, Bland Altman analysis and Spearman’s correlation coefficient were performed. QIDSTM tool consistency was assessed in terms of agreement rate in the treatment response classification. Kruskal Wallis test and the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross validation were used to identify radiomic metrics correlated with lesion size change.
Results:
Good and significant correlation was obtained between the measurements of largest diameter and of density among the QIDSTM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDSTM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDSTM and the radiologists CHOI classification was observed (58-63%). Among 594 extracted metrics, significant and robust predictors of RECIST response were energy, histogram entropy and uniformity, Kurtosis, coronal long axis, longest planar diameter, surface, Neighborhood Grey-Level Different Matrix (NGLDM) dependence nonuniformity and low dependence emphasis as Volume, entropy of Log(2.5 mm), wavelet energy, deviation and root man squared.
Conclusion:
In conclusion, we demonstrated that HealthMyne quantitative volumetric delineation was consistent and that several radiomic metrics extracted by QIDSTM were significant and robust predictors of RECIST response.
Artificial intelligence (AI) is poised to change much about the way we practice radiology in the near future. The power of AI tools has the potential to offer substantial benefit to patients. ...Conversely, there are dangers inherent in the deployment of AI in radiology, if this is done without regard to possible ethical risks. Some ethical issues are obvious; others are less easily discerned, and less easily avoided. This paper explains some of the ethical difficulties of which we are presently aware, and some of the measures we may take to protect against misuse of AI.
Background
The COVID-19 outbreak has played havoc within healthcare systems, with radiology sharing a substantial burden. Our purpose is to report findings from a survey on the crisis impact among ...members of the Italian Society of Medical and Interventional Radiology (SIRM).
Methods
All members were invited to a 42-question online survey about the impact of the COVID-19 outbreak on personal and family life, professional activity, socioeconomic and psychological condition. Participants were classified based on working in the most severely affected Italian regions (“hot regions”) or elsewhere.
Results
A total of 2150 radiologists joined the survey. More than 60% of respondents estimated a workload reduction greater than 50%, with a higher prevalence among private workers in hot regions (72.7% vs 66.5% elsewhere,
p
= 0.1010). Most respondents were concerned that the COVID-19 outbreak could impact the management of non-COVID-19 patients and expected a work overload after the crisis. More than 40% were moderately or severely worried that their professional activity could be damaged, and most residents believed that their training had been affected. More than 50% of respondents had increased emotional stress at work, including moderate or severe symptoms due to sleep disturbances, feeling like living in slow motion and having negative thoughts, those latter being more likely in single-living respondents from hot regions log OR 0.7108 (CI95% 0.3445 ÷ 1.0770),
p
= 0.0001.
Conclusions
The COVID-19 outbreak has had a sensible impact on the working and personal life of SIRM members, with more specific criticalities in hot regions. Our findings could aid preserving the radiologists’ wellbeing after the crisis.
Objectives
Multidisciplinary tumour boards (MTBs) play an increasingly important role in managing cancer patients from diagnosis to treatment. However, many problems arise around the organisation of ...MTBs, both in terms of organisation-administration and time management. In this context, the European Society of Oncologic Imaging (ESOI) conducted a survey among its members, aimed at assessing the quality and amount of involvement of radiologists in MTBs, their role in it and related issues.
Methods
All members were invited to fill in a questionnaire consisting of 15 questions with both open and multiple-choice answers. Simple descriptive analyses and graphs were performed.
Results
A total of 292 ESOI members in full standing for the year 2018 joined the survey. Most respondents (89%) declared to attend MT-Bs, but only 114 respondents (43.9%) review over 70% of exams prior to MTB meetings, mainly due to lack of time due to a busy schedule for imaging and reporting (46.6%). Perceived benefits (i.e. surgical and histological feedback (86.9%), improved knowledge of cancer treatment (82.7%) and better interaction between radiologists and referring clinicians for discussing rare cases (56.9%)) and issues (i.e. attending MTB meetings during regular working hours (71.9%) and lack of accreditation with continuing medical education (CME) (85%)) are reported.
Conclusions
Despite the value and benefits of radiologists’ participation in MTBs, issues like improper preparation due to a busy schedule and no counterpart in CME accreditation require efforts to improve the role of radiologists for a better patient care.
Key Points
• Most radiologists attend multidisciplinary tumour boards, but less than half of them review images in advance, mostly due to time constraints.
• Feedback about radiological diagnoses, improved knowledge of cancer treatment and interaction with referring clinicians are perceived as major benefits.
• Concerns were expressed about scheduling multidisciplinary tumour boards during regular working hours and lack of accreditation with continuing medical education.