Frequency and spectrum of incidental findings when using chest CT as a primary triage tool for COVID-19

Kilsdonk, Iris D.; Roos, Marlise P. de; Bresser, Paul; Reesink, Herre J.; Peringa, Jan

doi:10.1016/j.ejro.2021.100366

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…Despite eminence-based expectations that numerous computed tomography scans of the chest performed during the pandemic would increase the number of incident lung cancers, in 2020, we observed almost 20% fewer new respiratory cancer cases than expected. Other Dutch and Indian studies have also rejected the hypothesis of a higher number of incidental diagnoses of lung cancer during the pandemic ( 38 , 39 ). The Polish incidence gap for respiratory system cancers was considerably larger than in other European countries, including Hungary (-14%) ( 35 ), Denmark (-4%) ( 3 ), and Belgium (-2%) ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

Decrease in the number of new cancer diagnoses during the first year of the COVID-19 pandemic – cohort study of 3.5 million individuals in western Poland

Trojanowski,

Radomyski,

Kycler

et al. 2023

Front. Oncol.

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IntroductionThe COVID-19 pandemic has considerably affected healthcare systems worldwide and is expected to influence cancer incidence, mortality, stage at diagnosis, and survival. This study aimed to assess COVID-19-related changes in cancer incidence observed in 2020 in the Greater Poland region.Materials and methodsData from the Greater Poland Cancer Registry on cancer patients diagnosed between 2010 and 2020 were analysed. To quantify the change in the number of incident cancer cases during the COVID-19 pandemic, we calculated the standardized incidence ratio (SIR) and the incidence rate difference (IRD) to assume the pandemic-attributable gap in cancer incidence.ResultsIn 2020, in Greater Poland, the expected number of new cancer cases was 18 154 (9 226 among males and 8 927 among females), while the observed number was 14 770 (7 336 among males and 7 434 among females). The registered number of cancer cases decreased in 2020 by 20% (SIR 0·80, 95% CI 0·78 to 0·81) and 17% (SIR 0·83, 95% CI 0·81 to 0·85) in males and females, respectively. Among men, the most significant difference was reported for myeloma (SIR 0·59, 95% CI 0·45 to 0·77), among women for bone cancer (SIR 0·47, 95% CI 0·20 to 0·93). In females the observed incidence was higher than expected for cancer of an unspecified site (SIR 1·19, 95% CI 1·01 to 1·38). In our study, the decrease in new cancer cases was greater in males than in females.DiscussionThe observed incidence was affected in most cancer sites, with the most significant deviation from the expected number in the case of myeloma. An increase in the observed incidence was reported only in women diagnosed with cancer of an unspecified site, which might reflect shortages in access to oncological diagnostics.

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Section: Discussionmentioning

confidence: 99%

Decrease in the number of new cancer diagnoses during the first year of the COVID-19 pandemic – cohort study of 3.5 million individuals in western Poland

Trojanowski,

Radomyski,

Kycler

et al. 2023

Front. Oncol.

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“…During the COVID-19 pandemic, LCS programs were suspended as CT scanners were required to perform mass chest CT scans to diagnose this infection. It was noted that in half of the patients who underwent chest CT, incidental findings were detected, and in a quarter, they were clinically significant [6].…”

Section: Rationalementioning

confidence: 99%

A diagnostic and economic evaluation of the complex artificial intelligence algorithm aimed to detect 10 pathologies on the chest CT images

Chernina¹,

Belyaev²,

Silin³

et al. 2023

Preprint

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Background: Artificial intelligence (AI) technologies can help solve the significant problem of missed findings in radiology studies. An important issue is assessing the economic benefits of implementing AI. Aim: to evaluate the frequency of missed pathologies detection and the economic potential of AI technology for chest CT, validated by expert radiologists, compared with radiologists without access to AI in a private medical center. Methods: An observational, single-center retrospective study was conducted. The study included chest CTs without IV contrast performed from 01.06.2022 to 31.07.2022 in "Yauza Hospital" LLC, Moscow. The CTs were processed using a complex AI algorithm for ten pathologies: pulmonary infiltrates, typical for viral pneumonia (COVID-19 in pandemic conditions); lung nodules; pleural effusion; pulmonary emphysema; thoracic aortic dilatation; pulmonary trunk dilatation; coronary artery calcification; adrenal hyperplasia; osteoporosis (vertebral body height and density changes). Two experts analyzed CTs and compared results with AI. Further routing was determined according to clinical guidelines for all findings initially detected and missed by radiologists. The lost potential revenue (LPR) was calculated for each patient according to the hospital price list. Results: From the final 160 CTs, the AI identified 90 studies (56%) with pathologies, of which 81 studies (51%) were missing at least one pathology in the report. The "second-stage" LPR for all pathologies from 81 patients was RUB 2,847,760 (＄37,251 or CNY 256,218). LPR only for those pathologies missed by radiologists but detected by AI was RUB 2,065,360 (＄27,017 or CNY 185,824). Conclusion: Using AI for chest CTs as an "assistant" to the radiologist can significantly reduce the number of missed abnormalities. AI usage can bring 3.6 times more benefits compared to the standard model without AI. The use of complex AI for chest CT can be cost-effective.

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“…Strand и соавт., в течение последних десятилетий имеется тенденция к повышению частоты встречаемости рака легкого у молодых людей [9]. По данным, приведенным в European Journal of Radiology (2021), у пациентов, которым было выполнено исследование компьютерных томограмм ОГК с целью оценки коронавирусной пневмонии, в 12% случаев были впервые обнаружены узлы в легких, подозрительные на ЗНО и требующие дообследования [10]. Поэтому проведение дополнительного анализа компьютерных то-мограмм легких у пациентов без онкоанамнеза во время пандемии COVID-19 может являться уникаль ной возможностью для скрининга рака легкого в определенных группах населения без дополнительной лучевой нагрузки.…”

Section: Introductionunclassified

Evaluation of the prospects for using artificial intelligence technologies to analyze CT scans of the chest organs in order to identify signs of malignant neoplasms in the lungs

Pilus

Drokin

Bazhenova

et al. 2023

Medicinskaâ vizualizaciâ

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The purpose of the study. To analyze the possibility of using artificial intelligence as a decision support system for radiologists for pulmonary nodules detection on Chest CT before and during the COVID-19 pandemic on the example of the system Botkin.AI.Materials and methods. Two groups of Chest CT studies were identified: those performed before (group 1) and during the COVID-19 pandemic (group 2). Each group contains anonymized CT data of 150 patients. Chest CT scans for group 2 were selected based on the percentage of coronavirus lung damage from 0 to 25%. The research was analyzed by the artificial intelligence system Botkin. AI for the presence of peripheral pulmonary nodes up to 6 mm, followed by a “blind” check of the analysis results by three radiologists.Results. In group 1, the sensitivity of the method was 1.0; specificity – 0.88 and AUC – 0.94. In the 2nd group 0.93; 0.81 and 0.86, respectively.In group 2, a slight decrease in specificity is mainly associated with an increase in false positive results in the pulmonary opcities, as manifestations of coronavirus lung damage, taken by the AI model for pulmonary nodes.Conclusion. The platform has a high accuracy of detecting pulmonary nodules on computed tomography of the chest both in studies conducted before and during the COVID-19 pandemic. It can be useful to prevent possible omissions of important findings in conditions of increased workload for radiologists.

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Frequency and spectrum of incidental findings when using chest CT as a primary triage tool for COVID-19

Cited by 10 publications

References 34 publications

Decrease in the number of new cancer diagnoses during the first year of the COVID-19 pandemic – cohort study of 3.5 million individuals in western Poland

Decrease in the number of new cancer diagnoses during the first year of the COVID-19 pandemic – cohort study of 3.5 million individuals in western Poland

A diagnostic and economic evaluation of the complex artificial intelligence algorithm aimed to detect 10 pathologies on the chest CT images

Evaluation of the prospects for using artificial intelligence technologies to analyze CT scans of the chest organs in order to identify signs of malignant neoplasms in the lungs

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