ANTONILDES NASCIMENTO ASSUNCAO JUNIOR
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/65, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina
5 resultados
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- Diagnostic Performance of a Machine Learning-Based CT-Derived FFR in Detecting Flow-Limiting Stenosis(2021) MORAIS, Thamara Carvalho; ASSUNCAO-JR, Antonildes Nascimento; DANTAS JUNIOR, Roberto Nery; SILVA, Carla Franco Grego da; PAULA, Caroline Bastida de; TORRES, Roberto Almeida; MAGALHAES, Tiago Augusto; NOMURA, Cesar Higa; AVILA, Luiz Francisco Rodrigues de; PARGA FILHO, Jose RodriguesBackground: The non-invasive quantification of the fractional flow reserve (FFRCT) using a more recent version of an artificial intelligence-based software and latest generation CT scanner (384 slices) may show high performance to detect coronary ischemia. Objectives: To evaluate the diagnostic performance of FFRCT for the detection of significant coronary artery disease (CAD) in contrast to invasive FFR (iFFR) using previous generation CT scanners (128 and 256-detector rows). Methods: Retrospective study with patients referred to coronary artery CT angiography (CTA) and catheterization (iFFR) procedures. Siemens Somatom Definition Flash (256-detector rows) and AS+ (128-detector rows) CT scanners were used to acquire the images. The FFRCT and the minimal lumen area (MLA) were evaluated using a dedicated software (cFFR version 3.0.0, Siemens Healthineers, Forchheim, Germany). Obstructive CAD was defined as CTA lumen reduction >= 50%, and flow-limiting stenosis as iFFR <= 0.8. All reported P values are two-tailed, and when <0.05, they were considered statistically significant. Results: Ninety-three consecutive patients (152 vessels) were included. There was good agreement between FFRCT and iFFR, with minimal FFRCT overestimation (bias: -0.02; limits of agreement:0.14-0.09). Different CT scanners did not modify the association between FFRCT and FFRi (p for interaction=0.73). The performance of FFRCT was significantly superior compared to the visual classification of coronary stenosis (AUC 0.93vs.0.61, p<0.001) and to MLA (AUC 0.93vs.0.75, p<0.001), reducing the number of false-positive cases. The optimal cut-off point for FFRCT using a Youden index was 0.85 (87% Sensitivity, 86% Specificity, 73% PPV, 94% NPV), with a reduction of false-positives. Conclusion: Machine learning-based FFRCT using previous generation CT scanners (128 and 256-detector rows) shows good diagnostic performance for the detection of CAD, and can be used to reduce the number of invasive procedures.
- Lung Lesion Burden found on Chest CT as a Prognostic Marker in Hospitalized Patients with High Clinical Suspicion of COVID-19 Pneumonia: a Brazil ian experience(2021) FONSECA, Eduardo Kaiser Ururahy Nunes; ASSUNCAO JUNIOR, Antonildes Nascimento; ARAUJO-FILHO, Jose De Arimateia Batista; FERREIRA, Lorena Carneiro; LOUREIRO, Bruna Melo Coelho; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; CHATE, Rodrigo Caruso; CERRI, Giovanni Guido; SAWAMURA, Marcio Valente Yamada; NOMURA, Cesar HigaOBJECTIVE: To investigate the relationship between lung lesion burden (LLB) found on chest computed tomography (CT) and 30-day mortality in hospitalized patients with high clinical suspicion of coronavirus disease 2019 (COVID-19), accounting for tomographic dynamic changes. METHODS: Patients hospitalized with high clinical suspicion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a dedicated and reference hospital for COVID-19, having undergone at least one RTPCR test, regardless of the result, and with one CT compatible with COVID-19, were retrospectively studied. Clinical and laboratory data upon admission were assessed, and LLB found on CT was semi-quantitatively evaluated through visual analysis. The primary outcome was 30-day mortality after admission. Secondary outcomes, including the intensive care unit (ICU) admission, mechanical ventilation used, and length of stay RESULTS: A total of 457 patients with a mean age of 57 +/- 15 years were included. Among these, 58% presented with positive RT-PCR result for COVID-19. The median time from symptom onset to RT-PCR was 8 days [interquartile range 6-11 days]. An initial LLB of X50% using CT was found in 201 patients (44%), which was associated with an increased crude at 30-day mortality (31% vs. 15% in patients with LLB of <50%, p<0.001). An LLB of X50% was also associated with an increase in the ICU admission, the need for mechanical ventilation, and a prolonged LOS after adjusting for baseline covariates and accounting for the CT findings as a time-varying covariate; hence, patients with an LLB of X50% remained at a higher risk at 30-day mortality (adjusted hazard ratio 2.17, 95% confidence interval 1.47-3.18, p<0.001). CONCLUSION: Even after accounting for dynamic CT changes in patients with both clinical and imaging findings consistent with COVID-19, an LLB of X50% might be associated with a higher risk of mortality.
- Evaluation of the RSNA and CORADS classifications for COVID-19 on chest computed tomography in the Brazilian population(2021) FONSECA, Eduardo Kaiser Ururahy Nunes; LOUREIRO, Bruna Melo Coelho; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; GARCIA, Jose Vitor Rassi; GAMA, Victor Arcanjo Almeida; FERREIRA, Lorena Carneiro; CHATE, Rodrigo Caruso; ASSUNCAO JUNIOR, Antonildes Nascimento; SAWAMURA, Marcio Valente Yamada; NOMURA, Cesar HigaOBJECTIVE: To determine the correlation between the two tomographic classifications for coronavirus disease (COVID-19), COVID-19 Reporting and Data System (CORADS) and Radiological Society of North America Expert Consensus Statement on Reporting Chest Computed Tomography (CT) Findings Related to COVID-19 (RSNA), in the Brazilian population and to assess the agreement between reviewers with different experience levels. METHODS: Chest CT images of patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-positive COVID-19 were categorized according to the CORADS and RSNA classifications by radiologists with different levels of experience and who were initially unaware of the RT-PCR results. The inter- and intra-observer concordances for each of the classifications were calculated, as were the concordances between classifications. RESULTS: A total of 100 patients were included in this study. The RSNA classification showed an almost perfect inter-observer agreement between reviewers with similar experience levels, with a kappa coefficient of 0.892 (95% confidence interval [CI], 0.788-0.995). CORADS showed substantial agreement among reviewers with similar experience levels, with a kappa coefficient of 0.642 (95% CI, 0.491-0.793). There was inter-observer variation when comparing less experienced reviewers with more experienced reviewers, with the highest kappa coefficient of 0.396 (95% CI, 0.255-0.588). There was a significant correlation between both classifications, with a Kendall coefficient of 0.899 (p<0.001) and substantial intra-observer agreement for both classifications. CONCLUSION: The RSNA and CORADS classifications showed excellent inter-observer agreement for reviewers with the same level of experience, although the agreement between less experience reviewers and the reviewer with the most experience was only reasonable. Combined analysis of both classifications with the first RT-PCR results did not reveal any false-negative results for detecting COVID-19 in patients.
- U-Net Neural Network for Locating Midpoint of Insertion Zone of Transcatheter Aortic Valves in CTA Images(2021) MINEO, Eduardo; ASSUNCAO-JR, Antonildes N.; MORAIS, Thamara C.; CAMARA, Sergio F.; RIBEIRO, Henrique B.; SIMS, John A.; NOMURA, Cesar H.Identifying the insertion zone of transcatheter heart valves can be time-consuming and suffers from variability and reproducibility problems. We present a deep leaning approach in CTA images to locate the midpoint of the insertion zone. A U-Net neural network is implemented to automatically segment the aortic valve on axial projection. The insertion zone midpoint is calculated based on the range of slices with the more concentrated area of activated pixels. We found a very low systematic error with a median computed error of 0.38mm and interquartile range of 0.15 - 0.75mm. The proposed model was shown to be a robust and powerful tool to automatically locate the insertion zone midpoint and we believe it will play a critical role on automated assessment of aortic stenosis.
- Chest computed tomography in the diagnosis of COVID-19 in patients with false negative RT-PCR(2021) FONSECA, Eduardo Kaiser Ururahy Nunes; FERREIRA, Lorena Carneiro; LOUREIRO, Bruna Melo Coelho; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; QUEIROZ, Gabriel Abrantes de; GARCIA, Jose Vitor Rassi; TEIXEIRA, Renato de Freitas; GAMA, Victor Arcanjo Almeida; CHATE, Rodrigo Caruso; ASSUNCAO JUNIOR, Antonildes Nascimento; SAWAMURA, Marcio Valente Yamada; NOMURA, Cesar HigaObjective: To evaluate the role of chest computed tomography in patients with COVID-19 who presented initial negative result in reverse transcriptase-polymerase chain reaction (RT-PCR). Methods: A single-center, retrospective study that evaluated 39 patients with negative RT-PCR for COVID-19, who underwent chest computed tomography and had a final clinical or serological diagnosis of COVID-19. The visual tomographic classification was evaluated according to the Consensus of the Radiological Society of North America and software developed with artificial intelligence for automatic detection of findings and chance estimation of COVID-19. Results: In the visual tomographic analysis, only one of them (3%) presented computed tomography classified as negative, 69% were classified as typical and 28% as indeterminate. In the evaluation using the software, only four (about 10%) had a probability of COVID-19 <25%. Conclusion: Computed tomography can play an important role in management of suspected cases of COVID-19 with initial negative results in RT-PCR, especially considering those patients outside the ideal window for sample collection for RT-PCR.