Noninvasive Computed Tomography–Derived Fractional Flow Reserve Based on Structural and Fluid Analysis: Reproducibility of On-site Determination by Unexperienced Observers

K, Ri; Kumamaru, Kanako K.; Fujimoto, Shinichiro; Kawaguchi, Yuko; Dohi, Tomotaka; Yamada, Sou; Takamura, Kazuhisa; Kogure, Yosuke; Yamada, Norikazu; Katô, Etsurô; Irie, Ryusuke; Takamura, Tomohiro; Suzuki, Michitaka; Hori, Masaaki; Aoki, Shigeki; Daida, Hiroyuki

doi:10.1097/rct.0000000000000679

Cited by 17 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results were reviewed by a radiologist and cardiologist, each of whom had 10 or more years of experience. We previously reported that the correlation coefficient of the CT-FFR value between beginners and skilled analysts increased to 0.83 after specific training with feedback [18]. The inter-observer reproducibility and intra-observer reproducibility have also been found to be favorable among analysts who have trained for a specific time [11], suggesting that results similar to those of this study can be obtained by analysts who undergo specific training.…”

Section: Discussionsupporting

confidence: 77%

The predictive factors affecting false positive in on-site operated CT-fractional flow reserve based on fluid and structural interaction

Kawaguchi

Fujimoto

Kumamaru

et al. 2019

IJC Heart & Vasculature

Self Cite

View full text Add to dashboard Cite

Background A novel algorithm has been developed for the on-site analysis of CT-fractional flow reserve (CT-FFR) using fluid structural interactions. There have been no reports on the factors affecting the diagnostic performance of CT-FFR using this algorithm. We evaluated the factors predictive of false-positive CT-FFR findings compared to invasive FFR as a reference standard. Methods The subjects were 66 consecutive cases (81 vessels) who underwent invasive FFR assessment within 90 days of the detection of 30–90% stenosis of one vessel of the major coronary artery, from among patients with suspected coronary arterial disease who underwent one-rotation scanning by 320-row coronary CT angiography (CCTA). The prospective CCTA mode was used for all patients, with the X-ray exposure set in a range of 70–99% of the RR interval. The FFR was calculated on-site from multiple cardiac phases. Factors associated with a false-positive finding of functional stenosis on CT-FFR, defined as an invasive FFR of ≤0.80, were evaluated using logistic regression analysis. Results Thirty-nine vessels (48.1%) had an invasive FFR of ≤0.80. CT-FFR and invasive FFR values disagreed in 13 vessels in 13 patients. The values were false positive in 12 of the vessels. In an analysis of patient characteristics, the body mass index (odds ratio, 1.33; 95%CI, 1.06–1.67; p = 0.01) and Image noise (odds ratio, 1.18; 95%CI, 1.01–1.40; p = 0.04) were predictive of false-positive findings. The presence of calcified plaque (odds ratio, 5.16; 95%CI, 1.06–20.85; p = 0.01) was the only significant predictive factor in a vessel-based analysis of lesion characteristics. Conclusions The presence of calcified plaque exerted a significant effect on the diagnostic performance of CT-FFR, and did so independently of the degree of calcification indicated by the Agatston score.

show abstract

Section: Discussionsupporting

confidence: 77%

The predictive factors affecting false positive in on-site operated CT-fractional flow reserve based on fluid and structural interaction

Kawaguchi

Fujimoto

Kumamaru

et al. 2019

IJC Heart & Vasculature

Self Cite

View full text Add to dashboard Cite

show abstract

“…The machine-learning or deep-learning algorithm should be necessary to perform fully automatic CT-FFR measurement to improve the accuracy and to reduce inter-operator variability. However, a previous study reported that automated CT-FFR measurement software using structural and fluid analysis displayed good reproducibility even when the postprocessing was performed by an inexperienced observer given only brief training (32). Lastly, we did not compare the CCTA plus CT-FFR with other gold standard functional imaging i.e.…”

Section: Discussionmentioning

confidence: 94%

Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

Rationale and Objectives: Fractional flow reserve (FFR) is an established technique for detecting lesion-specific ischemia but is invasive. Our objective was to investigate the effects of combined assessment of coronary CT angiography (CCTA) imaging features and CT-FFR on detecting lesion-specific ischemia by comparing with invasive FFR. Materials and Methods: Forty-seven patients who had 60 coronary vessels with 30%À90% stenosis were included. Six anatomic CCTA descriptors (Agatston score, stenosis severity, mean plaque CT attenuation value, noncalcified and calcified plaque volumes, remodeling index) and a functional descriptor (CT-FFR) were measured. Random forest was used to identify which descriptors were useful to identify ischemia-related lesion. Receiver-operating characteristic (ROC) curves were calculated for 2 models: i.e. Model-1 for anatomical CT descriptors and Model-2 for anatomical CT descriptors plus CT-FFR. Results: Stenosis severity (40.8 § 15.7% vs 57.6 § 14.1%), noncalcified plaque volume (190 § 100 vs 254.8 § 133.3), and remodeling index (1.04 § 0.12 vs 1.11 § 0.13) were significantly higher in ischemia-related lesions than nonischemia-related lesions. CT-FFR was 0.84 § 0.14 and 0.71 § 0.14, respectively, for ischemia-related and nonischemia-related lesions, and the difference was significant. The area under the ROC curve was 0.738 and 0.835 in Model-1 and Model-2, respectively. Reclassification of ischemic lesion risk was significantly improved after adding CT-FFR: net reclassification improvement was 0.297 and integrated discrimination improvement was 0.254. Conclusion: Combined assessment of anatomical CCTA features and functional CT-FFR was helpful for detecting lesion-specific ischemia.

show abstract

“…[20,21] Moreover, the previously cited study also emphasized decreased variabilities in operators receiving face-to-face training [20]. As such in-person training may counter a potential source of variability which has been the incorrect determination of centerline [20,22].…”

Section: Discussionmentioning

confidence: 94%

Inter-Operator Reliability of an Onsite Machine Learning-Based Prototype to Estimate Ct Angiography-Derived Fractional Flow Reserve

Han¹,

Ahmed²,

Schwemmer³

et al. 2021

Journal of the American College of Cardiology

View full text Add to dashboard Cite

Background: Advances in computed tomography (CT) and machine learning have enabled on-site non-invasive assessment of fractional ow reserve (FFR CT ).Purpose: To assess the inter-operator variability of Coronary CT Angiography-derived FFR CT using a machine learning based post-processing prototype.Materials and Methods: We included 60 symptomatic patients who underwent coronary CT angiography. FFR CT was calculated by 2 independent operators after training using a machine learning based on-site prototype. FFR CT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coe cient (ICC) and Bland-Altman analysis were used to evaluate inter-operator variability effect in FFR CT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.Results: A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI: 0.977 -0.992) and 0.972 per segment (95% CI: 0.967 -0.977). The absolute mean difference in FFR CT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 -0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 -0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared to proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis. Conclusion:A high degree of inter-operator reproducibility can be achieved by onsite machine learning based FFR CT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFR CT .

show abstract

Noninvasive Computed Tomography–Derived Fractional Flow Reserve Based on Structural and Fluid Analysis: Reproducibility of On-site Determination by Unexperienced Observers

Abstract: The CT-derived FFR on-site postprocessing software showed good reproducibility for measurements by inexperienced observers.

Cited by 17 publications

References 19 publications

The predictive factors affecting false positive in on-site operated CT-fractional flow reserve based on fluid and structural interaction

The predictive factors affecting false positive in on-site operated CT-fractional flow reserve based on fluid and structural interaction

Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning

Inter-Operator Reliability of an Onsite Machine Learning-Based Prototype to Estimate Ct Angiography-Derived Fractional Flow Reserve

Contact Info

Product

Resources

About