Search

Objectives: To investigate the feasibility of automatic machine learning (autoML) based on native T1 mapping to predict late gadolinium enhancement (LGE) status in hypertrophic cardiomyopathy (HCM). Methods: Ninety-one HCM patients and 44 healthy controls who underwent cardiovascular MRI were enrolled. The native T1 maps of HCM patients were classified as LGE (+) or LGE (-) based on location-matched LGE images. An autoML pipeline was implemented using the tree-based pipeline optimization tool (TPOT) for 3 binary classifications: LGE (+) and LGE (-), LGE (-) and control, and HCM and control. TPOT modeling was repeated 10 times to obtain the optimal model for each classification. The diagnostic performance of the best models by slice and by case was evaluated using sensitivity, specificity, accuracy, and microaveraged area under the curve (AUC). Results: Ten prediction models were generated by TPOT for each of the 3 binary classifications. The diagnostic accuracy obtained with the best pipeline in detecting LGE status in the testing cohort of HCM patients was 0.80 by slice and 0.79 by case. In addition, the TPOT model also showed discriminability between LGE (-) patients and control (accuracy: 0.77 by slice; 0.78 by case) and for all HCM patients and controls (accuracy: 0.88 for both). Conclusions: Native T1 map analysis based on autoML correlates with LGE (+) or (-) status. The TPOT machine learning algorithm could be a promising method for predicting myocardial fibrosis, as reflected by the presence of LGE in HCM patients without the need for late contrast-enhanced MRI sequences. Key Points: • The tree-based pipeline optimization tool (TPOT) is a machine learning algorithm that could help predict late gadolinium enhancement (LGE) status in patients with hypertrophic cardiomyopathy. • The TPOT could serve as an adjuvant method to detect LGE by using information from native T1 maps, thus avoiding the need for contrast agent. • The TPOT also detects native T1 map alterations in LGE-negative patients with hypertrophic cardiomyopathy. [ABSTRACT FROM AUTHOR]

Objectives: We sought to identify the impact of transcatheter aortic valve implantation (TAVI) on changes of fractional flow reserve computed tomography (FFRCT) values and the associated clinical impact. Methods: A retrospective analysis was done with CT obtained pre-TAVI, prior to hospital discharge and at 1-year follow-up, which provided imaging sources for the calculation of FFRCT values based on an online platform. Results: A total of 190 patients were enrolled. Patients with pre-procedural FFRCT value > 0.80 (i.e., negative) and ≤ 0.80 (i.e., positive) demonstrated a significantly opposite change in the value after TAVI (0.8798 vs. 0.8718, p < 0.001 and 0.7634 vs. 0.8222, p < 0.001, respectively). The history of coronary artery disease (CAD) was identified as an independent predictor for FFRCT changing from negative to positive after TAVI (odds ratio [OR] 2.927, 95% confidence interval [CI] 1.130–7.587, p = 0.027), with lesions more severely stenosed (OR 1.039, 95% CI 1.003–1.076, p = 0.034) and in left anterior descending coronary artery (LAD) (OR 3.939, 95% CI 1.060–14.637, p = 0.041) being prone to change. Conclusions: TAVI directly brings improvement in FFRCT values in patients with compromised coronary flow. Patients with a history of CAD, especially with lesions more severely stenosed and in LAD, were under risk of FFRCT changing from negative to positive after TAVI. Key Points: •The effect of TAVI on coronary hemodynamics might be influenced by different ischemic severity and coronary territories reflected by FFRCTvalues. •As different FFRCTvariations did not impact outcomes of TAVI patients, AS, but not coronary issues, may be the primary problem to affect, which needs further validation. [ABSTRACT FROM AUTHOR]