TY - GEN
T1 - STAR-Echo
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
AU - Dhamdhere, Rohan
AU - Modanwal, Gourav
AU - Makhlouf, Mohamed H.E.
AU - Shafiabadi Hassani, Neda
AU - Bharadwaj, Satvika
AU - Fu, Pingfu
AU - Milioglou, Ioannis
AU - Rahman, Mahboob
AU - Al-Kindi, Sadeer
AU - Madabhushi, Anant
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Chronic Kidney Disease (CKD) patients are at higher risk of Major Adverse Cardiovascular Events (MACE). Echocardiography evaluates left ventricle (LV) function and heart abnormalities. LV Wall (LVW) pathophysiology and systolic/diastolic dysfunction are linked to MACE outcomes (O- and O+ ) in CKD patients. However, traditional LV volume-based measurements like ejection-fraction offer limited predictive value as they rely only on end-phase frames. We hypothesize that analyzing LVW morphology over time, through spatiotemporal analysis, can predict MACE risk in CKD patients. However, accurately delineating and analyzing LVW at every frame is challenging due to noise, poor resolution, and the need for manual intervention. Our contribution includes (a) developing an automated pipeline for identifying and standardizing heart-beat cycles and segmenting the LVW, (b) introducing a novel computational biomarker—STAR-Echo—which combines spatiotemporal risk from radiomic (MR ) and deep learning (MT ) models to predict MACE prognosis in CKD patients, and (c) demonstrating the superior prognostic performance of STAR-Echo compared to MR, MT, as well as clinical-biomarkers (EF, BNP, and NT-proBNP) for characterizing cardiac dysfunction. STAR-Echo captured the gray level intensity distribution, perimeter and sphericity of the LVW that changes differently over time in individuals who encounter MACE outcomes. STAR-Echo achieved an AUC of 0.71 [ 0.53 - 0.89 ] for MACE outcome classification and also demonstrated prognostic ability in Kaplan-Meier survival analysis on a holdout cohort (Sv= 44 ) of CKD patients (N= 150 ). It achieved superior MACE prognostication (p-value = 0.037 (log-rank test)), compared to MR (p-value = 0.042), MT (p-value = 0.069), clinical biomarkers—EF, BNP, and NT-proBNP (p-value >0.05).
AB - Chronic Kidney Disease (CKD) patients are at higher risk of Major Adverse Cardiovascular Events (MACE). Echocardiography evaluates left ventricle (LV) function and heart abnormalities. LV Wall (LVW) pathophysiology and systolic/diastolic dysfunction are linked to MACE outcomes (O- and O+ ) in CKD patients. However, traditional LV volume-based measurements like ejection-fraction offer limited predictive value as they rely only on end-phase frames. We hypothesize that analyzing LVW morphology over time, through spatiotemporal analysis, can predict MACE risk in CKD patients. However, accurately delineating and analyzing LVW at every frame is challenging due to noise, poor resolution, and the need for manual intervention. Our contribution includes (a) developing an automated pipeline for identifying and standardizing heart-beat cycles and segmenting the LVW, (b) introducing a novel computational biomarker—STAR-Echo—which combines spatiotemporal risk from radiomic (MR ) and deep learning (MT ) models to predict MACE prognosis in CKD patients, and (c) demonstrating the superior prognostic performance of STAR-Echo compared to MR, MT, as well as clinical-biomarkers (EF, BNP, and NT-proBNP) for characterizing cardiac dysfunction. STAR-Echo captured the gray level intensity distribution, perimeter and sphericity of the LVW that changes differently over time in individuals who encounter MACE outcomes. STAR-Echo achieved an AUC of 0.71 [ 0.53 - 0.89 ] for MACE outcome classification and also demonstrated prognostic ability in Kaplan-Meier survival analysis on a holdout cohort (Sv= 44 ) of CKD patients (N= 150 ). It achieved superior MACE prognostication (p-value = 0.037 (log-rank test)), compared to MR (p-value = 0.042), MT (p-value = 0.069), clinical biomarkers—EF, BNP, and NT-proBNP (p-value >0.05).
UR - http://www.scopus.com/inward/record.url?scp=85174679813&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174679813&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43987-2_28
DO - 10.1007/978-3-031-43987-2_28
M3 - Conference contribution
AN - SCOPUS:85174679813
SN - 9783031439865
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 284
EP - 294
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
A2 - Greenspan, Hayit
A2 - Greenspan, Hayit
A2 - Madabhushi, Anant
A2 - Mousavi, Parvin
A2 - Salcudean, Septimiu
A2 - Duncan, James
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Russell
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 October 2023 through 12 October 2023
ER -