TY - JOUR
T1 - Cardiac Magnetic Resonance to Predict Cardiac Mass Malignancy
T2 - The CMR Mass Score
AU - Paolisso, Pasquale
AU - Bergamaschi, Luca
AU - Angeli, Francesco
AU - Belmonte, Marta
AU - Foà, Alberto
AU - Canton, Lisa
AU - Fedele, Damiano
AU - Armillotta, Matteo
AU - Sansonetti, Angelo
AU - Bodega, Francesca
AU - Amicone, Sara
AU - Suma, Nicole
AU - Gallinoro, Emanuele
AU - Attinà, Domenico
AU - Niro, Fabio
AU - Rucci, Paola
AU - Gherbesi, Elisa
AU - Carugo, Stefano
AU - Musthaq, Saima
AU - Baggiano, Andrea
AU - Pavon, Anna Giulia
AU - Guglielmo, Marco
AU - Conte, Edoardo
AU - Andreini, Daniele
AU - Pontone, Gianluca
AU - Lovato, Luigi
AU - Pizzi, Carmine
N1 - Publisher Copyright:
© 2024 Lippincott Williams and Wilkins. All rights reserved.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - BACKGROUND: Multimodality imaging is currently suggested for the noninvasive diagnosis of cardiac masses. The identification of cardiac masses' malignant nature is essential to guide proper treatment. We aimed to develop a cardiac magnetic resonance (CMR)-derived model including mass localization, morphology, and tissue characterization to predict malignancy (with histology as gold standard), to compare its accuracy versus the diagnostic echocardiographic mass score, and to evaluate its prognostic ability. METHODS: Observational cohort study of 167 consecutive patients undergoing comprehensive echocardiogram and CMR within 1-month time interval for suspected cardiac mass. A definitive diagnosis was achieved by histological examination or, in the case of cardiac thrombi, by histology or radiological resolution after adequate anticoagulation treatment. Logistic regression was performed to assess CMR-derived independent predictors of malignancy, which were included in a predictive model to derive the CMR mass score. Kaplan-Meier curves and Cox regression were used to investigate the prognostic ability of predictors. RESULTS: In CMR, mass morphological features (non-left localization, sessile, polylobate, inhomogeneity, infiltration, and pericardial effusion) and mass tissue characterization features (first-pass perfusion and heterogeneity enhancement) were independent predictors of malignancy. The CMR mass score (range, 0-8 and cutoff, ≥5), including sessile appearance, polylobate shape, infiltration, pericardial effusion, first-pass contrast perfusion, and heterogeneity enhancement, showed excellent accuracy in predicting malignancy (areas under the curve, 0.976 [95% CI, 0.96-0.99]), significantly higher than diagnostic echocardiographic mass score (areas under the curve, 0.932; P=0.040). The agreement between the diagnostic echocardiographic mass and CMR mass scores was good (κ=0.66). A CMR mass score of ≥5 predicted a higher risk of all-cause death (P<0.001; hazard ratio, 5.70) at follow-up. CONCLUSIONS: A CMR-derived model, including mass morphology and tissue characterization, showed excellent accuracy, superior to echocardiography, in predicting cardiac masses malignancy, with prognostic implications.
AB - BACKGROUND: Multimodality imaging is currently suggested for the noninvasive diagnosis of cardiac masses. The identification of cardiac masses' malignant nature is essential to guide proper treatment. We aimed to develop a cardiac magnetic resonance (CMR)-derived model including mass localization, morphology, and tissue characterization to predict malignancy (with histology as gold standard), to compare its accuracy versus the diagnostic echocardiographic mass score, and to evaluate its prognostic ability. METHODS: Observational cohort study of 167 consecutive patients undergoing comprehensive echocardiogram and CMR within 1-month time interval for suspected cardiac mass. A definitive diagnosis was achieved by histological examination or, in the case of cardiac thrombi, by histology or radiological resolution after adequate anticoagulation treatment. Logistic regression was performed to assess CMR-derived independent predictors of malignancy, which were included in a predictive model to derive the CMR mass score. Kaplan-Meier curves and Cox regression were used to investigate the prognostic ability of predictors. RESULTS: In CMR, mass morphological features (non-left localization, sessile, polylobate, inhomogeneity, infiltration, and pericardial effusion) and mass tissue characterization features (first-pass perfusion and heterogeneity enhancement) were independent predictors of malignancy. The CMR mass score (range, 0-8 and cutoff, ≥5), including sessile appearance, polylobate shape, infiltration, pericardial effusion, first-pass contrast perfusion, and heterogeneity enhancement, showed excellent accuracy in predicting malignancy (areas under the curve, 0.976 [95% CI, 0.96-0.99]), significantly higher than diagnostic echocardiographic mass score (areas under the curve, 0.932; P=0.040). The agreement between the diagnostic echocardiographic mass and CMR mass scores was good (κ=0.66). A CMR mass score of ≥5 predicted a higher risk of all-cause death (P<0.001; hazard ratio, 5.70) at follow-up. CONCLUSIONS: A CMR-derived model, including mass morphology and tissue characterization, showed excellent accuracy, superior to echocardiography, in predicting cardiac masses malignancy, with prognostic implications.
KW - Heart Neoplasms/diagnosis
KW - Humans
KW - Magnetic Resonance Imaging, Cine/methods
KW - Magnetic Resonance Spectroscopy
KW - Pericardial Effusion
KW - Predictive Value of Tests
KW - Prognosis
KW - cardiac masses
KW - echocardiography
KW - cardiac magnetic resonance (CMR)
KW - prognosis
UR - http://www.scopus.com/inward/record.url?scp=85188351376&partnerID=8YFLogxK
U2 - 10.1161/CIRCIMAGING.123.016115
DO - 10.1161/CIRCIMAGING.123.016115
M3 - Article
C2 - 38502734
SN - 1941-9651
VL - 17
JO - Circulation. Cardiovascular imaging
JF - Circulation. Cardiovascular imaging
IS - 3
M1 - e016115
ER -