Cardiac Magnetic Resonance to Predict Cardiac Mass Malignancy: The CMR Mass Score

Pasquale Paolisso; Luca Bergamaschi; Francesco Angeli; Marta Belmonte; Alberto Foà; Lisa Canton; Damiano Fedele; Matteo Armillotta; Angelo Sansonetti; Francesca Bodega; Sara Amicone; Nicole Suma; Emanuele Gallinoro; Domenico Attinà; Fabio Niro; Paola Rucci; Elisa Gherbesi; Stefano Carugo; Saima Musthaq; Andrea Baggiano; Anna Giulia Pavon; Marco Guglielmo; Edoardo Conte; Daniele Andreini; Gianluca Pontone; Luigi Lovato; Carmine Pizzi

doi:10.1161/CIRCIMAGING.123.016115

Cardiac Magnetic Resonance to Predict Cardiac Mass Malignancy: The CMR Mass Score

Circ Cardiovasc Imaging. 2024 Mar;17(3):e016115. doi: 10.1161/CIRCIMAGING.123.016115. Epub 2024 Mar 19.

Authors

Pasquale Paolisso^#^{1

2

3}, Luca Bergamaschi^#^{4

5

6}, Francesco Angeli^{4

5

6}, Marta Belmonte^{3

7}, Alberto Foà^{4

5

6}, Lisa Canton^{4

5

6}, Damiano Fedele^{4

5

6}, Matteo Armillotta^{4

5

6}, Angelo Sansonetti^{4

5

6}, Francesca Bodega^{4

5

6}, Sara Amicone^{4

5

6}, Nicole Suma^{4

5

6}, Emanuele Gallinoro^{1

2

8}, Domenico Attinà^{1

4

6}, Fabio Niro^{4

5

6}, Paola Rucci⁹, Elisa Gherbesi¹⁰, Stefano Carugo^{10

8}, Saima Musthaq¹¹, Andrea Baggiano^{10

11}, Anna Giulia Pavon¹², Marco Guglielmo¹³, Edoardo Conte^{1

2}, Daniele Andreini^{2

5}, Gianluca Pontone^{10

14}, Luigi Lovato^#^{4

5

6}, Carmine Pizzi^#^{4

5

6}

Affiliations

¹ Clinical Cardiology and Cardiovascular Imaging Unit, Galeazzi-Sant'Ambrogio Hospital, IRCCS, Milan, Italy (P.P., E. Gallinoro, E.C., D.A.).
² Department of Biomedical and Clinical Sciences (P.P., E. Gallinoro, E.C., D.A.), University of Milan, Italy.
³ Department of Advanced Biomedical Sciences, University of Naples, Federico II, Italy (P.P., M.B.).
⁴ Cardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliera-Universitaria di Bologna (L.B., F.A., A.F., L.C., D.F., M.A., A.S., F.B., S.A., N.S., D.A., F.N., L.L., C.P.).
⁵ Department of Medical and Surgical Sciences, DIMEC (L.B., F.A., A.F., L.C., D.F., M.A., A.S., F.B., S.A., N.S., D.A., F.N., L.L., C.P.).
⁶ Alma Mater Studiorum (L.B., F.A., A.F., L.C., D.F., M.A., A.S., F.B., S.A., N.S., D.A., F.N., L.L., C.P.), University of Bologna, Italy.
⁷ Cardiovascular Center Aalst, OLV Hospital, Aalst, Belgium (M.B.).
⁸ Department of Cardio-Thoracic-Vascular Diseases, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy (E. Gherbesi, S.C.).
⁹ Division of Hygiene and Biostatistics, Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum (P.R.), University of Bologna, Italy.
¹⁰ Department of Clinical Sciences and Community Health (E. Gherbesi, S.C., A.B., G.P.), University of Milan, Italy.
¹¹ Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy (S.M., A.B.).
¹² Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, Lugano, Switzerland (A.G.P.).
¹³ Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, the Netherlands (M.G.).
¹⁴ Department of Biomedical, Surgical and Dentals Sciences (G.P.), University of Milan, Italy.

^# Contributed equally.

Abstract

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.

Keywords: cardiac magnetic resonance (CMR); cardiac masses; echocardiography; prognosis.

Publication types

Observational Study

MeSH terms

Heart Neoplasms* / diagnosis
Humans
Magnetic Resonance Imaging, Cine / methods
Magnetic Resonance Spectroscopy
Pericardial Effusion*
Predictive Value of Tests
Prognosis