Differential and prognostic value of cardiovascular magnetic resonance derived scoring algorithm in cardiac tumors

Objectives: To establish a scoring algorithm based on cardiovascular magnetic resonance (CMR) parameters for differentiating between benign and malignant cardiac tumors and for predicting outcome.

Methods: Patients referred for CMR for suspected cardiac tumors were prospectively enrolled. Tumors were categorized as benign or malignant based on pathology, imaging, and clinical information. The CMR protocol included cine, T1-weighted, T2-weighted, first-pass perfusion, and late gadolinium enhancement (LGE) sequences. Variables independently associated with malignancy in the multivariable logistic analysis were used to construct the scoring algorithm, and receiver operating characteristic analyses were used to assess the ability to discriminate malignant from benign tumors. The ability of the score to predict outcome (all-cause mortality) was also assessed by Kaplan-Meier survival analysis.

Results: Among the 105 enrolled patients, 74 had benign and 31 had malignant tumors. In multivariable analysis, the independent predictors of malignant tumors were invasiveness (odds ratio, OR = 11.4, 2 points), irregular border (OR = 5.8, 1 point), and heterogenous LGE (OR 10.6, 2 points). The area under curves (AUC) of the scoring algorithm was 0.912 (cut-off score of 5) and showed significantly higher AUCs than individual variables (all P < 0.05) in differentiating benign and malignant tumors. After median follow-up of 18.2 months, mortality was significantly higher in patients with a score of 5 than in patients with score ≤ 4.

Conclusions: The scoring algorithm based on CMR-detected invasiveness, irregularity of border, and heterogenous LGE is an effective method for differentiating malignant from benign cardiac tumors and for predicting outcome.