Prognostic value of non-contrast myocardial T1 mapping in cardiovascular diseases: a systematic review and meta-analysis.

Myocardial fibrosis predisposes the development of main adverse cardiovascular events (MACEs) in various cardiac disorders. Native T1 derived from cardiac magnetic resonance allows the quantitative assessment of myocardial fibrosis without the use of contrast media. However, the prognostic value of native T1 in risk stratification remains uncertain. We searched MEDLINE®, Embase, and the Cochrane Library for cohort studies up to July 31, 2021, that reported prognostic data for native T1 in various cardiac disorders; the studies enrolling patients with myocardial iron or amyloid deposition, edema, and inflammation were excluded. A random effects meta-analysis was conducted. Heterogeneity was assessed using I2 statistic. Nineteen studies with 5,380 patients were included in this meta-analysis. Patients with MACEs had higher native T1 than those without [weighted mean difference: 27.35 (15.55-39.16), I2 = 23.2%]. The increase of native T1 per 1 ms [pooled adjusted hazard ratio (HR): 1.02 (1.00-1.03), I2 = 41.8%] and per ≥ 10 ms [pooled adjusted HR: 1.11 (1.07-1.16), I2 = 28.6%] was both associated with the development of MACEs; the categorical variable derived from native T1 also has the predicative value for MACEs [pooled adjusted HR: 5.97 (3.69-9.68), I2 = 0.0%].Myocardial native T1 potentially serves as a prognostic biomarker in patients with various cardiac disorders. Different variable definitions of native T1 have different positively predictive value for outcome; the categorical variable derived from native T1 may be more helpful in identifying high-risk patients.

Integration of MRI-Based Radiomics Features, Clinicopathological Characteristics, and Blood Parameters: A Nomogram Model for Predicting Clinical Outcome in Nasopharyngeal Carcinoma.

Purpose: This study aimed to develop a nomogram model based on multiparametric magnetic resonance imaging (MRI) radiomics features, clinicopathological characteristics, and blood parameters to predict the progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC). Methods: A total of 462 patients with pathologically confirmed nonkeratinizing NPC treated at Sichuan Cancer Hospital were recruited from 2015 to 2019 and divided into training and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomics feature dimension reduction and screening in the training cohort. Rad-score, age, sex, smoking and drinking habits, Ki-67, monocytes, monocyte ratio, and mean corpuscular volume were incorporated into a multivariate Cox proportional risk regression model to build a multifactorial nomogram. The concordance index (C-index) and decision curve analysis (DCA) were applied to estimate its efficacy. Results: Nine significant features associated with PFS were selected by LASSO and used to calculate the rad-score of each patient. The rad-score was verified as an independent prognostic factor for PFS in NPC. The survival analysis showed that those with lower rad-scores had longer PFS in both cohorts (p < 0.05). Compared with the tumor-node-metastasis staging system, the multifactorial nomogram had higher C-indexes (training cohorts: 0.819 vs. 0.610; validation cohorts: 0.820 vs. 0.602). Moreover, the DCA curve showed that this model could better predict progression within 50% threshold probability. Conclusion: A nomogram that combined MRI-based radiomics with clinicopathological characteristics and blood parameters improved the ability to predict progression in patients with NPC.