Deep Learning for Synthetic Postcontrast T1-Weighted MRI: A Systematic Review With Targeted Meta-Analysis of Brain Tumor Studies.

Background. Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) methods have emerged as a potential approach for synthesizing postcontrast T1-weighted images from precontrast sequences alone. Objective. The objective of this study was to systematically review DL-based synthesis of postcontrast T1-weighted MRI, characterize model architectures and evaluation practices across subspecialties, and perform targeted meta-analysis where sufficient literature existed. Evidence Acquisition. A systematic search of PubMed, Embase, Cochrane Central, Scopus, and Web of Science (through January 16, 2025) identified peer-reviewed studies using DL to synthesize postcontrast T1-weighted MRI from precontrast sequences in adults. Two reviewers independently screened studies, extractingdata on subspecialty, architecture, quantitative metrics, pathology-specific evaluation, and reader studies. Risk of bias was assessed using modified QUADAS-2. Random-effects meta-analysis was performed for brain tumor studies. Evidence Synthesis. Of 268 records after deduplication, 41 met inclusion criteria. Most studies focused on neuroimaging (n = 24, 59%), followed by breast (n = 7, 17%) and body imaging (n = 6, 15%). Generative adversarial networks (n = 20, 45%) and convolutional neural networks (n = 19, 43%) predominated. Structural similarity index measure (SSIM, n = 31, 76%) and peak SNR (PSNR, n = 28, 68%) were the most common metrics. Fifty-one percent (n = 21) of studies performed pathology-specific evaluation, which showed substantially lower SSIM and PSNR compared with whole-image metrics. Thirty-seven percent (n = 15) included reader studies, 29% (n = 12) released code, and 61% (n = 25) used single-institution data. Meta-analysis of 15 brain tumor studies (30 models) yielded pooled SSIM of 0.92 (95% CI, 0.90-0.93) and PSNR of 30.6 dB (95% CI, 28.6-32.6). Given extreme heterogeneity (I2 > 99%), pooled estimates should be interpreted as descriptive. Conclusion. DL-based postcontrast MRI synthesis shows technical feasibility across subspecialties but suffers from substantial heterogeneity in study design, inconsistent quantitative metric computation, and limited clinical validation. Clinical Impact. Limited rates of reader studies and external validation represent key barriers to clinical translation of DL-based postcontrast MRI synthesis. Standardized evaluation workflows incorporating whole-image metrics, pathology-specific assessment, and reader studies are essential before these techniques can be translated into clinical practice.