<![CDATA[Introduction: Weight-based dosing of gadolinium-based contrast agents (GBCA; 0.1 mmol/kg) in oncological MRI disregards individual haemodynamics and tumour microvascularity, contributing to avoidable cumulative exposure and tissue-retention risk. We evaluated an artificial-intelligence (AI)-assisted pharmacokinetic-modelling approach to personalise and reduce GBCA dose while preserving diagnostic performance. Methods: In this prospective, paired diagnostic-accuracy study reported per STARD 2015 at a tertiary hospital in Palembang, Indonesia, 152 adults (198 lesions) with histologically confirmed solid primary malignancies underwent 3.0-T contrast-enhanced MRI. A convolutional-neural-network extended-Tofts model derived each patient’s minimum effective gadobutrol dose, compared against the standard 0.1 mmol/kg protocol. Two radiologists, blinded to clinical data and to a composite reference standard (histopathology and ≥6-month imaging follow-up), assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), 5-point diagnostic confidence and lesion characterisation. Sensitivity, specificity, AUC (DeLong), likelihood ratios, Cohen’s κ and McNemar testing were computed with 95% confidence intervals. Results: The AI protocol reduced GBCA dose by 38.4% (4.6 vs 7.5 mL; p<0.001). Sensitivity was 95.2% (95% CI 90.9–97.6), specificity 83.3% (66.4–92.7) and AUC 0.94 (0.90–0.97) versus 0.95 (0.91–0.98) for standard dose (DeLong p=0.620; McNemar p=0.773). SNR and CNR were non-inferior (all p>0.05). Inter-reader agreement was substantial-to-almost-perfect (characterisation κ 0.83; confidence κ 0.88). Diagnostic adequacy was maintained in 149/152 cases (98%). Conclusion: AI-assisted pharmacokinetic modelling enabled a 38% gadolinium-dose reduction without loss of diagnostic accuracy or image quality, supporting personalised contrast administration and lower cumulative exposure in oncological MRI.]]>
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