ArticleQuantitative imaging in medicine and surgery2026
Multiparametric MRI-derived biomarkers for preoperative prediction of recurrence and/or metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The early prediction of recurrence and/or metastasis (RM) in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT) remains a clinical challenge. This study aimed to investigate the predictive value of quantitative parameters derived from multiparametric magnetic resonance imaging (mpMRI) before and after nCRT for assessing RM risk. Methods: A total of 86 patients with LARC who underwent nCRT followed by total mesorectal excision (TME) were retrospectively analyzed. All patients received mpMRI scans before and after nCRT, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI). Quantitative parameters such as apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), and rate constant (Kep) were measured. Change rates (e.g., ΔADC%, ΔKtrans%) were calculated. Univariate logistic regression analyses were conducted to identify predictors of RM (P<0.05). A nomogram was developed based on the final combined model and validated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact curves (CICs). Results: Among the 86 patients, 25 (29.1%) developed RM within 3 years. ΔADC%, post-Ktrans, and ΔKtrans% showed statistically significant differences between the RM group and the non-recurrence and non-metastasis group (all P<0.05), and univariate logistic regression analysis confirmed these three parameters as significant predictive factors for RM (all P<0.05); variance inflation factor (VIF) analysis (all values <3) ruled out severe multicollinearity among them. The combined model incorporating ΔADC%, post-Ktrans, and ΔKtrans% showed the highest predictive performance [area under the curve (AUC) =0.908], significantly outperforming each individual parameter. The nomogram demonstrated good calibration and net clinical benefit. Longitudinal analysis revealed that patients without RM exhibited increased ADC and decreased perfusion parameters post-nCRT, whereas those with RM showed the opposite trends. Conclusions: Quantitative parameters derived from mpMRI, particularly ΔADC% and ΔKtrans%, are valuable for preoperative prediction of RM in LARC patients after nCRT. The developed nomogram offers a practical tool to assist individualized risk assessment and guide post-treatment strategies.
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