Quantitative imaging in medicine and surgery · 2025
Predicting glioma histo-molecular diagnosis and prognosis: preoperative dynamic contrast-enhanced magnetic resonance imaging insights.
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Ma Hui, Zeng Shanmei, Huang Yingqian, Xie Dingxiang, Mazu Liwei, Zhu Nengjin, Zhao Jing, Yang Zhiyun, Chu Jianping
Аннотация
BACKGROUND: The preoperative prediction of glioma, integrated histological/molecular classification, and prognosis are critical for personalized patient management and treatment optimization. This study aimed to explore whether preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), an advanced MRI technique, can comprehensively and noninvasively evaluate gliomas.
METHODS: Adult patients (June 2013 to May 2021) with diffuse glioma, retrospectively reclassified by the 2021 World Health Organization (WHO) classification criteria in this cohort study, underwent conventional and DCE-MRI examinations at our institution. Quantitative measurements, including the volume transfer constant (Ktrans), volume of extravascular extracellular space per unit volume of tissue (Ve), and rate constant of backflux (Kep), were derived from the tumor parenchyma areas. The diagnostic efficacy of glioma grading and genotyping, such as isocitrate dehydrogenase (IDH) and 1p/19q status, was evaluated using receiver operating characteristic (ROC) analysis. The prognostic predictive value was assessed using Cox or logistic regression analysis to build models visualized by nomograms. The area under the curve (AUC), calibration curves, and decision curves were used to evaluate the performance of these models.
RESULTS: The study population consisted of 101 participants [mean age ± standard deviation (SD), 47.05±12.81 years (72 males and 29 females)]. Tumor.Kep.max emerged as the most crucial parameter, serving as an independent protective predictor of 1p/19q-codeletion [odds ratio (OR) and 95% confidence interval (CI): 0.98 (0.97-0.996), AUC: 0.71 (0.58-0.82)], whereas it was negatively associated with high-grade gliomas [OR: 0.972 (0.950-0.996), AUC: 0.87 (0.80-0.94)] and IDH-mutant [OR: 1.02 (1.000-1.03), AUC: 0.72 (0.61-0.81)]. Tumor.Ve.max demonstrated excellent diagnostic value [AUC: 0.93 (0.86-1.00); sensitivity, 100%; specificity, 90.7%; P<0.001] for diagnosing cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) status. Tumor Ktrans.max emerged as an independent prognostic risk factor for glioma patients [AUCs for 1-, 3-, and 5-year survival, 0.75 (0.58-0.92), 0.64 (0.47-0.82), and 0.66 (0.43-0.89), respectively].
CONCLUSIONS: DCE-MRI technology holds significant value in glioma diagnosis, particularly in integrated molecular diagnostics, predicting grading, molecular genotype including IDH status, and prognosis, thereby offering a comprehensive preoperative evaluation framework. This approach enables clinicians to accurately stratify patients, thus optimizing treatment strategies.
METHODS: Adult patients (June 2013 to May 2021) with diffuse glioma, retrospectively reclassified by the 2021 World Health Organization (WHO) classification criteria in this cohort study, underwent conventional and DCE-MRI examinations at our institution. Quantitative measurements, including the volume transfer constant (Ktrans), volume of extravascular extracellular space per unit volume of tissue (Ve), and rate constant of backflux (Kep), were derived from the tumor parenchyma areas. The diagnostic efficacy of glioma grading and genotyping, such as isocitrate dehydrogenase (IDH) and 1p/19q status, was evaluated using receiver operating characteristic (ROC) analysis. The prognostic predictive value was assessed using Cox or logistic regression analysis to build models visualized by nomograms. The area under the curve (AUC), calibration curves, and decision curves were used to evaluate the performance of these models.
RESULTS: The study population consisted of 101 participants [mean age ± standard deviation (SD), 47.05±12.81 years (72 males and 29 females)]. Tumor.Kep.max emerged as the most crucial parameter, serving as an independent protective predictor of 1p/19q-codeletion [odds ratio (OR) and 95% confidence interval (CI): 0.98 (0.97-0.996), AUC: 0.71 (0.58-0.82)], whereas it was negatively associated with high-grade gliomas [OR: 0.972 (0.950-0.996), AUC: 0.87 (0.80-0.94)] and IDH-mutant [OR: 1.02 (1.000-1.03), AUC: 0.72 (0.61-0.81)]. Tumor.Ve.max demonstrated excellent diagnostic value [AUC: 0.93 (0.86-1.00); sensitivity, 100%; specificity, 90.7%; P<0.001] for diagnosing cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) status. Tumor Ktrans.max emerged as an independent prognostic risk factor for glioma patients [AUCs for 1-, 3-, and 5-year survival, 0.75 (0.58-0.92), 0.64 (0.47-0.82), and 0.66 (0.43-0.89), respectively].
CONCLUSIONS: DCE-MRI technology holds significant value in glioma diagnosis, particularly in integrated molecular diagnostics, predicting grading, molecular genotype including IDH status, and prognosis, thereby offering a comprehensive preoperative evaluation framework. This approach enables clinicians to accurately stratify patients, thus optimizing treatment strategies.