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Restricted spectrum imaging and diffusion kurtosis imaging for assessing lymph node metastasis in cervical cancer: a
Jiayin Pan1,2, Wei Wei2, Bo Dai2
1Department of Radiology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, China.
Background:
Restricted spectrum imaging (RSI) and diffusion kurtosis imaging (DKI) provide comprehensive characterization of tumor microstructural features; however, the clinical utility for evaluating lymph node metastasis (LNM) in cervical cancer (CC) warrants further investigation.
Methods:
A total of 71 CC patients were enrolled, including 30 with negative LNM and 41 with positive LNM, all of whom underwent pelvic MRI examination. Chi-square tests, independent sample t tests, and Mann‒Whitney U tests were employed to calculate and compare histogram parameters from the DKI (D, K) and RSI (f1, f2, and f3) models. Logistic regression (LR) analysis was performed to identify predictive factors and evaluate the combined diagnostic performance. Receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) calculations and the Delong test were conducted to assess diagnostic efficacy. Bootstrap calibration curves and decision curve analysis (DCA) assessed the combined diagnostic model's stability and clinical utility.
Results:
Compared with the LNM-positive group, the LNM-negative group had higher Dmedian, D25th, D75th, Dmean, f3 median, f3 25th, f3 75th, f3 mean, and f1 SKE values (P < 0.05). The f1 median, f1 25th, f1 75th, f1 mean, DSKE, DKUR, and f3 SKE values in the LNM-negative group were lower than those in the LNM-positive group (P < 0.05). LR analysis showed that maximum diameter and DKI_Dmedian were independent predictors for status of LNM in CC. Moreover, the combination of these independent predictors achieved an optimal diagnostic efficacy (AUC = 0.874, sensitivity = 0.927, specificity = 0.767) that was significantly better than individual histogram parameters.
Conclusion:
DKI and RSI, along with their corresponding histogram parameters, demonstrate outstanding potential for predicting LNM in CC patients. Moreover, the combination of maximum diameter, maximum diameter and DKI_Dmedian may be a promising joint biomarker for predicting LNM in patients with CC..
