Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging

Objective: To explore an MRI-based radiomics nomogram for preoperatively predicting of pelvic lymph node (PLN) metastasis in patients with early-stage cervical cancer (ECC). Methods: Ninety-six patients with ECC were enrolled in this study. All patients underwent T2WI and DWI scans before radical hysterectomy with PLN dissection surgery. Radiomics features extracted from T2WI and DWI were selected by least absolute shrinkage and selection operation regression for further radimoics signature calculation. The discrimination of this radiomics signature for PLN metastasis was then assessed using a support vector machine (SVM) model. Subsequently, a radiomics nomogram was constructed based on the radiomics signature and clinicopathologic risk factors using a multivariable logistic regression method. The performance of the radiomics nomogram for the preoperative prediction of PLN metastasis was evaluated for discrimination and calibration. Results: The radiomics signatures demonstrated a good discrimination for PLN metastasis. A radiomics signature derived from joint T2WI and DWI yielded higher AUC than the signatures derived from T2WI or DWI alone. The radiomics nomogram integrating the radiomics signature with clinicopathologic risk factors showed a significant improvement over the nomogram based only on clinicopathologic risk factors in the primary cohort(C-index, 0.893 vs. 0.616; P = 4.311×10 ^-5 ) and validation cohort(C-index, 0.922 vs. 0.799; P = 3.412 ×10 ^-2 ).The calibration curves also showed good agreement. Conclusions: The radiomics nomogram based on joint T2WI and DWI demonstrated an improved prediction ability for PLN metastasis in ECC. This noninvasive and convenient tool may be used to facilitate preoperative identification of PLN metastasis in patients with ECC.