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Erschienen in:
17.02.2020 | Imaging Informatics and Artificial Intelligence
Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram
Erschienen in: European Radiology | Ausgabe 6/2020
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Purpose
To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).
Materials and methods
All 137 patients with ECC (FIGO stages IB–IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient’s age, and pathological grade; its discrimination and calibration performances were estimated.
Results
For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682–0.911) vs 0.946 (95% CI, 0.899–0.994), and 0.780 (95% CI, 0.641–0.920) vs 0.921 (95% CI, 0.832–1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient’s age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933–1) and 0.941 (95% CI, 0.868–1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement.
Conclusions
The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC.
Key Points
• No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC.
• Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC.
• Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.