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An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors
Tongyu Wang1, Jingwei Hao1, Aixin Gao1
1Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Background:
Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem.
Purpose:
To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT.
Study Type:
Retrospective.
Population:
A total of 143 patients with 68.5% females (development/validation set, 98/45 patients).
Field Strength/Sequence:
A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C).
Assessment:
Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients.
Statistical Tests:
The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant.
Results:
The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated.
Data Conclusion:
A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model.
Evidence Level:
3.
Technical Efficacy:
Stage 5.
