Diagnosis of Cervical Cancer With Parametrial Invasion on Whole-Tumor Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined With Whole-Lesion Texture Analysis Based on T2- Weighted Images - Li, Xin-xiang; Lin, Ting-ting; Liu, Bin; Wei, Wei (Frontiers in Bioengineering and Biotechnology, 2020)
Purpose: To evaluate the diagnostic value the combination of the whole-tumor dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and whole-lesion texture features based on T2WI for cervical cancer with parametrial invasion. Materials and Methods: Sixty-two patients with cervical cancer (27 parametrial invasion and 35 non-invasion) underwent preoperatively routine MRI sequences and DCE-MRI examinations. DCE-MRI parameters (Ktrans, Kep, and Ve) and texture feature (meanvalue, skewness, kurtosis, uniformity, energy, and entropy) based on T2WI were acquired by two observers. All parameters of the parametrial invasion and non-invasion were analyzed by one-way anaylsis variance. The diagnostic efficency of significant variables were assessed using receiver operating characteristic (ROC) analysis. Results: The invasion group of cervical cancer demonstrated significantly higher Ktrans (0.335±0.050 vs. 0.269±0.079; p<0.001), lower energy values (0.503±0.093 vs. 0.602±0.087; p<0.001), and higher entropy values (1.391±0.193 vs. 1.24±0.129; p<0.001) than the non-invasion group. Optimal diagnostic performance (area under curve [AUC], 0.925; sensitivity, 0.935; specificity, 0.829) could be obtained by the combination of Ktrans, energy, and entropy values as the diagnostic indication. While the AUC values of Ktrans (0.788), energy (0.761), entropy (0.749), the combination of Ktrans and energy (0.814), the combination of Ktrans and entropy (0.727), and the combination of energy and entropy (0.619) were lower than the combination of Ktrans, energy, and entropy values. Conclusion: Ktrans, energy, and entropy could help to diagnose the cervical cancer with parametrial infiltration. The combination of DCE-MRI and texture analysis based on T2WI might impove diagnosis performance.