Zujun Hou

MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study

Background Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs). Purpose To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting. Material and Methods Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1–3 were utilized for training and validation, and the images from devices 4–7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models. Results The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists. Conclusion Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.

A comparative study of four diffusion-weighted imaging models in the diagnosis of cervical cancer

Background Most commonly used diffusion-weighted imaging (DWI) models include intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and mono-exponential model (MEM). Previous studies of the four models were inconsistent on which model was more effective in distinguishing cervical cancer from normal cervical tissue. Purpose To assess the performance of four DWI models in characterizing cervical cancer and normal cervical tissue. Material and Methods Forty-seven women with suspected cervical carcinoma underwent DWI using eight b-values before treatment. Imaging parameters, calculated using IVIM, SEM, DKI, and MEM, were compared between cervical cancer and normal cervical tissue. The diagnostic performance of the models was evaluated using independent t-test, Mann–Whitney U test, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression analysis. Results All parameters except pseudo-diffusion coefficient (D*) differed significantly between cervical cancer and normal cervical tissue ( P < 0.001). Through logistic regression analysis, all combined models showed a significant improvement in area under the ROC curve (AUC) compared to individual DWI parameters. The model with combined IVIM parameters had a larger AUC value compared to those of other combined models ( P < 0.05). Conclusion All four DWI models are useful for differentiating cervical cancer from normal cervical tissue and IVIM may be the optimal model.

A multicenter study of cervical cancer using quantitative diffusion-weighted imaging

Background Parameters from diffusion-weighted imaging (DWI) have been increasingly used as imaging biomarkers for the diagnosis and monitoring of treatment responses in cancer. The consistency of DWI measurements across different centers remains uncertain, which limits the widespread use of quantitative DWI in clinical settings. Purpose To investigate the consistency of quantitative metrics derived from DWI between different scanners in a multicenter clinical setting. Material and Methods A total of 193 patients with cervical cancer from four scanners (MRI1, MRI2, MRI3, and MRI4) at three centers were included in this retrospective study. DWI data were processed using the mono-exponential and intravoxel incoherent motion (IVIM) model, yielding the following parameters: apparent diffusion coefficient (ADC); true diffusion coefficient (D); pseudo-diffusion coefficient (D*); perfusion fraction (f); and the product of f and D* (fD*). Various parameters of cervical cancer obtained from different scanners were compared. Results The parameters D and ADC derived from MRI1 and MRI2 were significantly different from those derived from MRI3 or MRI4 ( P <0.01 for all comparisons). However, there was no significant difference in cervical cancer perfusion parameters (D* and fD*) between the different scanners ( P >0.05). The P values of comparisons of all DWI parameters (D, D*, fD*, and ADC) between MRI3 and MRI4 (same vendor in different centers) for cervical cancer were all >0.05, except for f ( P = 0.05). Conclusion Scanners of the same model by the same vendor can yield close measurements of the ADC and IVIM parameters. The perfusion parameters showed higher consistency among the different scanners.