Patient clinical characteristics
This study initially comprised 76 patients from Center 1 and 55 patients from Center 2. Following the application of the exclusion criteria, a total of 82 patients were enrolled, with 60 from Center 1 and the remaining 22 from Center 2. The mean age of the patients in the training group was 58.9 ± 2.47 (range: 34–77 years), and the mean age of the patients in the test group was 59.27 ± 4.26(range: 36–81 years). As shown in Table 1, no significant differences were found between the train group and the test group in terms of clinicopathologic indicators such as age, FIGO, myofibrillar infiltration, Ki-67 expression, and P53 expression (all P > 0.05). However, in terms of Grade, a statistical difference was found between the train and test groups (P < 0.05).
PTEN, PI3KCA, mTOR expression status and clinicopathological features are shown in Table 2. There were significant differences in terms of PTEN expression and Ki-67 expression, mTOR expression and EC Grade (p < 0.05), and there were no significant differences in other clinicopathological features and protein expression status (p > 0.05).
Construction and Evaluation of Imaging Histology Models
A total of 10,525 features were extracted from seven categories of 12 image preprocessing, including first-order features, shape features, texture features, luminance features (GLSZM), grayscale judgment matrix (GLDM), neighboring grayscale levels (NGTDM), and grayscale stroke length texture features (GLRLM).
Among the 10525 radiological features extracted from the MRI images, 9304 features were considered stable (ICC ≥ 0.75). First, feature pre-selection was performed by removing features with a Pearson correlation coefficient ≥ 0.9. Next, univariate feature selection was performed using SelectPercentile (percentile = 80), and finally, the most valuable radiological features were selected from the training cohort using the Lasso algorithm.
This study incorporated 7 clinicopathologic features associated with PTEN (FIGO, Myometrial invasion, Ki-67, P53, Lymphatic node transfer, Vessel carcinoma embolus, Ovarian metastasis) and 5 MRI-based radiomic features. Additionally, 9 clinicopathologic features related to PI3KCA (Age, Ovarian metastasis, Myometrial invasion, Ki-67, P53, ER, PR, Lymphatic node transfer, Vessel carcinoma embolus) along with 4 MRI-based radiomics features were selected to establish the models. Moreover, 10 clinicopathologic features associated with mTOR (Age, Vessel carcinoma embolus, Ovarian metastasis, FIGO, Grade, Myometrial invasion, Ki-67, P53, ER, Lymphatic node transfer) and 2 MRI-based radiomic features were utilized in model construction.
The ROC curves of the three models for each protein expression in the training and validation sets are shown in Fig. 3. In the training set, the AUC values for PTEN expression prediction were 0.875, 0.703, and 0.891 for the Radscore, Clinicopathology, and Combination models, respectively. In the validation set, the corresponding AUC values were 0.750, 0.844, and 0.833 (Fig. a, b). For predicting PI3KCA expression, the training set AUC values for the models were 0.856, 0.633, and 0.880, while in the validation set, they were 0.842, 0.667, and 0.825(Fig. c, d). Lastly, in the training set, the AUC values for mTOR expression prediction were 0.896, 0.831, and 0.912 for the three models, respectively, with corresponding validation set AUC values of 0.729, 0.847, and 0.829 (Fig. e, f). The AUC, sensitivity, specificity, and accuracy values are presented in Table 3. Calibration curve analysis (Fig. 5) and decision curve analysis (Fig. 6) showed that the joint model had good calibration and clinical utility in predicting the expression of three different proteins.
Figure 4 shows the ROC curves for the prediction of each protein expression in the training and validation sets for the gross tumor volume (GTV), peritumoral tumor volume (PTV), and combined intratumor and peritumor, i.e., Gross peritumoral tumor volume (GPTV) radiomics models. While the AUC, sensitivity, specificity, and accuracy values are presented in Table 4. The results showed that the performance of the GPTV model was improved over both the GTV and PTV models in predicting PI3KCA and mTOR expression. However, the results of DeLong test suggested that, except for the GPTV model in predicting mTOR expression, which was statistically different from the intratumor model in the validation set (P < 0.05), the rest of the GPTV models were not statistically different from the GTV and PTV models in the training and validation sets (P > 0.05).