According to the 2018 FIGO criteria, surgery is the primary treatment for patients with early-stage squamous cervical carcinoma. LVSI has an important influence on surgery and patient prognosis according to the FIGO criteria. Although most patients have excellent prognosis, approximately 30% patients might have recurrence and decreased survival rate. Many studies have proven that LVSI is closely associated with prognosis and is an independent risk factor[2–4, 6, 7]. The expression of some proteins, including COX-2, TNC, and others, are related to LVSI, tumor microenvironment, and inflammation[11, 12, 21]. Hence, accurate and early assessment of LVSI are important in prognosis assessment and treatment decision making in order to ensure that patients can obtain the maximum treatment benefit.
Our study aimed to establish a machine learning model that combines radiomics derived from PET images with molecular proteins that are associated with the pathology of cervical cancer in order to predict LVSI in patients with early-stage cervical cancer. The results indicate that the Rad-score was closely correlated with LVSI, and there were statistically significant differences in COX-2, TNC, and the Rad-score between the LVSI and non-LVSI groups. Moreover, we determined that the model based on the Rad-score could predict LVSI. When the Rad-score and molecular protein expression were combined, the AUC of the model improved a bit in the testing data set, but the DeLong test showed no statically significant difference between the two models in the testing data set.
Malignant tumors exhibit intratumoral biological heterogeneity and lead to changes in the texture parameters of the corresponding primary tumor on PET images. A previous study determined that heterogenic FDG uptake within a tumor correlated with intratumoral histopathological appearance. Mu et al found that inverse difference moment and correlation showed statistically significant differences between the early (stages I and II) and advanced stages (stages III and IV) of cervical cancer. Similarly, inverse difference moment and correlation were selected to calculate the Rad-score for predicting LVSI in our study. Recently, Li et al study showed that the PET textures of primary tumor could predict lymphatic metastasis in early-stage cervical carcinoma (AUC = 0.757 in the validation data set; 95% CI, 0.545–0.904; p < 0.05). Other research have also shown that radiomics of primary tumor based on PET images could reflect tumor malignancy and were associated with nodal metastases and molecular subtypes of solid tumor[24, 25]. The Rad-score, which is calculated by the linear combination of selected features (including histogram and texture parameters) weighted by their respective coefficients selected as informative features, is usually used for radiomics analysis. In the present study, the Mann-Whitney U test showed that the Rad-score and TNC had significant differences between the LVSI and non-LVSI in all data sets, and the LVSI group had a higher Rad-score than the non-LVSI group in Fig. 5.
A previous research found that the molecular expression of some proteins was correlated with LVSI. Normal cervical tissues have weak expression of TNC and COX-2. Previously, Pilch et al determined that, in invasive cervical carcinoma, TNC expression was markedly increased. Other studies have proven that TNC has a significant role in tumor growth, migration, metastasis, angiogenesis, and stromal inflammation[14, 27, 28]. The study of Liu et al found that COX-2 expression was associated with lymphangiogenesis and lymph node metastasis in cervical cancer. Similarly, Hoellen et al proved that COX‑2 expression was significantly associated with LVSI (p = 0.017). Similarly, our study also found that the differences in COX-2 and TNC between the LVSI and non-LVSI groups were statically significant. In the multivariate logistic regression, TNC expression was associated with LVSI. Furthermore, COX-2 had a slight correlation with TNC (Fig. 4). We hypothesized that the result may be caused by the inflammatory microenvironment of the tumor. Liu et al demonstrated that COX-2 may promote cancer progression and metastasis by enhancing the expression of vascular endothelial growth factor C and other mechanisms. The research showed that TNC could also facilitate the formation of cancer stroma, including desmoplasia and angiogenesis, and enhanced inflammation in the cancer stroma may augment macrophage recruitment and secretion of tumor-promoting and inflammatory cytokines by macrophages and fibroblasts. Thus, TNC and COX-2 were selected to predict LVSI in cervical cancer.
Although some protein expression and radiomics were associated with LVSI in cervical carcinoma, the correlation of molecular proteins and radiomics has been rarely explored and confirmed. In our study, the Rad-score was correlated with TNC, according to the Spearman correlation analysis (Fig. 4). Thus, we assumed that the PET imaging textures of primary tumors changed through TNC and other proteins. The textures of primary tumor reflect the heterogeneity of tumor, and they could be used to predict LVSI in cervical carcinoma. Another research also hoped to utilize the expression of TNC on PET imaging by devising a new PET tracer. However, our result initially showed that radiomics derived from PET imaging provided a new possibility for non-invasive visualization of TNC expression. Song et al also found that the image signal changes on magnetic resonance imaging (MRI) were consistent with TNC expression, and cervical cancer tissues with node metastasis had the highest TNC expression.
Three machine learning models were established with logistic regression algorithm in the training data set and evaluated in the testing data set. All three models performed well in the training data set (Table 4), but the radiomics model had the highest AUC in the training data set. However, in the testing data set, the AUC value of the combined model was higher than that of the other models (Table 4). The reason for the results was that our data set was slightly smaller. Thus, we used all data sets to perform the multivariate logistic regression analysis (Table 3). The results also showed that the Rad-score and TNC were associated with LVSI in all data sets. Two different methods (statistics and machine learning) both confirmed that the combination of radiomics and TNC could predict LVSI in early-stage cervical cancer. The combined model for predicting LVSI was credible. The DeLong test also indicated that the AUC of the ROC of the combined model was better than that of the protein model in the training data set (Table 5). Previously, a few researchers also wanted to predict or distinguish LVSI through radiology for cervical cancer. Yang et al determined found that the minimum apparent diffusion coefficient and the minimum apparent diffusion coefficient ratio were significantly lower in LVI-positive invasive cervical cancer than in LVI-negative invasive cervical cancer (0.772 ± 0.062 vs. 0.917 ± 0.052, p < 0.001, and 0.712 ± 0.078 × 10−3 vs. 0.867 ± 0.099 × 10−3 mm2/s, p < 0.001, respectively). Gross tumor volume on MRI was also identified to be a possible independent risk factor for predicting LVSI (AUC = 0.700, p < 0.05). Recently, the use of radiomics based on magnetic resonance for predicting LVSI has been studied. According to Hua et al, the model based on multiparametric MRI showed the best prediction results, with an AUC of 0.842 (95% CI, 0.772–0.913; sensitivity = 0.773; specificity = 0.776) in the training cohort and 0.775 (95% CI, 0.637–0.912; sensitivity = 0.739; specificity = 0.667) in the validation cohort. Similarly, Li et al also found that the radiomics nomogram derived from MRI showed favorable discrimination between LVSI and non-LVSI groups, with an AUC of 0.754 (95% CI, 0.6326–0.8745) in the training cohort and 0.727 (95% CI, 0.5449–0.9097) in the validation cohort. We initially used the combination of PET radiomics with protein molecule to predict LVSI, which showed that the radiomics and combined models based on 18F-FDG PET imaging showed better results than those of previous studies.
However, this study has some limitations. First, the size of the data set was inadequate; thus, we need a larger number of data set to test our models as well as multicenter imaging data to evaluate reproducibility. Second, we only analyzed the association of the expression of TNC and COX-2 with LVSI. In the future, we hope to perform more protein analyses and explore the correlation of DNA with LVSI and the function of radiogenomics in order to predict LVSI. Finally, the PET image resolution was low, thus limiting the precision of the segment of tumor VOI as well as the extraction of the radiomics features.