LNM is an paramount prognostic factor for patients with PCa, and has been proved to be an important predictor of BCR survival, metastasis free survival and overall survival of PCa.(Engel et al., 2010;Wilczak et al., 2018) Wessels et al. extracted prognostic information from the H&E histology of PCa and used the deep learning method to predict the LN status in PCa patients.(Wessels et al., 2021) Hou et al established PLNM risk calculator by integrating radiologist's interpretation, clinicopathologic factors and MRIs, and using ML and deep migration learning algorithms.(Hou et al., 2021) For the sake of accurately evaluating the risk of LNM, Some studies have designed different prediction models for lymph node prediction of intermediate and high-risk PCa according to the detection pathway. Diamand R et al. reported and validated the LNM of patients treated with ePLND by nomogram, and provided a more reasonable cut-off value.(Diamand et al., 2020) Ferraro DA et al. designed a new model by combining PSA, Gleason score and visual lymph node analysis on 68Ga-PSMA-11 PET. Compared with the previously used clinical nomograms, this model has an remarkably improved the positive rate of LNM in the patient selecting to perform ePLND.(Ferraro et al., 2020) In this study, we used the large sample size of SEER database and ML algorithm to develop six prediction models to predict LNM in the patients with intermediate and high-risk PCa. Logistic regression analysis showed that T stage, Gleason score, PSA and bone metastasis were independent risk factors for pelvic LNM of intermediate and high-risk PCa.
Among the six models, the AUC value of GBM model is the highest, and the prediction accuracy of other models for LNM is about 80%. RF model shows the best prediction performance before and after data balancing, with obvious advantages of high precision and fast speed, however, it also has the disadvantage of over fitting. F1 score, which represents the harmonic average of the accuracy rate and recall rate, is the final assessment parameter of the evaluating each model. According to the evaluation results of the test set, the prediction performance of GBM model is better than that of RF model. It can be seen that RF model may show over fitting in the training process, which makes it unsuitable for the data in the test set, while GBM model has the best prediction performance. In order to increase the application feasibility of this model, we developed a calculator to evaluate the individual probability of LNM in patients with intermediate and high-risk PCa.
The results of this study showed that T stage, PSA, Gleason score and bone metastasis were the most important predictors in the patients with intermediate and high-risk PCa. As an important indicator of tumor progression, T stage is positively correlated with LNM in a large number of tumors.(Barriera-Silvestrini et al., 2021) A large number of research data in this study show that the level of high PSA will increase the rate of lymph node invasion, which is contrary to the results of the previous studies. The possible reason is PSA may be more meaningful in D'Amico risk stratification. The increase of Gleason score also increases the risk of lymph node invasion.(Turk et al., 2018) Bone metastasis is significantly related to LNM of PCa, which can provide some ideas for follow-up research, that is, consider the existence of metastasis of other sites as a factor before patients have LNM.
The EAU guidelines used Briganti's nomogram prediction model to screen ePLND patients. The advantage of this study is to compare several models head-to-head with the nomogram model. The sensitivity, specificity and AUC of the nomogram are 0.882, 0.705 and 0.80 respectively, while the sensitivity, specificity and AUC of GBM are 0.877, 0.783 and 0.813 respectively. It shows that GBM in the six predictive models has the best predictive value for LNM in the patients with intermediate and high-risk PCa. In order to further facilitate clinical application, we designed a preliminary calculator model that can quickly calculate the probability of LNM.
Of course, this study has several limitations. First, this study is a retrospective study, which may have some selection bias. Secondly, SEER database lacks more data such as tumor volume, percentage of positive tissue cores, testosterone level, and so on. In addition, the external validation set data is small, and more sample sizes need to be included to test the effectiveness of the model. Finally, although we have corrected the sample imbalance problem of SEER dataset as much as possible, this problem will still interfere with the results and affect the generalization ability of the model.