Development and Validation of a Nomogram for Predicting Postoperative Distant Metastasis in Patients with Cervical Cancer

Distant organ metastasis patterns and prognosis of cervical adenocarcinoma: a population-based retrospective study

Background

Adenocarcinoma is a common histological subtype of cervical cancer, accounting for 10-15% of all cases. The prognosis of cervical adenocarcinoma with distant organ metastases remains unclear. Therefore, our study aimed to investigate the patterns and prognosis of distant organ metastasis in cervical adenocarcinoma.

Methods

We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database spanning from 2010 to 2019. Cox regression, Kaplan-Meier, and log-rank analyses were conducted.

Results

We observed that adenocarcinoma (AC) of the cervix primarily metastasizes to single organs, with a rate of 73.3%. The lungs are the most common organs of metastasis, followed by the liver and bones. Patients with bone metastases have a median survival period of 12 months, which is slightly longer compared to metastasis in other organs. Distant organ metastasis, age, positive lymph nodes, higher AJCC stages, larger tumor diameter, and higher cell grades are related to poor prognosis (p < 0.001). Furthermore, we have observed that surgical intervention, radiotherapy, and chemotherapy can potentially provide benefits for patients with distant organ metastases.

Conclusion

Metastasis is an independent prognostic factor for cervical adenocarcinoma patients. Surgery, radiotherapy, and chemotherapy can provide an overall survival advantage for patients with distant organ metastases.

A novel nomogram and risk stratification for early metastasis in cervical cancer after radical radiotherapy

OBJECT

This study aimed to establish an effective risk nomogram to predict the early distant metastasis (EDM) probability of cervical cancer (CC) patients treated with radical radiotherapy to aid individualized clinical decision-making.

METHODS

A total of 489 patients with biopsy-confirmed CC between December 2018 and January 2021 were enrolled. Logistic regression with the stepwise backward method was used to identify independent risk factors. The nomogram efficacy was evaluated by using the area under the receiver operating characteristic curve (AUC), C-index by 1000 bootstrap replications, etc. Finally, patients were divided into high- and low-risk groups of EDM based on the cut-off value of nomogram points.

RESULTS

36 (7.36%) CC patients had EDM, and 20 (55.6%) EDM had more than one metastatic site involved. Age below 51 (OR = 2.298, p < 0.001), tumor size larger than 4.5 cm (OR = 3.817, p < 0.001) and radiotherapy (OR = 3.319, p < 0.001) were independent risk factors of EDM. For the nomogram model, C-index was 0.701 (95% CI = 0.604-0.798), and 0.675 (95% CI = 0.578-0.760) after 1000 bootstrap resampling validations. The Hosmer-Lemeshow test demonstrated no overfitting (p = 0.924). According to the Kaplan-Meier curve of risk score, patients with high risk were more prone to get EDM (p < 0.001).

CONCLUSION

This is the first research to focus on EDM in CC patients. We have developed a robust scoring system to predict the risk of EDM in CC patients to screen out appropriate cases for consolidation therapy and more intensive follow-up.

Development and validation of a predictive model for febrile seizures

Febrile seizures (FS) are the most prevalent type of seizures in children. Existing predictive models for FS exhibit limited predictive ability. To build a better-performing predictive model, a retrospective analysis study was conducted on febrile children who visited the Children's Hospital of Shanghai from July 2020 to March 2021. These children were divided into training set (n = 1453), internal validation set (n = 623) and external validation set (n = 778). The variables included demographic data and complete blood counts (CBCs). The least absolute shrinkage and selection operator (LASSO) method was used to select the predictors of FS. Multivariate logistic regression analysis was used to develop a predictive model. The coefficients derived from the multivariate logistic regression were used to construct a nomogram that predicts the probability of FS. The calibration plot, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used to evaluate model performance. Results showed that the AUC of the predictive model in the training set was 0.884 (95% CI 0.861 to 0.908, p < 0.001) and C-statistic of the nomogram was 0.884. The AUC of internal validation set was 0.883 (95% CI 0.844 to 0.922, p < 0.001), and the AUC of external validation set was 0.858 (95% CI 0.820 to 0.896, p < 0.001). In conclusion, the FS predictive model constructed based on CBCs in this study exhibits good predictive ability and has clinical application value.

Investigation of the apoptosis-inducing effect of docetaxel by a comprehensive LC-MS based metabolomics and network pharmacology approaches

Docetaxel is one of the clinical first-line drugs and its combination with other chemotherapy agents for advanced or metastatic cancers has attracted widespread attention. Therefore, to promote the clinical application of docetaxel alone or in combination, a comprehensive investigation of the metabolic mechanism of docetaxel is of great importance. Here, we apply an integrative analysis of metabolomics and network pharmacology to elucidate the underlying mechanisms of docetaxel. After taking the intersection of the above two methods, 5 pathways including ABC transporters, Central carbon metabolism in cancer, Glycolysis and Gluconeogenesis, Cysteine and methionine metabolism, and Arginine biosynthesis have been screened out. In concern of the interaction network of these pathways and the anti-apoptosis effect of docetaxel itself, the Central carbon metabolism in cancer pathway was mainly focused. This study may help delineate global landscapes of cellular protein-metabolite interactions, to provide molecular insights about their mechanisms of action, to promote the clinical applications at well.