Construction and validation of a prognostic model for stage IIIC endometrial cancer patients after surgery

Construction and validation of novel nomograms based on the log odds of positive lymph nodes to predict the prognosis of papillary thyroid cancer: a retrospective cohort study

Objective

This study aims to assess the long-term prognostic significance of the log odds of positive lymph nodes (LODDS) in patients diagnosed with papillary thyroid cancer (PTC) and to develop a novel nomogram for predicting long-term overall survival (OS).

Methods

The cohort was randomly divided at a ratio of 7:3 from the Surveillance, Epidemiology, and End Results (SEER) database. Additionally, patient data from a medical center in China served as an external validation cohort. Nomograms were constructed using data from the training cohort and subsequently validated using both internal and external validation cohorts to predict 120- and 180-month OS in PTC patients. The predictive performance and clinical utility of the nomogram were assessed using various metrics, including the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), Integrated Discriminant Improvement Index (IDI), and Net Reclassification Improvement Index (NRI).

Results

LODDS is an independent prognostic factor for PTC, a nomogram demonstrating high accuracy in predicting long-term OS. The C-index values, and time-dependent area under the curve (AUC) indicated well discriminatory ability of the nomogram. Calibration plots exhibited high concordance, while DCA, NRI, and IDI analyses revealed superior performance of the nomogram compared to AJCC staging system.

Conclusion

The clinical prediction model incorporating LODDS exhibits robust predictive performance, aiding in the assessment of long-term prognosis post-surgery in PTC patients. It serves as a valuable adjunct to the AJCC system, offering a scientific basis for guiding interventions and rehabilitation strategies for PTC patients following surgery.

Immunohistochemistry as a reliable predictor of remission in patients with endometrial cancer: Establishment and validation of a machine learning model

Endometrial cancer (EC) is the most common gynecologic cancer. Unfortunately, its prognosis remains poor due to limited screening and treatment options. To address this issue, the present study evaluated the predictive value of four immunohistochemical (IHC) indicators for overall survival (OS) and recurrence-free survival (RFS) in patients with EC. A total of 834 patients diagnosed with EC were included at Peking University People's Hospital between January 2006 and December 2020. These patients were randomly divided into training and validation cohorts at a 2:1 ratio, collecting data on clinicopathological information and IHC indicators. A total of 92 combinations of algorithms were assessed using the Leave-One-Out Cross-Validation framework to identify the one with the highest C-index. To estimate the accuracy of the factors and four IHC indicators for predicting both OS and RFS, survival curves and receiver operating characteristic (ROC) curves were used. Independent predictors included estrogen receptor, progesterone receptor, body mass index, P53, FIGO stage, histology, grade, Ki67, ascites and lymph node metastasis. Both the training and validation cohorts exhibited excellent predictive performance for OS and RFS, as demonstrated by ROC curves at 1-year, 3-year and 5-year follow-ups. By introducing a model based solely on clinicopathological information as model 1 and adding four IHC indicators in model 2, a significant improvement was observed in the area under the curve (AUC) values across the entire sample. The AUC value for OS curves increased from 0.765 to 0.872, and the AUC for RFS curves rose from 0.791 to 0.882. Thus, the present study's model effectively predicts patients' probability of OS and RFS using these factors. This prediction capability can guide postoperative treatment plans and follow-up intervals, potentially enhancing long-term survival for patients with EC.

Validating the 2023 FIGO staging system: A nomogram for endometrioid endometrial cancer and adenocarcinoma

BACKGROUND

To find the factors impacting overall survival (OS) prognosis in patients with endometrioid endometrial carcinoma (EEC) and adenocarcinoma and to establish a nomogram model to validate the 2023 International Federation of Obstetrics and Gynecology (FIGO) staging system for endometrial cancer.

METHODS

Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) training cohort. An independent validation cohort was obtained from the First Affiliated Hospital of Anhui Medical University between 2008 and 2023. Cox regression analysis identified independent prognostic factors for OS in EEC and adenocarcinoma patients. A nomogram predicting OS was developed and validated utilizing the C-index, calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The relationship between the tumor grade and prognosis of EEC and adenocarcinoma was quantified using net reclassification improvement (NRI), propensity score matching (PSM), and Kaplan-Meier curves.

RESULTS

Cox regression analysis identified age, race, marital status, tumor grade, tumor stage, tumor size, and chemotherapy as independent prognostic factors for OS. A nomogram for predicting OS was developed based on these factors. The C-indexes for the OS nomogram was 0.743 and 0.720 for the SEER training set and external validation set, respectively. The area under the ROC (AUC) for the OS nomogram was 0.755, 0.757, and 0.741 for the SEER data subsets and 0.844, 0.719, and 0.743 for the external validation subsets. Calibration plots showed high concordance between the nomogram-predicted and observed OS. DCA also demonstrated the clinical utility of the OS nomogram. NRI, PSM, and survival analyses revealed that tumor grade was the most important histopathological factor for EEC and adenocarcinoma prognosis.

CONCLUSION

Seven independent prognostic variables for the OS of patients with EEC and adenocarcinoma were identified. The established OS nomogram has good predictive ability and clinical utility and validates the 2023 endometrial cancer FIGO staging system.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in advanced endometrial carcinoma after surgery: a retrospective analysis of the SEER Database

OBJECTIVE

We aimed to construct and validate a prognostic nomogram to predict cancer-specific survival (CSS) after surgery in patients with advanced endometrial carcinoma (EC).

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

The Surveillance, Epidemiology, and End Results (SEER) Database contains cancer incidence and survival data from population-based cancer registries in the USA. A total of 5445 patients from the SEER Database diagnosed with advanced EC between 2004 and 2015 were included and randomised 7:3 into a training cohort (n=3812) and a validation cohort (n=1633).

OUTCOME MEASURE

CSS.

RESULTS

The nomograms for CSS included 10 variables (positive regional nodes, age, tumour size, International Federation of Gynecology and Obstetrics (FIGO) stage, grade, ethnicity, income, radiation, chemotherapy and historical stage) based on the forward stepwise regression results. They revealed discrimination and calibration using the concordance index (C-index) and area under the time-dependent receiver operating characteristic curve, with a C-index value of 0.7324 (95% CI=0.7181 to 0.7468) and 0.7511 (95% CI=0.7301 to 0.7722) for the training and validation cohorts, respectively. Using calibration plots, a high degree of conformance was shown between the predicted and observed results. Additionally, a comparison of the nomogram and FIGO staging based on changes in the C-index, net reclassification index and integrated discrimination improvement demonstrated that the nomogram had better accuracy and efficacy.

CONCLUSIONS

We successfully constructed an accurate and effective nomogram to predict CSS in patients with advanced EC, which may help clinicians determine optimal individualised treatment strategies for patients with advanced EC. The predictive performance of the nomogram was evaluated thoroughly, but only internally. Therefore, further validation using different data sources is warranted in future related studies.

A population-based risk scoring system to individualize adjuvant treatment for stage IIIC endometrial cancer patients after surgery

BACKGROUND

To develop a risk scoring system to tailor the adjuvant treatment for stage IIIC EC patients after surgery.

METHODS

Data source was from the Surveillance, Epidemiology, and End Results (SEER) registry, where 3251 post-operative stage IIIC EC patients with different adjuvant treatment were included. Cox regression analysis was used to identify risk factors. The exp (β) of each independent risk factors generating from the cox analysis was used to construct the risk scoring system, which was further utilized to divide the patients into different risk subgroups and the efficacy of different adjuvant modalities in each risk subgroups would be compared accordingly.

RESULTS

Six independent risk factors were identified to develop the scoring system, which further divided the patients into three risk subgroups based on the total risk score (Low-risk≤8.46, 8.47 ≤ Middle-risk≤9.94, High-risk≥9.95). This study revealed that CRT was not superior to RT alone (HR:1.208, 95%CI: 0.852-1.741; P = 0.289) or CT alone (HR:1.260, 95%CI: 0.750-2.116; P = 0.382) in Low-risk subgroup. We also observed that CRT had a survival advantage over other treatment modalities in the Middle-risk subgroup (All P < 0.001), but CRT and CT alone to be superimposable in the High-risk subgroup (HR: 1.395, 95%CI: 0.878-2.216; P = 0.159).

CONCLUSION

A risk scoring system has been developed to tailor the adjuvant treatment for stage IIIC EC patients after surgery, where RT or CT alone could be a substitute for CRT in Low-risk patients and CT alone was a potential alternative for High-risk patients while CRT remained to be the optimal choice for the Middle-risk patients.

Prognostic model for the exemption of adjuvant chemotherapy in stage IIIC endometrial cancer patients

BACKGROUND

This study aimed to develop a nomogram to predict the survival for stage IIIC endometrial cancer (EC) patients with adjuvant radiotherapy (ART) alone and personalize recommendations for the following adjuvant chemotherapy (ACT).

METHODS

In total, 746 stage IIIC EC patients with ART alone were selected from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox regression analysis was performed to identify independent risk factors. A nomogram was developed accordingly, and the area under the receiver operating characteristic curve (AUC) and C-index were implemented to assess the predictive power. The patients were divided into different risk strata based on the total points derived from the nomogram, and survival probability was compared between each risk stratus and another SEER-based cohort of stage IIIC EC patients receiving ART+ACT (cohort ART+ACT).

RESULTS

Five independent predictors were included in the model, which had favorable discriminative power both in the training (C-index: 0.732; 95% CI: 0.704-0.760) and validation cohorts (C-index: 0.731; 95% CI: 0.709-0.753). The patients were divided into three risk strata (low risk <135, 135 ≤ middle risk ≤205, and high risk >205), where low-risk patients had survival advantages over patients from cohort ART+ACT (HR: 0.45, 95% CI: 0.33-0.61, P < 0.001). However, the middle- and high-risk patients were inferior to patients from cohort ART+ACT in survival (P < 0.001).

CONCLUSION

A nomogram was developed to exclusively predict the survival for stage IIIC EC patients with ART alone, based on which the low-risk patients might be perfect candidates to omit the following ACT. However, the middle- and high-risk patients would benefit from the following ACT.