A Nomogram-Based Risk Classification System Predicting the Overall Survival of Patients With Newly Diagnosed Stage IVB Cervix Uteri Carcinoma

A Bayesian network predicting survival of cervical cancer patients-Based on surveillance, epidemiology, and end results

This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer patients who received standard treatments and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log-rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BNs) were built to predict 3- and 5-year survival. We also grouped the patients into new stages by clustering their 5-year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43,749 and 39,333 cases were finally eligible for the 3- and 5-year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan-Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3-year survival, and 0.851 and 0.787 for 5-year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.

Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma

Objective

In individuals with stage IB1-IIA2 cervical cancer (CC) who received postoperative radiotherapy ± chemotherapy (PORT/CRT), the interaction between sarcopenia and malnutrition remains elusive, let alone employing a nomogram model based on radiomic features of psoas extracted at the level of the third lumbar vertebra (L3). This study was set to develop a radiomics-based nomogram model to predict malnutrition as per the Patient-Generated Subjective Global Assessment (PG-SGA) for individuals with CC.

Methods

In total, 120 individuals with CC underwent computed tomography (CT) scans before PORT/CRT. The radiomic features of psoas at L3 were obtained from non-enhanced CT images. Identification of the optimal features and construction of the rad-score formula were conducted utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression to predict malnutrition in the training dataset (radiomic model). Identification of the major clinical factors in the clinical model was performed by means of binary logistic regression analysis. The radiomics-based nomogram was further developed by integrating radiomic signatures and clinical risk factors (combined model). The receiver operating characteristic (ROC) curves and decision curves analysis (DCA) were employed for the evaluation and comparison of the three models in terms of their predictive performance.

Results

Twelve radiomic features in total were chosen, and the rad-score was determined with the help of the non-zero coefficient from LASSO regression. Multivariate analysis revealed that besides rad-score, age and Eastern Cooperative Oncology Group performance status could independently predict malnutrition. As per the data of this analysis, a nomogram prediction model was constructed. The area under the ROC curves (AUC) values of the radiomic and clinical models were 0.778 and 0.847 for the training and 0.776 and 0.776 for the validation sets, respectively. An increase in the AUC was observed up to 0.972 and 0.805 in the training and validation sets, respectively, in the combined model. DCA also confirmed the clinical benefit of the combined model.

Conclusion

This radiomics-based nomogram model depicted potential for use as a marker for predicting malnutrition in stage IB1-IIA2 CC patients who underwent PORT/CRT and required further investigation with a large sample size.

Dynamic Nomogram Based on the Metastatic Number and Sites and Therapy Strategies Predicting the Prognosis of Patients with Metastatic Cervical Cancer

Background

Individual survival prediction is of vital importance to optimize the individualized treatment of metastatic cervical cancer (mCC) patients. The goal of this study was to identify the potential risk factors for the survival of mCC patients and construct a nomogram for their prognosis.

Methods

Medical records of patients with newly diagnosed mCC at the First Affiliated Hospital of Xi'an Jiaotong University were reviewed retrospectively. Risk factors were identified using Cox proportional hazards analysis and Kaplan-Meier curves. Random forest was used to identify factors associated with therapy strategy. Nomogram and dynamic nomogram were established using 'rms' and "DynNom" R package.

Results

A total of 98 patients with mCC were finally identified. In Cox analyses, multiple metastases and concurrent chemoradiotherapy (CCRT) were identified as independent predictors for overall survival (OS). We further explored the prognostic value of metastatic number and sites and therapy strategies for mCC patients by Kaplan-Meier curves. A dynamic nomogram including metastases number and sites (multiple metastases, liver and lymph node (LN) above diaphragm metastases) and chemoradiotherapy strategies (CCRT, postradiotherapy chemotherapy, and radiotherapy to metastatic sites) was constructed for predicting the prognosis of mCC patients. For newly diagnosed patients, we strongly recommended the combination of chemotherapy and definitive pelvic radiotherapy and, if possible, radiation to metastatic site, but CCRT should be implemented with caution. We constructed a dynamic nomogram indicating that patients with younger age, shorter symptom duration, and better laboratory test results are suitable for CCRT.

Conclusion

Survival analyses showed that the metastatic number and sites and therapy strategies are associated with the prognosis of mCC patients. The CCRT and prognostic nomograms may help clinicians to make better clinical decisions and effectively predict the prognosis for newly diagnosed mCC patients.

Treatment strategy and clinical characteristics of patients with cervical cancer as prognostic parameters of survival

Background. Cervical cancer (CRC) ranks second in the world among all malignant neoplasms of the female genital organs. According to the WHO, more than 500000 new cases of CRC are detected annually in the world. In 2021, 3398 new cases of the disease were registered in Ukraine. Currently a number of factors that have a rather high individual prognostic significance influences the CRC development. Among these factors are those which are associated with an adverse outcome: heavy hereditary anamnesis, growth form of tumor and its localization, variant of spread, depth of invasion into the stroma, morphological structure, methods of treatment, etc. Over the last decade, there has been an unfavorable trend towards an increase in the number of young patients who have an advanced tumor process, which significantly restricts methods and reduces the chances of the successful treatment. Thereby, the high level of morbidity and mortality from CRC, especially among young patients, as well as the unsatisfactory results of 5-year survival after traditional methods of treatment, indicate that the cancer of this type is the prior problem in domestic oncology. Purpose. To make scientific analysis of modern treatment strategies and characteristics of CRC as prognostic survival parameters. Materials and methods. The literature review included available full-text publications, which were obtained as a result of an in-depth analysis of foreign and domestic scientific publications. Results. Authors gave analysis of modern domestic and foreign literature on the evaluation of the effectiveness of treatment of cervical cancer patients; namely, how treatment strategies, individual characteristics of patients and characteristics of the tumor affect the results of treatment and prognosis of patient survival. It is shown which parameters, factors and important nuances of the disease should be taken into account, which is decisive in choosing treatment tactics and preventing complications. Conclusions. Knowing and taking into account the prognostic parameters of the survival of cervical cancer patients is important for the assessment of treatment standards, as well as the development of personalized tactics for the treatment and monitoring of this disease.

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

BACKGROUND

Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5-year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute.

METHODS

The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver-operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively.

RESULTS

A total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models (p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors.

CONCLUSIONS

The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision-making for nonmetastatic CC patients in the future.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Childhood with Clear Cell Sarcoma of the Kidney Based on the SEER Database

Objective. Clear cell sarcoma of the kidney (CCSK) is a lethal pediatric renal malignancy with poor prognosis. A prognostic nomogram needs to be established for overall survival (OS) prediction of patients with CCSK. Methods. Eligible 2588 CCSK patients (age 0–19) diagnosed between 2000 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomized into training and validation cohorts (7 : 3). Independent prognostic factors were identified by univariate and multifactorial Cox regression analyses and used to construct a nomogram. Receiver operating characteristics (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Moreover, a risk classification system was established based on the risk scores of the nomogram. Results. Cox analyses revealed that age, combined stage, and origin were most significant prognostic factors. Based on these prognostic factors, a nomogram was established for predicting 3- and 5-year OS of patients with CCSK. The area under the ROC curve (AUC) of 3- and 5-year OS was 0.733 and 0.728 in the training cohort, corresponding to 0.69 and 0.674 in the validation cohort. The C-index of calibration curves in the training and validation cohorts was 0.724 and 0.686. DCAs indicated the clinical utility of this nomogram. A risk classification system stratified CCSK patients into three different risk cohorts. The OS time of low-, intermediate-, and high-risk patients was 76, 68, and 65 months in the training cohort, corresponding to 69.5, 66, and 72 months in the validation cohort. Conclusion. A nomogram-based risk classification system has high accuracy for the prognostic prediction of CCSK.

Survival Comparison Between Squamous Cell Carcinoma and Adenocarcinoma for Radiotherapy-Treated Patients with Stage IIB-IVA Cervical Cancer

Objective

To compare the prognostic significance of adenocarcinoma (AC) with squamous cell carcinoma (SCC) on overall survival (OS) in patients with stage IIB-IVA cervical cancer (CC) treated by external beam radiotherapy (EBRT) and brachytherapy (BRT) with/without chemotherapy registered in the Surveillance, Epidemiology, and End Results database.

Methods

Data of eligible patients were extracted between 2004 and 2016. A univariate analysis was conducted using the cumulative incidence function (CIF) by considering competing events and compared using Gray’s test. The significant variables in univariate analysis were further evaluated in a multivariate analysis performed with the Fine-Gray regression model. Propensity score matching (PSM) analysis was also employed to reconfirm the results found in the present study.

Results

A total of 2,243 patients with SCC and 176 patients with AC were extracted from the database. The 5-year OS rates were 57.8% in the SCC group and 52.8% in the AC group. 149 patients died of causes other than CC—considered as competing events. Compared with the SCC group, patients diagnosed with AC had statistically significant worse 5-year OS rate before and after PSM. In the multivariate Fine-Gray regression model, the histological subtype of AC was proven as an independent prognostic factor associated with poorer OS before [hazard ratio (HR) = 1.340; 95% confidence interval (CI): 1.081-1.660; P = 0.007] and after [HR = 1.376; 95% CI: 1.107-1.711; P = 0.004] PSM.

Conclusions

The histological subtype of AC is significantly correlated with impaired OS as an independent prognostic variable in patients with stage IIB-IVA CC who received EBRT and BRT compared to patients with SCC. Future studies should incorporate effective and individualized treatment strategies into clinical decision-making to improve the unsatisfactory survival outcomes for patients with AC.

Development and Validation of Novel Nomograms to Predict the Overall Survival and Cancer-Specific Survival of Cervical Cancer Patients With Lymph Node Metastasis

Objective

The objective of this study was to establish and validate novel individualized nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in cervical cancer patients with lymph node metastasis.

Methods

A total of 2,956 cervical cancer patients diagnosed with lymph node metastasis (American Joint Committee on Cancer, AJCC N stage=N1) between 2000 and 2018 were included in this study. Univariate and multivariate Cox regression models were applied to identify independent prognostic predictors, and the nomograms were established to predict the OS and CSS. The concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were applied to estimate the precision and discriminability of the nomograms. Decision-curve analysis (DCA) was used to assess the clinical utility of the nomograms.

Results

Tumor size, log odds of positive lymph nodes (LODDS), radiotherapy, surgery, T stage, histology, and grade resulted as significant independent predictors both for OS and CSS. The C-index value of the prognostic nomogram for predicting OS was 0.788 (95% CI, 0.762–0.814) and 0.777 (95% CI, 0.758–0.796) in the training and validation cohorts, respectively. Meanwhile, the C-index value of the prognostic nomogram for predicting CSS was 0.792 (95% CI, 0.767–0.817) and 0.781 (95% CI, 0.764–0.798) in the training and validation cohorts, respectively. The calibration curves for the nomograms revealed gratifying consistency between predictions and actual observations for both 3- and 5-year OS and CSS. The 3- and 5-year area under the curves (AUCs) for the nomogram of OS and CSS ranged from 0.781 to 0.828. Finally, the DCA curves emerged as robust positive net benefits across a wide scale of threshold probabilities.

Conclusion

We have successfully constructed nomograms that could predict 3- and 5-year OS and CSS of cervical cancer patients with lymph node metastasis and may assist clinicians in decision-making and personalized treatment planning.

A prognostic model of patients with ovarian mucinous adenocarcinoma: a population-based analysis

BACKGROUND

Ovarian mucinous carcinoma is a disease that requires unique treatment. But for a long time, guidelines for ovarian serous carcinoma have been used for the treatment of ovarian mucinous carcinoma. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with ovarian mucinous adenocarcinoma.

METHODS

In this study, patients initially diagnosed with ovarian mucinous adenocarcinoma from 2004 to 2015 were screened from the Surveillance, Epidemiology, and End Results (SEER) database, and divided into the training group and the validation group at a ratio of 7:3. Independent risk factors for OS and CSS were determined by multivariate Cox regression analysis, and nomograms were constructed and validated.

RESULTS

In this study, 1309 patients with ovarian mucinous adenocarcinoma were finally screened and randomly divided into 917 cases in the training group and 392 cases in the validation group according to a 7:3 ratio. Multivariate Cox regression analysis showed that the independent risk factors of OS were age, race, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. Independent risk factors of CSS were age, race, marital, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. According to the above results, the nomograms of OS and CSS in ovarian mucinous adenocarcinoma were constructed. In the training group, the C-index of the OS nomogram was 0.845 (95% CI: 0.821-0.869) and the C-index of the CSS nomogram was 0.862 (95%CI: 0.838-0.886). In the validation group, the C-index of the OS nomogram was 0.843 (95% CI: 0.810-0.876) and the C-index of the CSS nomogram was 0.841 (95%CI: 0.806-0.876). The calibration curve showed the consistency between the predicted results and the actual results, indicating the high accuracy of the nomogram.

CONCLUSION

The nomogram provides 3-year and 5-year OS and CSS predictions for patients with ovarian mucinous adenocarcinoma, which helps clinicians predict the prognosis of patients and formulate appropriate treatment plans.