A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database

Stage IV ovarian cancer prognosis nomogram and analysis of racial differences: A study based on the SEER database

Purpose

Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer.

Methods

The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model.

Results

A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people.

Conclusion

This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.

Construction and validation of an innovative prognostic nomogram for overall survival in cervical cancer patients with lung metastasis: an analysis utilizing the SEER database

Purpose

To facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years.

Methods

We extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results

We gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts.

Conclusion

By utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.

Development and validation of prognostic nomograms for adult with papillary renal cell carcinoma: A retrospective study

OBJECTIVE

The aim of the study was to create two consensus nomograms for predicting Overall Survival (OS) and Cancer-Specific Survival (CSS) in adults with papillary Renal Cell Carcinoma (pRCC).

METHODS

Using the Surveillance, Epidemiology, and End Results databases, a retrospective analysis of 1,074 adults with pRCC from 2004 to 2015 was performed. These patients were then randomly divided into two independent cohorts with a ratio of 7:3 (training cohort: 752; validation cohort: 322). In a retrospective analysis of 752 patients from the training cohort, independent prognostic variables affecting OS and CSS were found. R software was used to create prognostic nomograms based on the findings of Cox regression analysis. The performance of the nomograms was assessed using the Concordance Index (C-index), the Area Under Curve (AUC), a calibration curve, and Decision Curve Analysis (DCA). Data from the 107 postoperative pRCC patients at the Affiliated Hospital of Xuzhou Medical University were used for external validation of the nomogram.

RESULTS

For OS and CSS, the C-indices and AUCs of the training cohort and the validation cohort indicated that the model had excellent discrimination. The DCA demonstrated that the model was clinically applicable, and the calibration curves in the internal and external validations showed that the model's accuracy was high.

CONCLUSION

The authors developed and validated a prognostic nomogram that accurately predicted the 3-, 5-, and 8-year OS and CSS of adults with pRCC. Clinicians can use this knowledge to direct the clinical management and counseling of patients with pRCC.

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 prognostic nomographs for patients with cervical cancer: SEER-based Asian population study

To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients.

Effect of marital status on the survival outcomes of cervical cancer: a retrospective cohort study based on SEER database

BACKGROUND

Cervical cancer is the fourth most common malignant tumor troubling women worldwide. Whether marital status affects the prognosis of cervical cancer is still unclear. Here, we investigate the prognostic value of marital status in patients with cervical cancer based on the seer database.

MATERIAL/METHODS

The demographic and clinical data of patients with cervical cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2017. Patients were divided into two groups (married and unmarried) according to marital status, and then the clinical characteristics of each group were compared using the chi-square test. Propensity score matching (PSM) was used to reduce differences in baseline characteristics. The overall survival (OS) and cervical cancer-specific survival (CCSS) were assessed by the Kaplan-Meier method, univariate and multivariate Cox regression models, and stratified analysis. Moreover, univariate and multivariate competing risk regression models were performed to calculate hazard ratios (HR) of death risk.

RESULTS

A total of 21,148 patients were included in this study, including 10,603 married patients and 10,545 unmarried patients. Married patients had better OS(P < 0.05) and CCSS (P < 0.05) compared to unmarried patients, and marital status was an independent prognostic factor for both OS (HR: 0.830, 95% CI: 0.798-0.862) and CCSS (HR: 0.892, 95% CI: 0.850-0.937). Moreover, after eliminating the competing risk, married patients (CCSD: HR:0.723, 95% CI: 0.683-0.765, P < 0.001) had a significantly decreased risk of death compared to unmarried patients. In stratified analysis, the married patients showed better OS and CCSS than the unmarried patients diagnosed in 1975-2000 and 2001-2017.

CONCLUSIONS

Being married was associated with a favorable prognosis of cervical cancer, and marital status was an independent prognostic factor for cervical cancer.

Analysis of prognostic factors for cervical mucinous adenocarcinoma and establishment and validation a nomogram: a SEER-based study

Up to now, there are no relevant studies on prognostic factors of cervical mucinous adenocarcinoma. Therefore, we explored the prognostic factors for cervical mucinous adenocarcinoma, and established and validated the prognostic model using the SEER database. We selected the independent factors through univariate and multivariate analyses. LASSO regression analysis was conducted to identify potential risk factors. In conjunction with LASSO and multivariate analysis, the nomogram incorporated three variables, including age, tumour size, and AJCC stage for OS. The c-index was 0.794 and 0.831 in development and validated cohorts, indicating that this prediction model showed adequate discriminative ability in the development cohort. Besides, calibration curves showed good concordance for the development cohort, as well as the validation cohort. We constructed a first-of-its-kind nomogram to predict cervical mucinous adenocarcinomas OS and it showed better performance than AJCC and FIGO stages. Patients with cervical mucinous adenocarcinoma might benefit from using this model to develop tailored treatments.IMPACT STATEMENTWhat is already known on this subject? Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.What do the results of this study add? We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.What are the implications of these findings for clinical practice and/or further research? Through the analysis of the SEER dataset, the prognostic factors affecting cervical mucinous adenocarcinoma were identified, and the first predictive model was created to predict the prognosis to help doctors develop individualised treatment plans and follow-up plans.

Optimising follow-up strategy based on cytology and human papillomavirus after fertility-sparing surgery for early stage cervical cancer: a nationwide, population-based, retrospective cohort study

BACKGROUND

The optimal follow-up strategy to detect recurrence after fertility-sparing surgery for early stage cervical cancer is unknown. Tailored surveillance based on individual risks could contribute to improved efficiency and, subsequently, reduce costs in health care. The aim of this study was to establish the predictive value of cervical cytology and high-risk human papillomavirus (HPV) testing to detect recurrent cervical intraepithelial neoplasia grade 2 or worse (CIN2+; including recurrent cervical cancer) after fertility-sparing surgery.

METHODS

In this nationwide, population-based, retrospective cohort study, we used data from the Netherlands Cancer Registry and the Dutch Nationwide Pathology Databank. All patients aged 18-40 years with cervical cancer of any histology who received fertility-sparing surgery (ie, large loop excision of the transformation zone, conisation, or trachelectomy) between Jan 1, 2000, and Dec 31, 2020, were included. Pathology data from diagnosis, treatment, and during follow-up were analysed. The primary and secondary outcomes were the cumulative incidence of recurrent CIN2+ and recurrence-free survival, overall and stratified by results for cytology and high-risk HPV.

FINDINGS

1548 patients were identified, of whom 1462 met the inclusion criteria. Of these included patients, 19 568 pathology reports were available. The median age at diagnosis was 31 years (IQR 30-35). After a median follow-up of 6·1 years (IQR 3·3-10·8), recurrent CIN2+ was diagnosed in 128 patients (cumulative incidence 15·0%, 95% CI 11·5-18·2), including 52 patients (cumulative incidence 5·4%, 95% CI 3·7-7·0) with recurrent cervical cancer. The overall 10-year recurrence-free survival for CIN2+ was 89·3% (95% CI 87·4-91·3). By cytology at first follow-up visit within 12 months after fertility-sparing surgery, 10-year recurrence-free survival for CIN2+ was 92·1% (90·2-94·1) in patients with normal cytology, 84·6% (77·4-92·3) in those with low-grade cytology, and 43·1% (26·4-70·2) in those with high-grade cytology. By high-risk HPV status at first follow-up visit within 12 months after surgery, 10-year recurrence-free survival for CIN2+ was 91·1% (85·3-97·3) in patients who were negative for high-risk HPV and 73·6% (58·4-92·8) in those who were positive for high-risk HPV. Cumulative incidence of recurrent CIN2+ within 6 months after any follow-up visit (6-24 months) in patients negative for high-risk HPV with normal or low-grade cytology was 0·0-0·7% and with high-grade cytology was 0·0-33·3%. Cumulative incidence of recurrence in patients positive for high-risk HPV with normal or low-grade cytology were 0·0-15·4% and with high-grade cytology were 50·0-100·0%. None of the patients who were negative for high-risk HPV without high-grade cytology, at 6 months and 12 months, developed recurrence.

INTERPRETATION

Patients who are negative for high-risk HPV with normal or low-grade cytology at 6-24 months after fertility-sparing surgery, could be offered a prolonged follow-up interval of 6 months. This group comprises 80% of all patients receiving fertility-sparing surgery. An interval of 12 months seems to be safe after two consecutive negative tests for high-risk HPV with an absence of high-grade cytology, which accounts for nearly 75% of all patients who receive fertility-sparing surgery.

FUNDING

KWF Dutch Cancer Society.

A preoperative nomogram predicting risk of lymph node metastasis for early-stage cervical cancer

OBJECTIVE

This study aimed to develop a preoperative nomogram based on clinical and pathological characteristics to provide a more individualized and accurate estimation of lymph node metastasis (LNM) in patients with early-stage cervical cancer.

METHODS

A total of 7,349 early-stage cervical cancer patients with pathologically confirmed between 1988 and 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n = 5,500) and validation (n = 1,849) cohorts randomly. A cohort of 455 patients from multicenter was used for the external validation. We established a multivariate logistic regression model based on preoperative clinicopathological data, from which a nomogram was developed and validated. A predicted probability of LNM < 5% was defined as low risk.

RESULTS

From multivariate logistic regression analysis, age at diagnosis, histologic subtype, tumor grade, tumor size and FIGO stage were identified as preoperative independent risk factors of LNM. The nomogram incorporating these factors demonstrated good discrimination and calibration (concordance index = 0.723; 95% confidence interval (CI), 0.707-0.738). In the validation cohort, the discrimination accuracy was 0.745 (95% CI, 0.720-0.770) and 0.747 (95% CI, 0.690-0.804), respectively. The nomogram was well calibrated with a high concordance probability. We also established an R-enabled Internet browser for LNM risk assessment, which tool may be convenient for physicians.

CONCLUSIONS

We developed an effective preoperative nomogram based on clinical and pathological characteristics to predict LNM for early-stage cervical cancer. This model could improve clinical trial design and help physicians to decide whether to perform lymphadenectomy or not.

Establishment and validation of prognostic nomograms integrating histopathological features in patients with endocervical adenocarcinoma

AIMS

To develop and verify pathological models using pathological features basing on HE images to predict survival invasive endocervical adenocarcinoma (ECA) postoperatively.

METHODS

There are 289 ECA patients were classified into training and validation cohort. A histological signature was produced in 191 patients and verified in the validation groups. Histological models combining the histological features were built, proving the incremental value of our model to the traditional staging system for individualised prognosis estimation.

RESULTS

Our model included five chosen histological characteristics and was significantly related to overall survival (OS). Our model had AUC of 0.862 and 0.955, 0.891 and 0.801 in prognosticating 3-year and 5 year OS in the training and validation cohort, respectively. In training cohorts, our model had better performance for evaluation of OS (C-index: 0.832; 95% CI 0.751 to 0.913) than International Federation of Gynecology and Obstetrics (FIGO) staging system (C-index: 0.648; 95% CI 0.542 to 0.753) and treatment (C-index: 0.687; 95% CI 0.605 to 0.769), with advanced efficiency of the classification of survival outcomes. Furthermore, in both cohorts, a risk stratification system was built that was able to precisely stratify stage I and II ECA patients into high-risk and low-risk subpopulation with significantly different prognosis.

CONCLUSIONS

A nomogram with five histological signatures had better performance in OS prediction compared with traditional staging systems in ECAs, which might enable a step forward to precision medicine.

Development and validation of nomograms to recurrence and survival in patients with early-stage cervical adenocarcinoma

PURPOSE

Cervical adenocarcinoma is one of the most common types of cervical cancer and its incidence is increasing. The biological behavior and treatment outcomes of cervical adenocarcinoma (CA) differ from those of squamous cell carcinoma (SCC). We sought to develop a model to predict recurrence and cancer-specific survival (CSS) deaths in CA patients.

METHODS

131 patients were included in model development and internal validation, and patients from the SEER database (N = 1679) were used for external validation. Multivariable Cox proportional hazards regression analysis was used to select predictors of relapse-free survival (RFS) and CSS and to construct the model, which was presented as two nomograms. Internal validation of the nomograms was performed using the bootstrap resampling method.

RESULTS

Age, FIGO (International Federation of Gynecology and Obstetrics) stage, size of the tumor, lymph metastasis and depth of invasion were identified as independent prognostic factors for RFS, while age, FIGO stage, size of the tumor and number of positive LNs were identified as independent prognostic factors for CSS. The nomogram of the recurrence model predicted 2- and 5-year RFS, with optimism adjusted c-statistic of 75.41% and 74.49%. Another nomogram predicted the 2- and 5-year CSS with an optimism-adjusted c-statistic of 83.22% and 83.31% after internal validation; and 68.6% and 71.33% after external validation.

CONCLUSIONS

We developed and validated two effective nomograms based on static nomograms or online calculators that can help clinicians quantify the risk of relapse and death for patients with early-stage CA.

Nomogram incorporating log odds of positive lymph nodes improves prognostic prediction for ovarian serous carcinoma: a real-world retrospective cohort study

OBJECTIVES

Ovarian serous carcinoma (OSC) is a major cause of gynaecological cancer death, yet there is a lack of reliable prognostic models. To address this, we developed and validated a nomogram based on conventional clinical characteristics and log odds of positive lymph nodes (LODDS) to predict the prognosis of OSC patients.

SETTING

A Real-World Retrospective Cohort Study from the Surveillance, Epidemiology and End Results programme.

PARTICIPANTS

We obtained data on 4192 patients diagnosed with OSC between 2010 and 2015. Eligibility criteria included specific diagnostic codes, OSC being the primary malignant tumour and age at diagnosis over 18 years. Exclusion criteria were missing information on various factors and unknown cause of death or survival time.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome were overall survival (OS) and ovarian cancer-specific survival (OCSS).

RESULTS

For OS and OCSS outcomes, we selected 7 and 5 variables, respectively, to establish the nomogram. In the training and validation cohorts, the C index for OS or OCSS was 0.716 or 0.718 and 0.731 or 0.733, respectively, with a 3-year time-dependent area under the curve (AUC) of 0.745 or 0.751 and a 5-year time-dependent AUC of 0.742 or 0.751. Calibration curves demonstrated excellent consistency between predicted and observed outcomes. The Net Reclassification Index, integrated discrimination improvement and decision curve analysis curves indicated that our nomogram performed better than the International Federation of Gynaecology and Obstetrics (FIGO) staging system in predicting OS and OCSS for OSC patients in both the training and validation cohorts.

CONCLUSION

Our nomogram, which includes LODDS, offers higher accuracy and reliability than the FIGO staging system and can predict overall and OCSS in OSC patients.

Determinants of acute and subacute case-fatality in elderly patients with hypertensive intracerebral hemorrhage

Background

Given that limited reports have described the survival and risk factors for elderly patients with hypertensive intracerebral hemorrhage (HICH), we aimed to develop a valid but simple prediction nomogram for the survival of HICH patients.

Methods

All elderly patients ≥65 years old who were diagnosed with HICH between January 2011 and December 2019 were identified. We performed the least absolute shrinkage and selection operator (Lasso) on the Cox regression model with the R package glmnet. A concordance index was performed to calculate the nomogram discrimination; and calibration curves and decision curves were graphically evaluated by depicting the observed rates against the probabilities predicted by the nomogram.

Results

A total of 204 eligible patients were analyzed, and over 20 % of the population was above the age of 80 (65-79 years old, n = 161; 80+ years old, n = 43). A hematoma volume ≥13.64 cm3 was associated with higher 7-day mortality (OR = 6.773, 95 % CI = 2.622-19.481; p < 0.001) and higher 90-day mortality (OR = 3.955, 95 % CI = 1.611-10.090, p = 0.003). A GCS score between 13 and 15 at admission was associated with a 7-day favorable outcome (OR = 0.025, 95 % CI = 0.005-0.086; p < 0.001) and a 90-day favorable outcome (OR = 0.033, 95 % CI = 0.010-0.099; p < 0.001).

Conclusions

Our nomogram models were visualized and accurate. Neurosurgeons could use them to assess the prognostic factors and provide advice to patients and their relatives.

A nomogram for predicting cancer-specific survival in patients with uterine clear cell carcinoma: a population-based study

Uterine clear cell carcinoma (UCCC) is a relatively rare endometrial cancer. There is limited information on its prognosis. This study aimed to develop a predictive model predicting the cancer-specific survival (CSS) of UCCC patients based on data from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018. A total of 2329 patients initially diagnosed with UCCC were included in this study. Patients were randomized into training and validation cohorts (7:3). Multivariate Cox regression analysis identified that age, tumor size, SEER stage, surgery, number of lymph nodes detected, lymph node metastasis, radiotherapy and chemotherapy were independent prognostic factors for CSS. Based on these factors, a nomogram for predicting the prognosis of UCCC patients was constructed. The nomogram was validated using concordance index (C-index), calibration curves, and decision curve analyses (DCA). The C-index of the nomograms in the training and validation sets are 0.778 and 0.765, respectively. Calibration curves showed good consistency of CSS between actual observations and nomogram predictions, and DCA showed that the nomogram has great clinical utility. In conclusion, a prognostic nomogram was firstly established for predicting the CSS of UCCC patients, which can help clinicians make personalized prognostic predictions and provide accurate treatment recommendations.

Systematic review and meta-analysis of prediction models used in cervical cancer

BACKGROUND

Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available.

DESIGN

We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately.

RESULTS

The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R²] >0.7) in endpoint prediction.

CONCLUSIONS

Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R² > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

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.

Establishment and validation of a prognostic nomogram for extrahepatic cholangiocarcinoma

Simple summary

Accurately estimate the prognosis of patients with ECCA is important. However, the TNM system has some limitations, such as low accuracy, exclusion of other factors (e.g., age and sex), and poor performance in predicting individual survival risk. In contrast, a nomogram-based clinical model related to a comprehensive analysis of all risk factors is intuitive and straightforward, facilitating the probabilistic analysis of tumor-related risk factors. Simultaneously, a nomogram can also effectively drive personalized medicine and facilitate clinicians for prognosis prediction. Therefore, we construct a novel practical nomogram and risk stratification system to predict CSS in patients with ECCA.

Background

Accurately estimate the prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) was important, but the existing staging system has limitations. The present study aimed to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in ECCA patients.

Methods

3415 patients diagnosed with ECCA between 2010 and 2015 were selected from the SEER database and randomized into a training cohort and a validation cohort at 7:3. The nomogram was identified and calibrated using the C-index, receiver operating characteristic curve (ROC), and calibration plots. Decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI) and the risk stratification were used to compare the nomogram with the AJCC staging system.

Results

Nine variables were selected to establish the nomogram. The C-index (training cohort:0.785; validation cohort:0.776) and time-dependent AUC (>0.7) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 2-, and 3-year CSS:0.27, 0.27,0.52; validation cohort:1-,2-,3-year CSS:0.48,0.13,0.34), IDI (training cohort: 1-, 2-, 3-year CSS:0.22,0.18,0.16; validation cohort: 1-,2-,3-year CSS:0.18,0.16,0.17), and DCA indicated that the established nomogram significantly outperformed the AJCC staging system (P<0.05) and had better recognition compared to the AJCC staging system.

Conclusions

We developed a practical prognostic nomogram to help clinicians assess the prognosis of patients with ECCA.

Clinicopathological characteristics and prognosis of colon cancer with lung metastasis without liver metastasis: A large population-based analysis

Distant metastasis explains the high mortality rate of colon cancer, in which lung metastasis without liver metastasis (LuM) is a rare subtype. This study is aimed to identify risk factors of LuM and LLM (lung metastasis with liver metastasis) from colon cancer, and to analyze the prognosis of patients with LuM by creating a nomogram. Patients' information were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was used to determine the risk factors for LuM and LLM. Prognostic factors for cancer-specific survival (CSS) and overall survival (OS) were identified by multivariate Cox proportional hazards regression and nomogram models were established to predict CSS and OS. Multivariate logistic regression analysis showed that blacks, splenic flexure of colon tumor, tumor size >5 cm, T4, N3, and higher lymph node positive rate were associated with the occurrence of LuM. Meanwhile, age >65 years old, female, splenic flexure of colon, higher lymph node positive rate, and brain metastasis were independent risk factors for CSS. The C-index of the prediction model for CSS was 0.719 (95% CI: 0.691-0.747). In addition, age, primary site, tumor size, differentiation grade, N stage, and bone metastasis were significantly different between LuM and LLM. The nomograms we created were effective in predicting the survival of individuals. Furthermore, patients with LuM and LLM from colon cancer might require different follow-up intervals and examinations.

Nomogram models for the prognosis of cervical cancer: A SEER-based study

Background

Cervical cancer (CC) is one of the most common cancers in women. This study aimed to investigate the clinical and non-clinical features that may affect the prognosis of patients with CC and to develop accurate prognostic models with respect to overall survival (OS) and cancer-specific survival (CSS).

Methods

We identified 11,148 patients with CC from the SEER (Surveillance, Epidemiology, and End Results) database from 2010 to 2016. Univariate and multivariate Cox regression models were used to identify potential predictors of patients’ survival outcomes (OS and CSS). We selected meaningful independent parameters and developed nomogram models for 1-, 3-, and 5-year OS and CSS via R tools. Model performance was evaluated by C-index and receiver operating characteristic curve. Furthermore, calibration curves were plotted to compare the predictions of nomograms with observed outcomes, and decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical effectiveness of the nomograms.

Results

All eligible patients (n=11148) were randomized at a 7:3 ratio into training (n=7803) and validation (n=3345) groups. Ten variables were identified as common independent predictors of OS and CSS: insurance status, grade, histology, chemotherapy, metastasis number, tumor size, regional nodes examined, International Federation of Obstetrics and Gynecology stage, lymph vascular space invasion (LVSI), and radiation. The C-index values for OS (0.831 and 0.824) and CSS (0.844 and 0.841) in the training cohorts and validation cohorts, respectively, indicated excellent discrimination performance of the nomograms. The internal and external calibration plots indicated excellent agreement between nomogram prediction and actual survival, and the DCA and CICs reflected favorable potential clinical effects.

Conclusions

We constructed nomograms that could predict 1-, 3-, and 5-year OS and CSS in patients with CC. These tools showed near-perfect accuracy and clinical utility; thus, they could lead to better patient counseling and personalized and tailored treatment to improve clinical prognosis.

Prognostic factors for squamous cervical carcinoma identified by competing-risks analysis: A study based on the SEER database

Cervical cancer has a high incidence of malignant tumors and a high mortality rate, with squamous cervical carcinoma (SCC) accounting for 80% of cases. A competing-risks model is recommended as being more feasible for evaluating the prognosis and guiding clinical practice in the future compared to Cox regression. Data originating from the Surveillance, epidemiology, and end results (SEER) database during 2004 to 2013 were analyzed. Univariate analysis with the cumulative incidence function was performed to assess the potential risk of each covariate. Significant covariates (P < .05) were extracted for inclusion in a Cox regression analysis and a competing-risks model that included a cause-specific (CS) hazard function model and a sub-distribution (SD) hazard function model. A total of 5591 SCC patients met the inclusion criteria. The three methods (Cox regression analysis, CS analysis, and SD analysis) showed that age, metastasis, American Joint Committee on Cancer stage, surgery, chemotherapy, radiation sequence with surgery, lymph node dissection, tumor size, and tumor grade were prognostic factors affecting survival in patients with SCC. In contrast, race and radiation status were prognostic factors affecting survival in the Cox regression and CS analysis, but the results were different in the SD analysis. Being separated, divorced, or widowed was an independent prognostic factor in the Cox regression analysis, but the results were different in the CS and SD analyses. A competing-risks model was used as a new statistical method to more accurately identify prognostic factors than conventional Cox regression analysis leading to bias in the results. This study found that the SD model may be better suited to estimate the clinical prognosis of a patient, and that the results of an SD model analysis were close to those of a CS analysis.

Prognostic Nomogram of Osteocarcinoma after Surgical Treatment

Purpose

This study aimed to establish a valid prognostic nomogram for osteocarcinoma after surgical management.

Methods

Based on the SEER database, we retrieved the clinical variables of patients confirmed to have osteocarcinoma between 1975 and 2016. Then, we performed univariate and multivariate analyses and constructed a nomogram of overall survival.

Results

Multivariate analysis of the primary cohort revealed that the independent factors for survival were age, grade, pathologic stage, T stage, and surgery performed. All these factors were showed by the nomogram. The correction curve of survival probability showed that the prediction results of nomogram well agreed with the actual observation results. The C index of the nomogram used to predict survival was 0.82; the AUC of 1-year, 3-year, and 5-year survival rates in the training cohort were 0.9, 0.819, and 0.80631, respectively, indicating that the model was accurate and reliable; whether the operation was performed or not; T stage; grade; and age were the main factors affecting the survival of patients. The AUC of the validation cohort for 1 year, 3 years, and 5 years were 0.8, 0.831, and 0.80023, respectively.

Conclusion

The proposed nomogram can more accurately predict the prognosis of patients with osteocarcinoma after surgical management. This could be a potential method that services clinical work.

Evaluation of log odds of positive lymph nodes in predicting the survival of patients with non-small cell lung cancer treated with neoadjuvant therapy and surgery: a SEER cohort-based study

Background

Log odds of positive lymph nodes (LODDS) is a novel lymph node (LN) descriptor that demonstrates promising prognostic value in many tumors. However, there is limited information regarding LODDS in patients with non-small cell lung cancer (NSCLC), especially those receiving neoadjuvant therapy followed by lung surgery.

Methods

A total of 2059 patients with NSCLC who received neoadjuvant therapy and surgery were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the LODDS cutoff value. Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curve analysis were performed to compare predictive values of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct a model for predicting prognosis.

Results

According to the survival analysis, LODDS had better differentiating ability than the N staging descriptor (log-rank test, P < 0.0001 vs. P = 0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than that of the N staging descriptor in the 1-, 3-, and 5-year survival analyses (all P < 0.05). Univariate and multivariate Cox regression analyses showed that LODDS was an independent risk factor for patients with NSCLC receiving neoadjuvant therapy followed by surgery both before and after IPTW (all P < 0.001). A clinicopathological model with LODDS, age, sex, T stage, and radiotherapy could better predict prognosis.

Conclusions

Compared with the AJCC N staging descriptor, LODDS exhibited better predictive ability for patients with NSCLC receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS demonstrated a sound performance in predicting prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09908-3.

A dynamic predictive nomogram of long-term survival in primary gastric lymphoma: a retrospective study

Background

Primary gastric lymphoma (PGL) is the most common extranodal non-Hodgkin lymphoma (NHL). Due to the rarity of the disease, it is important to create a predictive model that provides treatment and prognosis for patients with PGL and physicians.

Methods

A total of 8898 and 127 patients diagnosed with PGL were obtained from the SEER database and from our Cancer Center as training and validation cohorts, respectively. Univariate and multivariate Cox proportional hazards models were used to investigate independent risk factors for the construction of predictive survival nomograms, and a web nomogram was developed for the dynamic prediction of survival of patients with PGL. The concordance index (C-index), calibration plot, and receiver operating characteristics (ROC) curve were used to evaluate and validate the nomogram models.

Results

There were 8898 PGL patients in the SEER cohort, most of whom were married men over the age of 60, 16.1% of the primary tumors were localized in the antrum and pylori of the stomach, which was similar to the composition of 127 patients in the Chinese cohort, making both groups comparable. The Nomogram of overall survival (OS) was compiled based on eight variables, including age at diagnosis, sex, race, marital status, histology, stage, radiotherapy and chemotherapy. Cancer-specific survival (CSS) nomogram was developed with eight variables, including age at diagnosis, sex, marital status, primary tumor site, histology, stage, radiotherapy and chemotherapy. The C-index of OS prediction nomogram was 0.948 (95% CI: 0.901–0.995) in the validation cohort, the calibration plots showed an optimal match and a high area below the ROC curve (AUC) was observed in both training and validation sets. Also, we established the first web-based PGL survival rate calculator (https://yangjinru.shinyapps.io/DynNomapp/).

Conclusion

The web dynamic nomogram provided an insightful and applicable tool for evaluating PGL prognosis in OS and CSS, and can effectively guide individual treatment and monitoring.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02419-2.

A novel nomogram model to predict the overall survival of patients with retroperitoneal leiomyosarcoma: a large cohort retrospective study

Retroperitoneal leiomyosarcomas (RLS) are the second most common type of retroperitoneal sarcoma and one of the most aggressive tumours. The lack of early warning signs and delay in regular checkups lead to a poor prognosis. This study aims to create a nomogram to predict RLS patients' overall survival (OS). Patients diagnosed with RLS in the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were enrolled in this study. First, univariable and multivariable Cox regression analyses were used to identify independent prognostic factors, followed by constructing a nomogram to predict patients' OS at 1, 3, and 5 years. Secondly, the nomogram's distinguishability and prediction accuracy were assessed using receiver operating characteristic (ROC) and calibration curves. Finally, the decision curve analysis (DCA) investigated the nomogram's clinical utility. The study included 305 RLS patients, and they were divided into two groups at random: a training set (216) and a validation set (89). The training set's multivariable Cox regression analysis revealed that surgery, tumour size, tumour grade, and tumour stage were independent prognostic factors. ROC curves demonstrated that the nomogram had a high degree of distinguishability. In the training set, area under the curve (AUC) values for 1, 3, and 5 years were 0.800, 0.806, and 0.788, respectively, while in the validation set, AUC values for 1, 3, and 5 years were 0.738, 0.780, and 0.832, respectively. As evidenced by the calibration curve, the nomogram had high prediction accuracy. Moreover, DCA revealed that the nomogram had high clinical utility. Furthermore, the risk stratification system based on the nomogram could effectively categorise patients into three mortality risk subgroups. Therefore, the developed nomogram and risk stratification system may aid in optimising the treatment decisions of RLS patients to improve treatment prognosis and maximise their healthcare outcomes.

Nomogram Predicts Overall Survival in Patients With Stage IV Thyroid Cancer (TC): A Population-Based Analysis From the SEER Database

Background

Stage IV Thyroid cancer (TC) has a relatively poor prognosis and lacks a precise and efficient instrument to forecast prognosis. Our study aimed to construct a nomogram for predicting the prognosis of patients with stage IV TC based on data from the SEER programme.

Methods

We enrolled patients diagnosed with TC from 2004 to 2015 in the study. Furthermore, the median survival time (MST) for the patients equalled 25 months. The patients were split into two groups: the training group and validation group. We used descriptive statistics to calculate demographic and clinical variables, Student’s t test was used to describe continuous variables, and the chi-square test was used to describe classified variables. We used the concordance index (C-index) to evaluate discrimination ability and calibration plots to evaluate calibration ability. The improvement of the nomogram compared with the AJCC TNM system was evaluated by the net weight classification index (NRI), comprehensive discriminant rate improvement (IDI) and decision curve analysis (DCA).

Results

There were 3501 patients contained within our cohort, and the median follow-up was 25 months [quartile range (IQR): 6-60] in the whole population, 25 months (IQR: 6-60) in the training cohort, and 25 months (IQR: 5-59) in the validation cohort. The C-index value of the training cohort equalled 0.86 (95% CI: 0.85-0.87), and the value of the validation cohort equalled 0.85 (95% CI: 0.84-0.86). The NRI values were as follows: training queue: 1.16 for three-year and 1.12 for five-year OS prediction; authentication group: 1.22 for three-year and 1.21 for five-year OS prediction. The IDI values were as follows: training cohort: 0.25 for three-year and 0.21 for five-year OS prediction; validation cohort: 0.27 for three-year and 0.21 for five-year OS prediction. The DCA diagram showed that the nomogram was superior in predicting the three-year and five-year trends.

Conclusions

Our nomogram can be used to forecast the survival of patients with stage IV TC.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Purpose

Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC.

Patients and Methods

We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan–Meier analysis was used to compare the survival results among different risk subgroups.

Results

Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791–0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791–0.915), 0.886 (95% CI: 0.852–0.920) and 0.815 (95% CI: 0.766–0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group.

Conclusion

The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

Purpose

The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments.

Methods

Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients.

Results

Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study.

Conclusion

The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03491-8.

LRRFIP1, an epigenetically regulated gene, is a prognostic biomarker and predicts malignant phenotypes of glioma

Aims

Glioblastoma (GBM) is the most common malignant brain tumor with an adverse prognosis in the central nervous system. Traditional histopathological diagnosis accompanied by subjective deviations cannot accurately reflect tumor characteristics for clinical guidance. DNA methylation plays a critical role in GBM genesis. The focus of this project was to identify an effective methylation point for the classification of gliomas, the interactions between DNA methylation and potential epigenetic targeted therapies for clinical treatments.

Methods

Three online (TCGA, CGGA, and REMBRANDT) databases were employed in this study. T‐test, Venn analysis, univariate cox analysis, and Pearson's correlation analysis were adopted to screen significant prognostic methylation genes. Clinical samples were collected to determine the distributions of LRRFIP1 (Leucine Rich Repeat of Flightless‐1 Interacting Protein) protein by immunohistochemistry assay. Kaplan–Meier survival and Cox analysis were adopted to evaluate the prognostic value of LRRFIP1. Nomogram model was used to construct a prediction model. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway were performed to explore functions and related mechanisms of LRRFIP1 in gliomas.

Results

Our results showed that 16 genes were negatively connected with their methylation level and correlated with clinical prognosis of GBM patients. Among them, LRRFIP1 expression showed the highest correlation with its methylation level. LRRFIP1 was highly expressed in WHO IV, mesenchymal, and IDH wild‐type subtype. LRRFIP1 expression was an independent risk factor for OS (overall survival) in gliomas.

Conclusion

LRRFIP1 is an epigenetically regulated gene and a potential prognostic biomarker for glioma. Our research may be beneficial to evaluate clinical efficacy, assess the prognosis, and provide individualized treatment for gliomas.

A New Staging System Based on the Dynamic Prognostic Nomogram for Elderly Patients With Primary Gastrointestinal Diffuse Large B-Cell Lymphoma

Objective

The purpose of this study is to establish an accurate prognostic model based on important clinical parameters to predict the overall survival (OS) of elderly patients with primary gastrointestinal diffuse large B-cell lymphoma (EGI DLBCL).

Methods

The Cox regression analysis is based on data from the Surveillance, Epidemiology, and End Results (SEER) database.

Results

A total of 1,783 EGI DLBCL cases were eligible for the study [median (interquartile range, IQR) age, 75 (68–82) years; 974 (54.63%) males], of which 1,248 were randomly assigned to the development cohort, while 535 were into the validation cohort. A more accurate and convenient dynamic prognostic nomogram based on age, stage, radiation, and chemotherapy was developed and validated, of which the predictive performance was superior to that of the Ann Arbor staging system [C-index:0.69 (95% CI:0.67–0.71) vs. 56 (95%CI:0.54–0.58); P < 0.001]. The 3- and 5-year AUC values of ROC curves for 3-year OS and 5-year OS in the development cohort and the validation cohort were were alll above 0.7.

Conclusion

We establish and validate a more accurate and convenient dynamic prognostic nomogram for patients with EGI DLBCL, which can provide evidence for individual treatment and follow-up.

HOXC6 impacts epithelial-mesenchymal transition and the immune microenvironment through gene transcription in gliomas

BACKGROUND

Gliomas are the most common primary malignant tumours of the central nervous system (CNS). To improve the prognosis of glioma, it is necessary to identify molecular markers that may be useful for glioma therapy. HOXC6, an important transcription factor, is involved in multiple cancers. However, the role of HOXC6 in gliomas is not clear.

METHODS

Bioinformatic and IHC analyses of collected samples (n = 299) were performed to detect HOXC6 expression and the correlation between HOXC6 expression and clinicopathological features of gliomas. We collected clinical information from 177 to 299 patient samples and estimated the prognostic value of HOXC6. Moreover, cell proliferation assays were performed. We performed Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA) based on ChIP-seq and public datasets to explore the biological characteristics of HOXC6 in gliomas. RNA-seq was conducted to verify the relationship between HOXC6 expression levels and epithelial-mesenchymal transition (EMT) biomarkers. Furthermore, the tumour purity, stromal and immune scores were evaluated. The relationship between HOXC6 expression and infiltrating immune cell populations and immune checkpoint proteins was also researched.

RESULTS

HOXC6 was overexpressed and related to the clinicopathological features of gliomas. In addition, knockdown of HOXC6 inhibited the proliferation of glioma cells. Furthermore, increased HOXC6 expression was associated with clinical progression. The biological role of HOXC6 in gliomas was primarily associated with EMT and the immune microenvironment in gliomas. High HOXC6 expression was related to high infiltration by immune cells, a low tumour purity score, a high stromal score, a high immune score and the expression of a variety of immune checkpoint genes, including PD-L1, B7-H3 and CLTA-4.

CONCLUSIONS

These results indicated that HOXC6 might be a key factor in promoting tumorigenesis and glioma progression by regulating the EMT signalling pathway and might represent a novel immune therapeutic target in gliomas.

Treatment Strategies and Prognostic Factors of 2018 FIGO Stage IIIC Cervical Cancer: A Review

Cervical cancer is the fourth most common malignant tumor globally in terms of morbidity and mortality. The presence of lymph node metastasis (LNM) is an independent prognostic factor for progression-free survival (PFS) and overall survival (OS) in cervical cancer patients. The International Federation of Gynecology and Obstetrics (FIGO) staging system was revised in 2018. An important revision designates patients with regional LNM as stage IIIC, pelvic LNM only as stage IIIC1, and para-aortic LNM as stage IIIC2. However, the current staging system is only based on the anatomical location of metastatic lymph nodes (LNs). It does not consider other LN status parameters, which may limit its prognostic significance to a certain extent and needs further exploration and confirmation in the future. The purpose of this review is to summarize the choice of treatment for stage IIIC cervical cancer and the effect of different LN status parameters on prognosis.

Construction and validation of nomograms for predicting the prognosis of grade 3 endometrial endometrioid adenocarcinoma cancers: a SEER-based study

Most cases of endometrial adenocarcinoma (EAC) are diagnosed early and have a good prognosis; however, grade 3 (G3) EACs have poor outcomes. We retrospectively analyzed the data of 11,519 patients with G3 EACs registered between 2004 and 2015 in the Surveillance, Epidemiology, and End Results Program database and constructed a nomogram to guide clinicians in decision-making and accurate prediction of the prognosis. The caret package was used to divide samples into a training set and a validation set. Univariate and multivariate Cox regression analyses were performed, and a nomogram was constructed. A calibration curve was plotted, and a decision curve analysis was performed to verify the accuracy and clinical utility in both cohorts. The Cox regression analysis revealed that age, race, tumor size, number of lymph nodes resected, International Federation of Gynecology and Obstetrics stage, tumor/node stage, and adjuvant therapy were the prognostic factors for G3 EAC, and these were included in the nomogram. The area under the curve values of the training cohort for 1-, 3-, and 5-year were 0.832, 0.798, and 0.784, respectively for the overall survival (OS) group, and 0.858, 0.812, and 0.799, respectively for the cancer specific survival (CSS) group. A nomogram was constructed to predict the survival rate of patients with G3 EACs more accurately. The predictive nomogram will help clinicians manage patients with G3 EACs more effectively in terms of clinical prognosis.

Nomogram Based on Log Odds of Positive Lymph Nodes Predicting Cancer-Specific Survival in Patients With T3 and T4 Gallbladder Cancer After Radical Resection

Background: The aim of this study based on log odds of positive lymph nodes (LODDS) is to develop and validate an effective prognostic nomogram for patients with T3 and T4 gallbladder cancer (GBC) after resection.

Patients and Methods: A total of 728 T3 and T4 gallbladder cancer patients after resection from the Surveillance, Epidemiology, and End Results (SEER) database, randomly divided into training cohort and validation cohort according to 7:3. Another 128 patients from The Second Affiliated Hospital of Nanchang University for external validation. The nomograms were built by the Cox regression model and the Fine and Grey's model. Concordance index (C-index), calibration curve and the area under receiver operating characteristic (ROC) curve (AUC) were used to evaluate the nomogram and internal verification. The decision curve analysis (DCA) was used to measure clinical applicability.

Result: LODDS was independent prognostic predictor for overall survival (OS) and cancer-specific survival (CSS), and established the nomograms on this basis. The nomogram we have established has a good evaluation effect, with a C-index of 0.719 (95%CI, 0.707–0.731) for OS and 0.747 (95%CI, 0.733–0.760) for CSS. The calibration curves of OS and CSS both showed good calibration capability, and the AUC for predicting 1-, 2-, and 3-year 0.858, 0.848 were and 0.811 for OS, and 0.794, 0.793, and 0.750 for CSS. The DCA of nomograms both showed good clinical applicability.

Conclusion: The nomogram can provide effective OS and CSS prediction for patients with advanced gallbladder cancer after surgery.

Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer

To establish a prediction model based on clinical and pathological information for the long-term survival of patients with cervical cancer, we retrospectively analyzed the clinical data of patients pathologically diagnosed with stage IB-IIA cervical cancer between July 2007 and September 2017 in the Chinese Academy of Medical Sciences Cancer Hospital. Factors affecting the overall survival of the patients were analyzed using a Cox model, and a cervical cancer patient prediction nomogram model was established. A total of 2,319 patients were included in the study. According to the multivariate Cox regression analysis, number of complications, surgical methods, neoadjuvant treatment, lymph node metastasis, postoperative treatment, lymphovascular space invasion (LVSI), and other independent factors affecting prognosis were included to establish a nomogram. The nomogram consistency index in the training and validation cohorts was 0.691 and 0.615, respectively. The study established a highly accurate predictive model for the postoperative survival of cervical cancer patients.

The annual recurrence risk model for tailored surveillance strategy in patients with cervical cancer

PURPOSE

Current guidelines for surveillance strategy in cervical cancer are rigid, recommending the same strategy for all survivors. The aim of this study was to develop a robust model allowing for individualised surveillance based on a patient's risk profile.

METHODS

Data of 4343 early-stage patients with cervical cancer treated between 2007 and 2016 were obtained from the international SCCAN (Surveillance in Cervical Cancer) consortium. The Cox proportional hazards model predicting disease-free survival (DFS) was developed and internally validated. The risk score, derived from regression coefficients of the model, stratified the cohort into significantly distinctive risk groups. On its basis, the annual recurrence risk model (ARRM) was calculated.

RESULTS

Five variables were included in the prognostic model: maximal pathologic tumour diameter; tumour histotype; grade; number of positive pelvic lymph nodes; and lymphovascular space invasion. Five risk groups significantly differing in prognosis were identified with a five-year DFS of 97.5%, 94.7%, 85.2% and 63.3% in increasing risk groups, whereas a two-year DFS in the highest risk group equalled 15.4%. Based on the ARRM, the annual recurrence risk in the lowest risk group was below 1% since the beginning of follow-up and declined below 1% at years three, four and >5 in the medium-risk groups. In the whole cohort, 26% of recurrences appeared at the first year of the follow-up, 48% by year two and 78% by year five.

CONCLUSION

The ARRM represents a potent tool for tailoring the surveillance strategy in early-stage patients with cervical cancer based on the patient's risk status and respective annual recurrence risk. It can easily be used in routine clinical settings internationally.

Clinicopathological Features and Survival of Adolescent and Young Adults with Cervical Cancer

PURPOSE

To explore clinicopathological characteristics and their prognostic value among young patients with cervical cancer (who are aged ≤25 years old).

METHODS

The Surveillance, Epidemiology, and End Results Program (SEER) database was used to extract data on cervical cancer patients. They were then stratified by age as young women (≤25 years old) and old women (26-35 years old) and analyzed for clinicopathology characteristics and treatment modalities. Prognosis was analyzed using Kaplan-Meier survival curve, as well as hazard ratios using Cox regression modeling. The nomogram was developed based on Cox hazards regression model.

RESULTS

Compared to 26-35 years old women, patients aged ≤25 years tended to be white ethnicity, unmarried, had earlier stage of disease. There was also a better prognosis among younger cohort. Grade, FIGO stage, histologic subtypes, and surgical modalities influenced the survival outcomes of young patients. Among young cohorts, surgery prolonged the survival time of IA-IIA stage patients while surgical and non-surgical management presented no statistically prognostic difference among patients at IIB-IVB stage. Besides, the nomogram which constructed according to Cox hazards regression model which contained independent prognosis factors including FIGO stage, surgery type, and histologic type of tumor can robustly predict survival of young patients.

CONCLUSION

Cervical cancer patients ≤25 years old were uncommon and lived longer than the older patients. Among these young patients at IA-IIA stage, surgical treatment could be more effective at preventing death than non-surgery. The nomogram could perfectly predict the prognosis of young adults and adolescents with cervical cancer.

Development and validation of a SEER-based prognostic nomogram for cervical cancer patients below the age of 45 years

In this study, we established a nomogram for the prognostic prediction of patients with early-onset cervical cancer (EOCC) for both overall survival (OS) and cancer-specific survival (CSS). The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 10,079 patients diagnosed with EOCC between 2004 and 2015; these cases were then randomly divided into training and validation sets. The independent prognostic factors were identified in a retrospective study of 7,055 patients from the training set. A prognostic nomogram was developed using R software according to the results of multivariable Cox regression analysis. Furthermore, the model was externally validated using the data from the remaining 3,024 patients diagnosed at different times and enrolled in the SEER database. For the training set, the C-indexes for OS and CSS prediction were determined to be 0.831 (95 % confidence interval [CI]: 0.815–0.847) and 0.855 (95 % CI: 0.839–0.871), respectively. Receiver operating characteristic (ROC) analysis has revealed that the nomograms were a superior predictor compared with TNM stage and SEER stage. The areas under the curve (AUC) of the nomogram for OS and CSS prediction in the ROC analysis were 0.855 (95 % CI: 0.847–0.864) and 0.782 (95 % CI: 0.760–0.804), respectively. In addition, calibration curves indicated a perfect agreement between the nomogram-predicted and the actual 1-, 3-, and 5-year OS and CSS rates in the validation cohort. Thus, in this study, we established and validated a prognostic nomogram that provides an accurate prediction for 3-, 5-, and 10-year OS and CSS of EOCC patients. This will be useful for clinicians in guiding counseling and clinical trial design for cervical cancer patients.

Does adenocarcinoma have a worse prognosis than squamous cell carcinoma in patients with cervical cancer? A real-world study with a propensity score matching analysis

Objective

To compare survival outcomes between cervical adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using a propensity score matching (PSM) analysis based on the Surveillance, Epidemiology, and End Results (SEER) Program.

Methods

Patients diagnosed with cervical cancer between 1998 and 2016 were identified from the SEER database. The Kaplan-Meier method and Cox regression analysis were used to analyze survival. A subgroup analysis of overall survival (OS) between patients with ADC and SCC was performed after the 1:1 PSM analysis.

Results

Of the 33,148 patients, 24,591 (79.19%) had SCC and 8,557 (25.81%) had ADC. In the unmatched cohort, after adjustment in multivariate analysis, patients with ADC had a worse prognosis than patients with SCC (hazard ratio [HR]=1.12; 95% confidence interval [CI]=1.07–1.18; p<0.001). In the propensity matched cohort, Kaplan-Meier analysis and subgroup analysis showed that ADC was associated with a worse prognosis than SCC (p=0.001). An analysis stratified by SEER stage revealed a worse prognosis for patients with ADC patients presenting with a regional disease than patients with SCC (HR=1.24; 95% CI=1.14–1.36 p<0.001), but no statistically significant differences were observed between the localized disease (HR=0.97; 95% CI=0.86–1.10; p=0.664) and distant disease (HR=1.09; 95% CI=0.97–1.22; p=0.162) subgroups.

Conclusion

The significant differences in survival outcomes between patients with cervical ADC and SCC were only observed in the regional disease subgroup, but not in the localized disease and distant disease subgroups.

Development and validation of a prognostic nomogram for colorectal cancer after surgery

BACKGROUND

A nomogram is a diagram that aggregates various predictive factors through multivariate regression analysis, which can be used to predict patient outcomes intuitively. Lymph node (LN) metastasis and tumor deposit (TD) conditions are two critical factors that affect the prognosis of patients with colorectal cancer (CRC) after surgery. At present, few effective tools have been established to predict the overall survival (OS) of CRC patients after surgery.

AIM

To screen out suitable risk factors and to develop a nomogram that predicts the postoperative OS of CRC patients.

METHODS

Data from a total of 3139 patients diagnosed with CRC who underwent surgical removal of tumors and LN resection from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results program. The data were divided into a training set (n = 2092) and a validation set (n = 1047) at random. The Harrell concordance index (C-index), Akaike information criterion (AIC), and area under the curve (AUC) were used to assess the predictive performance of the N stage from the American Joint Committee Cancer tumor-node-metastasis classification, LN ratio (LNR), and log odds of positive lymph nodes (LODDS). Univariate and multivariate analyses were utilized to screen out the risk factors significantly correlating with OS. The construction of the nomogram was based on Cox regression analysis. The C-index, receiver operating characteristic (ROC) curve, and calibration curve were employed to evaluate the discrimination and prediction abilities of the model. The likelihood ratio test was used to compare the sensitivity and specificity of the final model to the model with the N stage alone to evaluate LN metastasis.

RESULTS

The predictive efficacy of the LODDS was better than that of the LNR based on the C-index, AIC values, and AUC values of the ROC curve. Seven independent predictive factors, namely, race, age at diagnosis, T stage, M stage, LODDS, TD condition, and serum carcinoembryonic antigen level, were included in the nomogram. The C-index of the nomogram for OS prediction was 0.8002 (95%CI: 0.7839-0.8165) in the training set and 0.7864 (95%CI: 0.7604-0.8124) in the validation set. The AUC values of the ROC curve predicting the 1-, 3-, and 5-year OS were 0.846, 0.841, and 0.825, respectively, in the training set and 0.823, 0.817, and 0.835, respectively, in the validation test. Great consistency between the predicted and actual observed OS for the 1-, 3-, and 5-year OS in the training set and validation set was shown in the calibration curves. The final nomogram showed a better sensitivity and specificity than the nomogram with N stage alone for evaluating LN metastasis in both the training set (-4668.0 vs -4688.3, P < 0.001) and the validation set (-1919.5 vs -1919.8, P < 0.001) through the likelihood ratio test.

CONCLUSION

The nomogram incorporating LODDS, TD, and other risk factors showed great predictive accuracy and better sensitivity and specificity and represents a potential tool for therapeutic decision-making.

The prognostic performance of the log odds of positive lymph nodes in patients with esophageal squamous cell carcinoma: A population study of the US SEER database and a Chinese single-institution cohort

Background

The purpose of this study was to assess the prognostic performance of the log odds of positive lymph nodes (LODDS) value compared with the pathological N stage and lymph node ratio (LNR) in patients with esophageal squamous cell carcinoma (ESCC).

Method

In total 1144 patients diagnosed with ESCC from the Surveillance, Epidemiology, and End Results (SEER) database and 930 patients from our validation cohort were eligible. Kaplan–Meier plotter and multivariate Cox proportional hazards models were conducted to investigate the prognostic value of the N stage, LNR stage, and LODDS stage. The homogeneity, discriminatory ability, and monotonicity of these variables were evaluated using the linear trend χ² test, likelihood ratio χ² test, Akaike information criterion (AIC), and consistency index (C‐index) to determine the potential superiorities.

Results

The prognostic LODDS cutoff values were determined to be −1.49 and −0.55 (p < 0.001). Univariate analyses showed significant association among the N, LNR, and LODDS stages and overall survival of the patients (all p < 0.001). Multivariate analyses confirmed that the LODDS stage remained an independent prognostic indicator in both the SEER database and our validation cohort. Subgroup analyses identified the ability of LODDS stage to distinguish heterogeneous patients within various groups in both independent databases. Furthermore, the model with the highest C‐index and smallest AIC value was the one incorporating the LODDS stage among the three investigated nodal classifications of both cohorts.

Conclusion

The novel LODDS stage demonstrated better prognostic performance than the traditional N or LNR stages in ESCC patients. It can serve as an auxiliary factor to improve prognostic performance and can be applied to evaluate the lymph node status to increase the precision of staging and evaluation of survival.

DDX60 Is Associated With Glioma Malignancy and Serves as a Potential Immunotherapy Biomarker

DDX60, an interferon (IFN)-inducible gene, plays a promotional role in many tumors. However, its function in glioma remains unknown. In this study, bioinformatic analysis (TCGA, CGGA, Rembrandt) illustrated the upregulation and prognostic value of DDX60 in gliomas. Immunohistochemical staining of clinical samples (n = 49) validated the DDX60 expression is higher in gliomas than in normal tissue (n = 20, P < 0.0001). It also could be included in nomogram as a parameter to predict the 3- and 5-year survival risk (C-index = 0.86). The biological process of DDX60 in glioma was mainly enriched in the inflammatory and immune response by GSEA and GO analysis. DDX60 expression had a positive association with most inflammatory-related functions, such as hematopoietic cell kinase (HCK) (R = 0.31), interferon (R = 0.72), STAT1 (R = 54), and a negative correlation with IgG (R = −0.24). Furthermore, DDX60 expression tends to be positively related to multiple infiltrating immune cells, while negatively related to CD56 dim nature killer cell in glioma. Some important immune checkpoints, like CTLA-4, PD-L1, EGF, CD96, and CD226, were all positively related with DDX60 (all Pearson correlation R > 0.26). The expression and correlation between DDX60, EGF, and PD-L1 were confirmed by western blot in clinical samples (n = 14, P < 0.0001) and GBM cells. These results indicated that DDX60 might have important clinical significance in glioma and could serve as a potential immune therapeutic target.

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal

Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research. Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool. Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable. Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Database: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP)

Background/Aims

Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database.

Methods

Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated.

Results

Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively.

Conclusions

The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.

Nomograms predicting the overall survival and cancer-specific survival of patients with stage IIIC1 cervical cancer

BACKGROUND

To explore the factors that affect the prognosis of overall survival (OS) and cancer-specific survival (CSS) of patients with stage IIIC1 cervical cancer and establish nomogram models to predict this prognosis.

METHODS

Data from patients in the Surveil-lance, Epidemiology, and End Results (SEER) programme meeting the inclusion criteria were classified into a training group, and validation data were obtained from the First Affiliated Hospital of Anhui Medical University from 2010 to 2019. The incidence, Kaplan-Meier curves, OS and CSS of patients with stage IIIC1 cervical cancer in the training group were evaluated. Nomograms were established according to the results of univariate and multivariate Cox regression models. Harrell's C-index, calibration plots, receiver operating characteristic (ROC) curves and decision-curve analysis (DCA) were calculated to validate the prediction models.

RESULTS

The incidence of pelvic lymph node metastasis, a high-risk factor for the prognosis of cervical cancer, decreased slightly over time. Eight independent prognostic variables were identified for OS, including age, race, marriage status, histology, extension range, tumour size, radiotherapy and surgery, but only seven were identified for CSS, with marriage status excluded. Nomograms of OS and CSS were established based on the results. The C-indexes for the nomograms of OS and CSS were 0.687 and 0.692, respectively, using random sampling of SEER data sets and 0.701 and 0.735, respectively, using random sampling of external data sets. The AUCs for the nomogram of OS were 0.708 and 0.705 for the SEER data sets and 0.750 and 0.750 for the external data sets, respectively. In addition, AUCs of 0.707 and 0.709 were obtained for the nomogram of CSS when validated using SEER data sets, and 0.788 and 0.785 when validated using external data sets. Calibration plots for the nomograms were almost identical to the actual observations. The DCA also indicated the value of the two models.

CONCLUSIONS

Eight independent prognostic variables were identified for OS. The same factors predicted CSS, with the exception of the marriage status. Both OS and CSS nomograms had good predictive and clinical application value after validation. Notably, tumour size had the largest contribution to the OS and CSS nomograms.

Tumour-free distance: a novel prognostic marker in patients with early-stage cervical cancer treated by primary surgery

BACKGROUND

Models predicting recurrence risk (RR) of cervical cancer are used to tailor adjuvant treatment after radical surgery. The goal of our study was to compare available prognostic factors and to develop a prognostic model that would be easy to standardise and use in routine clinical practice.

METHODS

All consecutive patients with early-stage cervical cancer treated by primary surgery in a single referral centre (01/2007-12/2016) were eligible if assessed by standardised protocols for pre-operative imaging and pathology. Fifteen prognostic markers were evaluated in 379 patients, out of which 320 lymph node (LN)-negative.

RESULTS

The best predictive model for the whole cohort entailed a combination of tumour-free distance (TFD) ≤ 3.5 mm and LN positivity, which separated two subgroups with a substantially distinct RR 36% and 6.5%, respectively. In LN-negative patients, a combination of TFD ≤ 3.5 mm and adenosquamous tumour type separated a group of nine patients with RR 33% from the rest of the group with 6% RR.

CONCLUSIONS

A newly identified prognostic marker, TFD, surpassed all traditional tumour-related markers in the RR assessment. Predictive models combining TFD, which can be easily accessed on pre-operative imaging, with LN status or tumour type can be used in daily practice and can help to identify patients with the highest RR.

Clinical characteristics and overall survival nomogram of second primary malignancies after prostate cancer, a SEER population-based study

Prostate cancer (PCa) is the most prevalent cancer among males and the survival period of PCa has been significantly extended. However, the probability of suffering from second primary malignancies (SPMs) has also increased. Therefore, we downloaded SPM samples from the SEER database and then retrospectively analyzed the general characteristics of 34,891 PCa patients diagnosed between 2000 and 2016. After excluding cases with unknown clinical information, 2203 patients were used to construct and validate the overall survival (OS) nomogram of SPM patients after PCa. We found that approximately 3.69% of PCa patients were subsequently diagnosed with SPMs. In addition, the three most prevalent sites of SPM were respiratory and intrathoracic organs, skin, and hematopoietic system. The top three histological types of SPMs were squamous cell carcinoma, adenoma and adenocarcinoma, nevi and melanoma. Through univariate and multivariate Cox regression analysis, we found that the site of SPM, age, TNM stage, SPM surgery history, and PCa stage were associated with the OS of SPM. By virtue of these factors, we constructed a nomogram to predict the OS of SPM. The C-index in the training set and validation set were 0.824 (95CI, 0.806-0.842) and 0.862 (95CI, 0.840-0.884), respectively. Furthermore, we plotted the receiver operating characteristic curve (ROC) and the area under curve (AUC) which showed that our model performed well in assessing the 3-year (0.861 and 0.887) and 5-year (0.837 and 0.842) OS of SPMs in the training and validation set. In summary, we investigated the general characteristics of SPMs and constructed a nomogram to predict the prognosis of SPM following PCa.

Study of the predictors for radiation pneumonitis in patient with non-small cell lung cancer received radiotherapy after pneumonectomy

PURPOSE

To identify the valuable predictors of grade≥2 radiation pneumonitis (RP) in patient treated with radiotherapy after pneumonectomy for non-small cell lung cancer (NSCLC); and to construct a nomogram predicting the incidence of grade≥2 RP in such patients.

PATIENTS AND METHODS

We reviewed 82 patients with NSCLC received radiotherapy after pneumonectomy from 2008 to 2018. The endpoint was grade≥2 RP. Univariate and multivariate regression analysis were conducted to evaluate significant factors of grade≥2 RP. Receiver operating characteristic (ROC) curve was used to establish optimal cutoff values and the nomogram was built to make the predictive model visualized. Descriptive analysis was performed on 5 patients with grade 3 RP.

RESULTS

A total of 22(26.8%) patients developed grade 2 RP and 5(6.1%) patients were grade 3 RP. V5, V10, V20, V30, MLD, PTV, and PTV/TLV were associated with the occurrence of grade≥2 RP in univariate analysis, while none of the clinical factors was significant; V5(OR,1.213;95%CI,1.099-1.339; P<0.001) and V20(OR,1.435;95%CI,1.166-1.765; P=0.001) were the independent significant predictors by multivariate analysis and were included in the nomogram. The ROC analysis for the cutoff values for predicting grade≥2 RP were V5>23% (AUC=0.819, sensitivity:0.701, specificity:0.832) and V20>8% (AUC=0.812, sensitivity:0.683, specificity:0.811). Additionally, grade≥3 RP did not occur when V5<30%, V20<13% and MLD<751.2cGy, respectively.

CONCLUSIONS

Our study showed that V5 and V20 were independent predictors for grade≥2 RP in NSCLC patients receiving radiotherapy after pneumonectomy. Grade 3 RP did not occur whenV5<30%, V20<13% and MLD<751.2cGy, respectively. In addition, patient underwent right pneumonectomy may have a lower tolerance to radiation compared to left pneumonectomy.

Prognostic Model for Predicting Overall and Cancer-Specific Survival Among Patients With Cervical Squamous Cell Carcinoma: A SEER Based Study

BACKGROUND

Cervical squamous cell carcinoma (CSCC) is the most common histological subtype of cervical cancer. The purpose of this study was to assess prognostic factors and establish personalized risk assessment nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in CSCC patients.

METHODS

CSCC patients diagnosed between 1988 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox proportional hazard regression models were applied to select meaningful independent predictors and construct predictive nomogram models for OS and CSS. The concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve were used to determine the predictive accuracy and discriminability of the nomogram.

RESULTS

A total cohort (n=17962) was randomly divided into a training cohort (n=11974) and a validation cohort (n=5988). Age, race, histologic grade, clinical stage, tumor size, chemotherapy and historic stage were assessed as common independent predictors of OS and CSS. The C-index value of the nomograms for predicting OS and CSS was 0.771 (95% confidence interval 0.762-0.780) and 0.786 (95% confidence interval 0.777-0.795), respectively. Calibration curves of the nomograms indicated satisfactory consistency between nomogram prediction and actual survival for both 3-year and 5-year OS and CSS.

CONCLUSION

We constructed nomograms that could predict 3- and 5-year OS and CSS of CSCC patients. These nomograms showed good performance in prognostic prediction and can be used as an effective tool to evaluate the prognosis of CSCC patients, thus contributing to clinical decision making and individualized treatment planning.

Sarcomatoid Carcinoma in the Head and Neck: A Population-Based Analysis of Outcome and Survival

Objectives

To characterize sarcomatoid cell carcinoma (SaC) in head and neck, explore the value of radiotherapy (RT) and chemotherapy, and build a nomogram to predict the prognosis.

Study Design

Retrospective cohort study.

Methods

In total, 559 patients diagnosed with head and neck SaC from 2004 to 2015 were included from the Surveillance, Epidemiology, and End Results program. All the cases were divided into training (N = 313) and validation (N = 246) cohorts according to the year of diagnosis. The cases were analyzed on the age, site, sex, race, T stage, N stage, M stage, surgery, RT, and chemotherapy. Cancer‐specific survival (CSS) and overall survival (OS) were compared among disease‐related categories. The parameters significantly correlated with CSS were used to construct a nomogram.

Results

The multivariate analysis showed that age, T stage, N stage, and M stage were significantly correlated with CSS and OS. Overall, RT was correlated with improved CSS for Stage T3–4 and Stage N1–3. The subgroup analysis showed that RT was correlated with CSS in the Stage N1–3 patients after surgery while chemotherapy indicated an improved survival for Stage T3–4 and N1–3 patients without surgery. The prognostic nomogram was constructed and had a powerful discriminatory ability with the C‐index of CSS: 0.711.

Conclusion

Late‐stage head and neck SaC patients unfit for surgery need comprehensive treatment based on chemotherapy, and patients with node metastasis require adjuvant RT after surgery. Generally, RT might improve the survival of late‐stage patients. A reliable and powerful nomogram was established that can provide an individual prediction of CSS for head and neck SaC.

Level of Evidence

3 Laryngoscope, 131:E489–E499, 2021

A validated survival nomogram for early-onset diffuse gastric cancer

This study aimed to establish and independently validate a prognostic nomogram for individual risk prediction in patients with early-onset diffuse gastric cancer (EODGC). Data for 794 patients with EODGC from the SEER database were randomly assigned to training (N=558) and internal validation (N=236) sets, and data for 82 patients from the Renmin Hospital of Wuhan University (RMHWHU) were used as an independent validation cohort. Our LASSO regression analyses of the training set yielded five clinicopathological features (race, AJCC stage, surgery for primary site, chemotherapy and tumor size), which were used to create a survival nomogram. Our survival nomogram achieved better predictive performance than the AJCC staging system, the current standard. Additionally, the calibration curves of the prognostic nomogram revealed good agreement between the predicted survival probabilities and the ground truth values. Indeed, our nomogram, which estimates individualized survival probabilities for patients with EODGC, shows good predictive accuracy and calibration ability for both the SEER and RMHWHU cohorts. These results suggest that a survival nomogram may be better at predicting OS for EODGC patients than the AJCC staging system.