Development and Validation of a Nomogram for Predicting Survival in Male Patients With Breast Cancer

The features of male breast cancer in China: A real-world study

BACKGROUND

Male breast cancer (MBC) is a rare disease. Although several large-scale studies have investigated MBC patients in other countries, the features of MBC patients in China have not been fully explored. This study aims to explore the features of Chinese MBC patients comprehensively.

METHODS

We retrospectively collected data of MBC patients from 36 centers in China. Overall survival (OS) was evaluated by the Kaplan-Meier method, log-rank test, and Cox regression analyses. Multivariate Cox analyses were used to identify independent prognostic factors of the patients.

RESULTS

In total, 1119 patients were included. The mean age at diagnosis was 60.9 years, and a significant extension over time was observed (P < 0.001). The majority of the patients (89.1 %) received mastectomy. Sentinel lymph node biopsy was performed in 7.8 % of the patients diagnosed in 2009 or earlier, and this percentage increased significantly to 38.8 % in 2020 or later (P < 0.001). The five-year OS rate for the population was 85.5 % [95 % confidence interval (CI), 82.8 %-88.4 %]. Multivariate Cox analysis identified taxane-based [T-based, hazard ratio (HR) = 0.32, 95 % CI, 0.13 to 0.78, P = 0.012] and anthracycline plus taxane-based (A + T-based, HR = 0.47, 95 % CI, 0.23 to 0.96, P = 0.037) regimens as independent protective factors for OS. However, the anthracycline-based regimen showed no significance in outcome (P = 0.175).

CONCLUSION

As the most extensive MBC study in China, we described the characteristics, treatment and prognosis of Chinese MBC population comprehensively. T-based and A + T-based regimens were protective factors for OS in these patients. More research is required for this population.

Nomograms predict survival in elderly women with triple-negative breast cancer: A SEER population-based study

BACKGROUND

The effective treatment of breast cancer in elderly patients remains a major challenge.

OBJECTIVE

To construct a nomogram affecting the overall survival of triple-negative breast cancer (TNBC) and establish a survival risk prediction model.

METHODS

A total of 5317 TPBC patients with negative expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) who were diagnosed and received systematic treatment from 2010 to 2015 were collected from the American Cancer Surveillance, Epidemiology and End Results (SEER) database. They were randomly divided into training set (n= 3721) and validation set (n= 1596). Univariate and multivariate Cox regression analysis were used to identify prognostic features, and a nomogram was established to predict the probability of 1-year, 3-year and 5-year OS and BCSS. We used consistency index (C-index), calibration curve, area under the curve (AUC) and decision curve analysis (DCA) to evaluate the predictive performance and clinical utility of the nomogram.

RESULTS

The C-indices of the nomograms for OS and BCSS in the training cohort were 0.797 and 0.825, respectively, whereas those in the validation cohort were 0.795 and 0.818, respectively. The receiver operating characteristic (ROC) curves had higher sensitivity at all specificity values as compared with the Tumor Node Metastasis (TNM) system. The calibration plot revealed a satisfactory relationship between survival rates and predicted outcomes in both the training and validation cohorts. DCA demonstrated that the nomogram had clinical utility when compared with the TNM staging system.

CONCLUSION

This study provides information on population-based clinical characteristics and prognostic factors for patients with triple-negative breast cancer, and constructs a reliable and accurate prognostic nomogram.

A Population-Based Research Utilized a Risk Stratification Model to Forecast the Overall Survival of Young Women With Diagnosed Stage IV Breast Cancer

BACKGROUND

The goal of this study is to develop a risk prediction model for estimating overall survival (OS) in young females diagnosed with stage IV breast cancer.

METHODS

The clinical information was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. To identify the dependent risk factors, we utilized the Cox proportional hazards regression model in both single and multivariate analyses. We then created a new nomogram to predict the 1-, 3-, and 5-year overall survival probability for these patients based on the identified risk factors.

RESULTS

Six hundred seventy-six patients who met the eligibility requirements were stochastically partitioned into training (n = 475) and validation (n = 201) groups in a 7:3 ratio. Histology, breast subtype, T classification, brain metastasis, bone metastasis, liver metastasis, and surgery were identified as independent prognostic factors for cancer. To predict the 1-, 3-, and 5-year overall survival (OS) probabilities, all of these independent factors were incorporated into nomograms. Our nomogram demonstrated a favorable discriminatory power, as evidenced by a C-index of 0.737 (95% CI: 0.708-0.766) and 0.717 (95% CI: 0.664-0.770) for the training and validation cohorts, respectively. The calibration curves showed satisfactory consistency in both cohorts. Using this nomogram, we developed a risk stratification model that categorized patients into low-, intermediate-, and high-risk groups.

CONCLUSION

The prediction model was more precisely to predict the OS of young females with stage IV breast cancer and could enable individualized risk estimation that could be conducive to physicians exploring therapeutic strategies for effectiveness.

Risk factors of early mortality in patients with small cell lung cancer: a retrospective study in the SEER database

BACKGROUND

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine cancer with a high risk of early mortality (i.e., survival time less than 1 month). This study aimed to identify relevant risk factors and predict early mortality in SCLC patients.

METHODS

A total of 27,163 SCLC cases registered between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) data. Significant independent risk factors were identified by univariate and multivariate logistic regression analyses. Nomograms for all-causes and cancer-specific early death were constructed and evaluated.

RESULTS

Age, sex, clinical stage, presence of metastasis (liver and lung), and absence of treatment (surgery, radiotherapy and chemotherapy) were identified for significant association with all-causes and cancer-specific early death. Nomograms based on these predictors exhibited high accuracy (area under ROC curve > 0.850) and potential clinical practicality in the prediction of early mortality.

CONCLUSION

We identified a set of factors associated with early mortality from SCLC and developed a clinically useful nomogram to predict high-risk patients. This nomogram could aid oncologists in the administration of individualized treatment regimens, potentially improving clinical outcomes of SCLC patients.

Characteristics and prognostic factors of adult patients with osteosarcoma from the SEER database

Osteosarcoma is the most common bone malignancy. There are many studies on the prognostic factors of children and adolescents, but the characteristics and prognostic factors of adult osteosarcoma are rarely studied. The aim of this study was to construct a nomogram for predicting the prognosis of adult osteosarcoma. Information on all osteosarcoma patients aged ≥ 18 years from 2004 to 2015 was downloaded from the surveillance, epidemiology and end results database. A total of 70% of the patients were included in the training set and 30% of the patients were included in the validation set. Univariate log-rank analysis and multivariate cox regression analysis were used to screen independent risk factors affecting the prognosis of adult osteosarcoma. These risk factors were used to construct a nomogram to predict 3-year and 5-year prognosis in adult osteosarcoma. Multivariate cox regression analysis yielded 6 clinicopathological features (age, primary site, tumor size, grade, American Joint Committee on Cancer stage, and surgery) for the prognosis of adult osteosarcoma patients in the training cohort. A nomogram was constructed based on these predictors to assess the prognosis of adult patients with osteosarcoma. Concordance index, receiver operating characteristic and calibration curves analyses also showed satisfactory performance of the nomogram in predicting prognosis. The constructed nomogram is a helpful tool for exactly predicting the prognosis of adult patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

The evaluation of tumor microenvironment infiltration and the identification of angiogenesis-related subgroups in skin cutaneous melanoma

BACKGROUND

There are limited studies on the association between angiogenesis-related genes (ARGs) and the predictive risk of melanoma, even though angiogenic factors, which are essential for tumor growth and metastasis, might be secreted by angiogenesis-related protein in skin cutaneous melanoma (SKCM). To forecast patient outcomes, this study attempts to develop a predictive risk signature linked to angiogenesis in cutaneous melanoma.

METHODS

In 650 patients with SKCM, the expression and mutation of ARGs were examined, and this information was related to the clinical prognosis. SKCM patients were split into two groups based on how well they performed on the ARG. The link between ARGs, risk genes, and immunological microenvironment was examined using a range of algorithmic analysis techniques. Based on these five risk genes, an angiogenesis risk signature was created. We developed a nomogram and examined the sensitivity of antineoplastic medications to help the proposed risk model's clinical applicability.

RESULTS

The risk model developed by ARGs revealed that the prognosis for the two groups was significantly different. The predictive risk score was negatively connected with memory B cells, activated memory CD4 + T cells, M1 macrophages, and CD8 + T cells, and favorably correlated with dendritic cells, mast cells, and neutrophils.

CONCLUSIONS

Our findings offer fresh perspectives on prognostic evaluation and imply that ARG modulation is implicated in SKCM. Potential medications for the treatment of individuals with various SKCM subtypes were predicted by drug sensitivity analysis.

Cancer-specific survival in patients with cholangiocarcinoma after radical surgery: a Novel, dynamic nomogram based on clinicopathological features and serum markers

BACKGROUND

This study aims to (1) identify preoperative testing-based characteristics associated with enhanced prognosis and survival for cholangiocarcinoma patients, and (2)create a distinctive nomogram to anticipate each patient's cancer-specific survival (CSS).

METHODS

Retrospective analysis was performed on 197 CCA patients who underwent radical surgery at Sun Yat-sen Memorial Hospital; they were divided into a 131-person "training cohort" and a 66-person "internal validation cohort." The prognostic nomogram was created following a preliminary Cox proportional hazard regression search for independent factors influencing the patients' CSS. Its applicable domain was examined via an external validation cohort, which included 235 patients from the Sun Yat-sen University Cancer Center.

RESULTS

The median follow-up period for the 131 patients in the training group was 49.3 months (range, 9.3 to 133.9 months). One-, three-, and five-year CSS rates were 68.7%, 24.5%, and 9.2%, respectively, with the median CSS length being 27.4 months (range: 1.4 to 125.2 months). PLT, CEA, AFP, tumor location, differentiation, lymph node metastasis, chemotherapy, and TNM stage were determined to be independent risk factors for CCA patients by univariate and multivariate Cox proportional hazard regression analysis. We were able to accurately predict postoperative CSS after incorporating all of these characteristics into a nomogram. The AJCC's 8th edition staging method's C-indices were statistically substantially (P < 0.001) lower than the nomogram's C-indices (0.84, 0.77, and 0.74 in the training, internal and external validation cohorts respectively).

CONCLUSIONS

A realistic and useful model for clinical decision-making and the optimization of therapy is presented as a nomogram that includes serum markers and clinicopathologic features for predicting postoperative survival in cholangiocarcinoma.

Determination of weight loss effectiveness evaluation indexes and establishment of a nomogram for forecasting the probability of effectiveness of weight loss in bariatric surgery: a retrospective cohort

BACKGROUND

The purpose of this research was to determine the index that contributes the most to assessing the effectiveness of weight loss 1 year following bariatric surgery and to implement it as the clinical outcome to develop and confirm a nomogram to predict whether bariatric surgery would be effective.

METHODS

Patient information was extracted from the Chinese Obesity and Metabolic Surgery Database for this retrospective study. The most contributing weight loss effectiveness evaluation index was created using canonical correlation analysis (CCA), and the predictors were screened using logistic regression analysis. A nomogram for estimating the likelihood of effectiveness of weight loss was constructed, and its performance was further verified.

RESULTS

Information was obtained for 540 patients, including 30 variables. According to the CCA, ≥25 percentage total weight loss was found to be the most correlated with patient information and contribute the most as a weight loss effectiveness evaluation index. Logistic regression analysis and nomogram scores identified age, surgical strategy, abdominal circumference, weight loss history, and hyperlipidemia as predictors of effectiveness in weight loss. The prediction model's discrimination, accuracy, and clinical benefit were demonstrated by the consistency index, calibration curve, and decision curve analysis.

CONCLUSIONS

The authors determined a 25 percentage total weight loss as an index for weight loss effectiveness assessment by CCA and next established and validated a nomogram, which demonstrated promising performance in predicting the probability of effectiveness of weight loss in bariatric surgery. The nomogram might be a valuable tool in clinical practice.

Development and Validation of Nomograms Predicting the 5- and 8-Year Overall and Cancer-Specific Survival of Bladder Cancer Patients Based on SEER Program

BACKGROUND

Bladder cancer is often prone to recurrence and metastasis. We sought to construct nomogram models to predict the overall survival (OS) and cancer-specific survival (CSS) of bladder cancer patients.

METHODS

A reliable random split-sample approach was used to divide patients into two groups: modeling and validation cohorts. Uni-variate and multivariate survival analyses were used to obtain the independent prognostic risk factors based on the modeling cohort. A nomogram was constructed using the R package, "rms". Harrell's concordance index (C-index), calibration curves and receiver operating characteristic (ROC) curves were applied to evaluate the discrimination, sensitivity and specificity of the nomograms using the R packages "hmisc", "rms" and "timeROC". A decision curve analysis (DCA) was used to evaluate the clinical value of the nomograms via R package "stdca.R".

RESULTS

10,478 and 10,379 patients were assigned into nomogram modeling and validation cohorts, respectively (split ratio ≈ 1:1). For OS and CSS, the C-index values for internal validation were 0.738 and 0.780, respectively, and the C-index values for external validation were 0.739 and 0.784, respectively. The area under the ROC curve (AUC) values for 5- and 8-year OS and CSS were all greater than 0.7. The calibration curves show that the predicted probability values of 5- and 8-year OS and CSS are close to the actual OS and CSS. The decision curve analysis revealed that the two nomograms have a positive clinical benefit.

CONCLUSION

We successfully constructed two nomograms to forecast OS and CSS for bladder cancer patients. This information can help clinicians conduct prognostic evaluations in an individualized manner and tailor personalized treatment plans.

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

INTRODUCTION

Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance.

OBJECTIVE

The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others.

METHODS

This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database.

RESULTS

Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820-0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/).

CONCLUSIONS

The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.

Development and validation of nomograms to predict the survival probability and occurrence of a second primary malignancy of male breast cancer patients: a population-based analysis

Background

Male breast cancer (MBC) is rare, which has restricted prospective research among MBC patients. With effective treatments, the prognosis of MBC patients has improved and developing a second primary malignancy (SPM) has become a life-threatening event for MBC survivors. However, few studies have focused on the prognosis of MBC patients and looked into the SPM issue in MBC survivors.

Method

We reviewed MBC patients diagnosed between 1990 and 2016 from the latest Surveillance, Epidemiology, and End Results (SEER) Plus database. Competing risk models and nomograms were conducted for predicting the risk of cancer-specific death and SPM occurrence. C-indexes, calibration curves, ROC curves, and decision curve analysis (DCA) curves were applied for validation.

Result

A total of 1,843 MBC patients with complete information were finally enrolled and 60 (3.26%) had developed an SPM. Prostate cancer (40%) was the most common SPM. The median OS of all the enrolled patients was 102.41 months, while the median latency from the initial MBC diagnosis to the subsequent diagnosis of SPM was 67.2 months. The patients who suffered from an SPM shared a longer OS than those patients with only one MBC (p = 0.027). The patients were randomly divided into the development cohort and the validation cohort (at a ratio of 7:3). The Fine and Gray competing risk model was used to identify the risk factors. Two nomograms were constructed and validated to predict the 5-year, 8-year, and 10-year survival probability of MBC patients, both of which had good performance in the C-index, ROC curves, calibration plots, and DCA curves, showing the ideal discrimination capability and predictive value clinically. Furthermore, we, for the first time, constructed a nomogram based on the competing risk model to predict the 5-year, 8-year, and 10-year probability of developing an SPM in MBC survivors, which also showed good discrimination, calibration, and clinical effectiveness.

Conclusion

We, for the first time, included treatment information and clinical parameters to construct a nomogram to predict not only the survival probability of MBC patients but also the probability of developing an SPM in MBC survivors, which were helpful in individual risk estimation, patient follow-up, and counseling in MBC patients.

A prognostic nomogram to predict survival in elderly patients with small-cell lung cancer: a large population-based cohort study and external validation

BACKGROUND

Age is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

A total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA).

RESULTS

Ten independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks.

CONCLUSION

We constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.

Risk stratification system and visualized dynamic nomogram constructed for predicting diagnosis and prognosis in rare male breast cancer patients with bone metastases

Background

Bone metastases (BM) from malignant tumors could disrupt the balance between osteoclasts and osteoblasts and affect bone homeostasis. Malignant breast cancer (BC) is rare in male patients, and co-occurrence of BM is even rarer. Given its low incidence, there is limited research evaluating risk and prognosis. Despite the widespread application of nomograms to predict uncommon malignancies, no studies have constructed predictive models focusing on the diagnosis and prognosis of male breast cancer with bone metastases (MBCBM).

Methods

This study selected all male breast cancer patients (MBC) between 2010 and 2019 in the Surveillance, Epidemiology, and End Results (SEER) database. We used simple and multivariate Logistic regression analyses to identify independent risk factors for BM in MBC patients. Then simple and multivariate Cox regression analyses were employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in MBCBM patients. We established and validated three new nomograms based on these independent factors.

Result

A total of 4187 MBC patients were included, with 191 (4.56%) having bone metastases at the time of diagnosis. The independent risk factors of BM in MBC patients included age, tumor size, marital status, T stage, and N stage. In MBCBM patients, independent prognostic factors for OS and CSS were both age, T stage, ER status, PR status, and surgery. The concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve analysis (DCA) confirmed that these three nomograms could accurately predict the diagnosis and prognosis of MBCBM patients with excellent discrimination and clinical utility superior to the TNM staging system. We then established two prognostic-based risk stratification systems and three visualized dynamic nomograms that could be applied in clinical practice.

Conclusion

In conclusion, this study aimed to establish and validate an accurate novel nomogram to objectively predict the diagnosis and prognosis of MBCBM patients. On this basis, prognostic-based risk stratification systems and visualized dynamic nomograms were constructed to facilitate doctors and patients to quantify individual BM risk probability and survival probability to assist in personalized risk assessment and clinical decision-making.

Prognostic risk factor analysis and nomogram construction for primary liver cancer in elderly patients based on SEER database

OBJECTIVE

To evaluate the risk factors and construct a nomogram model for the prognosis of primary liver cancer in the elderly based on the data from the US SEER database.

METHODS

The latest data of patients with primary liver cancer were extracted from the SEER database using SEER*STAT software, and the required variables were included. The data were screened and then divided into a training cohort and a validation cohort. A nomogram model was constructed by screening the variables through univariate and multivariate Cox analysis. The C-Index, ROC and calibration curves were used for model evaluation.

RESULTS

A total of 10 824 eligible cases from 2004 to 2017 were extracted, among which, 7757 cases were included in the training cohort and 3247 in the validation cohort. The C-Index of the model was 0.747 (in the training cohort) and 0.773 (in the validation cohort). The 3-year area under the curve (AUCs) of the training and the validation cohorts were 0.760 and 0.750, and the 5-year AUCs of the two cohorts were 0.761 and 0.748. The calibration curves showed an ideal calibration of the constructed model.

CONCLUSIONS

The nomogram model constructed followed by Cox regression analysis showed moderate calibration and discrimination property, and can provide reference to a certain extent for furture clinical application of primary liver cancer in the elderly.

Identification of hub genes and construction of diagnostic nomogram model in schizophrenia

Schizophrenia (SCZ), which is characterized by debilitating neuropsychiatric disorders with significant cognitive impairment, remains an etiological and therapeutic challenge. Using transcriptomic profile analysis, disease-related biomarkers linked with SCZ have been identified, and clinical outcomes can also be predicted. This study aimed to discover diagnostic hub genes and investigate their possible involvement in SCZ immunopathology. The Gene Expression Omnibus (GEO) database was utilized to get SCZ Gene expression data. Differentially expressed genes (DEGs) were identified and enriched by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and disease ontology (DO) analysis. The related gene modules were then examined using integrated weighted gene co-expression network analysis. Single-sample gene set enrichment (GSEA) was exploited to detect immune infiltration. SVM-REF, random forest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to identify hub genes. A diagnostic model of nomogram was constructed for SCZ prediction based on the hub genes. The clinical utility of nomogram prediction was evaluated, and the diagnostic utility of hub genes was validated. mRNA levels of the candidate genes in SCZ rat model were determined. Finally, 24 DEGs were discovered, the majority of which were enriched in biological pathways and activities. Four hub genes (NEUROD6, NMU, PVALB, and NECAB1) were identified. A difference in immune infiltration was identified between SCZ and normal groups, and immune cells were shown to potentially interact with hub genes. The hub gene model for the two datasets was verified, showing good discrimination of the nomogram. Calibration curves demonstrated valid concordance between predicted and practical probabilities, and the nomogram was verified to be clinically useful. According to our research, NEUROD6, NMU, PVALB, and NECAB1 are prospective biomarkers in SCZ and that a reliable nomogram based on hub genes could be helpful for SCZ risk prediction.

CD133 is an independent predictive and prognostic marker in metastatic breast cancer

BACKGROUND

CD133 is a transmembrane glycoprotein and is considered the most common cell surface marker to identify cancer stem cells in hematological and solid tumors, including breast cancer.

OBJECTIVES

To evaluate the impact of immunohistochemical expression of CD133 on response rate and survival in metastatic breast cancer, as well as to correlate it with various demographics and clinicopathological characteristics.

METHODS

One-hundred metastatic breast cancer patients were prospectively recruited at the Medical Oncology Department at South Egypt Cancer Institute during the period from January 2018 to January 2020.

RESULTS

There was a statistically significant correlation between CD133 positive patients with various adverse clinicopathological parameters such as high grade (p= 0.013), higher tumor (p= 0.001), and nodal staging (p= 0.024) during a median follow-up time of 17 months. In addition, Cases with CD133 positive expression had a significantly lower survival time than those with negative expression (3-years OS 37.4% versus 85.5%, p= 0.024). Regarding the response rate, CD133 positive patients had a lower response rate than negative patients (50% versus 54%, p= 0.012).

CONCLUSIONS

Positive CD133 is correlated with poor prognosis in metastatic breast cancer patients.

Establishment of prognostic model for postoperative patients with metaplastic breast cancer: Based on a retrospective large data analysis and Chinese multicenter study

Purpose: Models for predicting postoperative overall survival of patients with metaplastic breast cancer have not yet been discovered. The purpose of this study is to establish a model for predicting postoperative overall survival of metaplastic breast cancer patients.

Methods: Patients in the Surveillance, Epidemiology, and End Results database diagnosed with MBC from 2010 to 2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell’s consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. We verify the performance of the prediction model with a Chinese multi-center data set.

Results: Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the eighth edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. It has also been verified in the SEER internal verification set and the Chinese multi-center data set.

Conclusion: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.

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.

Development and validation of a nomogram to predict survival for advanced male breast cancer

Male breast cancer is a rare disease. Many experiences of male breast cancer were derived of female breast cancer. However, there are huge differences between two groups. We conducted this study to find a reliable prognostic model for advanced male breast cancer. The cohort was selected from the Surveillance, Epidemiology, and End Results database. The enrolled patients were randomly divided into training and validation group. The univariate and multivariate analyses were used for prognostic assessment and a nomogram was built. Calibration curves and concordance index were compiled to determine predictive and discriminatory capacity. The time-dependent receiver operating curves and the decision curve analysis was used to verify the model's ability. Two hundred and eighty individuals were enrolled. The cumulative rates of 1-, 3- and 5-year overall survival (OS) rates were 98.6%, 72% and 57.9%. The C-indexes for OS were 0.835 (95%CI, 0.777-0.893) in the training group and 0.765 (95%CI, 0.668-0.862) in the validation group. The calibration curves confirmed the consistency of the nomogram both in the training and validation group. The time-dependent receiver operating curves and decision curve analysis demonstrated that the nomogram had better prediction capacity than TNM stage system for advanced male breast cancer. The nomogram we built was a reliable and solid predictive model.

Nomogram for distant metastasis-free survival in patients with locoregionally advanced nasopharyngeal carcinoma

OBJECTIVE

To develop and validate a nomogram to predict distant metastasis-free survival of patients with locoregionally advanced nasopharyngeal carcinoma.

METHODS

We collected the total clinical data of 820 nasopharyngeal carcinoma (NPC) patients, of whom 482 formed the training cohort from one hospital and 328 made up the validation cohort from another hospital. By analyzing the prognosis of all patients after intensity-modulated radiotherapy by univariate and multivariate Cox regression models, a nomogram related to DMFS was created in the training cohort. The discriminatory and calibration power of the nomogram was successively assessed in the training and validation cohorts by the C‑index and calibration curve. The predictive ability for 3‑year DMFS was compared between the nomogram and TNM stage using ROC curves. Patients were divided into different risk groups based on scores calculated from the nomogram.

RESULTS

Age, lymph node gross tumor volume (GTVnd), and gross tumor volume of the nasopharynx (GTVnx) were the factors included in the nomogram. The C‑index of the nomogram was 0.721 in the training cohort and 0.750 in the validation cohort. The calibration curves were satisfactory. Patients in the high-risk group were more likely to develop metastases.

CONCLUSION

A nomogram incorporating age, GTVnd, and GTVnx showed good performance for predicting DMFS in patients with locoregionally advanced NPC.

Development and Verification of Prognostic Nomogram for Penile Cancer Based on the SEER Database

Aim

We aimed to establish a prognostic nomogram for penile cancer (PC) patients based on the Surveillance, Epidemiology, and End Results Program (SEER) database.

Methods

Data from 1643 patients between 2010 and 2015 were downloaded and extracted from the SEER database. They were randomly divided into the development group (70%) and the verification group (30%), and then, univariate and multivariate Cox proportional hazards regression, respectively, was used to explore the possible risk factors of PC. The factors significantly related to overall survival (OS) and cancer-specific survival (CSS) were used to establish the nomogram, which was assessed via the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve. An internal validation was conducted to test the accuracy and effectiveness of the nomogram. Kaplan–Meier calculation was used to predict the further OS and CSS status of these patients.

Results

On multivariate Cox proportional hazards regression, the independent prognostic risk factors associated with OS were age, race, marital status, N/M stage, surgery, surgery of lymph nodes, and histologic type, with a moderate C-index of 0.737 (95% confidence interval (CI): 0.713–0.760) and 0.766 (95% CI: 0.731–0.801) in the development and verification groups, respectively. The areas under the ROC (AUC) of 3- and 5-year OS were 0.749 and 0.770, respectively. While marital status, N/M stage, surgery, surgery of lymph nodes, and histologic type were significantly linked to PC patients' CSS, which have better C-index of 0.802 (95% confidence interval (CI): 0.771–0.833) and 0.82 (95% CI: 0.775–0.865) in the development and verification groups, and the AUC of 3- and 5-year CSS were 0.766 and 0.787. Both of the survival calibration curves of 3- and 5-year OS and CSS brought out a high consistency.

Conclusion

Our study produced a satisfactory nomogram revealing the survival of PC patients, which could be helpful for clinicians to assess the situation of PC patients and to implement further treatment.

Prognostic Factors in Male Breast Cancer: A Retrospective Nationwide Study in South Korea by the Study of SMARTSHIP Group

This study evaluated the incidence, the survival outcomes and its prognostic factors for male breast cancer (MBC) in Korea. Using the National Health Insurance Service database of Korea, we identified MBC patients who had the new claim code of C50. Medical records including type of surgeries and radiotherapy within one year of the first claim and death records were reviewed. Between 2005 and 2016, 838 newly diagnosed MBC patients were included (median follow-up, 1,769 days). The 70–74-year age group had the highest incidence of MBC. The 5-year survival rate was 73.7%. Age > 65 years, low income, no surgical intervention, no tamoxifen use, and > 2 comorbidities correlated with a worse outcome. MBC incidence has increased over time, and its peak is noted at age > 70 years. Age > 65 years, > 2 comorbidities, no surgical intervention, and no tamoxifen use correlate to poor prognosis.

Prognostic difficulties of men with breast cancer

Most adequately powered studies confirm a worse prognosis for males versus matched females with breast cancer. There is in-stage migration for stage I cancers with a different ratio of tumor/normal breast tissue in males. Younger men have a better prognosis, largely the result of increased morbidity in the elderly, exacerbated by smoking, low socioeconomic differences, and ethnic disparity. BRCA2 carriers with MBC have a worse outcome than noncarriers as do men with amplification of EMSY. Men with tumors having a high cytosol level of plasminogen activator inhibitor 1 (PAI-1) may have more invasive cancers leading to earlier spread and hence a worse outcome. PREDICT+ is a useful prognostic model for MBC and multigene testing enables more specific systemic therapies to be used.

Development and Validation of Prognostic Nomograms for Elderly Patients with Osteosarcoma

Background

The aim of the current study was to construct prognostic nomograms for individual risk prediction in elderly patients with osteosarcoma.

Methods

Data for 816 elderly patients (≥40 years old) with osteosarcoma between 2004 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were randomly assigned to training (N=573) and internal validation (N=243) sets. The essential clinical predictors were identified based on least absolute shrinkage and selection operator (Lasso) Cox regression. Nomograms were constructed to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS).

Results

Our LASSO regression analyses of the training set yielded five clinicopathological features (age, chemotherapy, surgery, AJCC stage, and summary stage) in the training cohort for the prognosis of elderly patients with osteosarcoma, while grade was only associated with OS and M stage was only associated with CSS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of elderly patients with osteosarcoma. The C-index, calibration and decision curve analysis also showed the satisfactory performance of these nomograms for prognosis prediction.

Conclusion

The constructed nomograms are helpful tools for exactly predicting the prognosis of elderly patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study

Objective

The prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat.

Methods

We identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system.

Results

Grade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes.

Conclusion

We have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.

A Nomogram Model to Predict Prognosis of Patients With Genitourinary Sarcoma

Objectives

The aim of this study is to evaluate the significant factors influencing the overall survival (OS) and recurrence free survival (RFS) and make an attempt to develop a nomogram for predicting the prognosis of patients with genitourinary sarcoma (GS).

Methods

Data on adult GS from 1985 to 2010 were collected. The impact of clinical factors on OS and RFS were estimated by Kaplan–Meier (KM) analysis, and differences between groups were analyzed by the log-rank test. To establish a nomogram, all patients were randomly divided into a training set (n = 125) and a testing set (n = 63). Cox proportion hazard model was utilized to assess the prognostic effect of variables. Then, a nomogram was established to estimate 1-, 3-, and 5-year OS based on Cox regression model. Subsequently, the nomogram was validated by a training set and a validation set.

Results

A total of 188 patients were enrolled into our study. Male patients with bladder sarcoma had better OS rather than RFS when stratified by gender (P = 0.022). According to histological subtypes, patients with leiomyosarcoma (LMS) undergoing chemotherapy were associated with favorable OS (P = 0.024) and RFS (P = 0.001). Furthermore, LMS in kidney sarcoma were associated with lower recurrence rate in comparison to rhabdomyosarcoma (RMS) (P = 0.043). Margin status after surgical excision markedly influenced the OS and RFS of GS patients and negative margins presented optimal prognosis. Chemotherapy was associated with improved OS for patients without surgery (P = 0.029) and patients with positive margins (P = 0.026). Based on the multivariate analysis of the training cohort, age, gender, surgery status, histological subtype, and chemotherapy were included in our nomogram for prediction of OS. The nomogram had sufficient power with concordance index (C-index) of OS: 0.770, 95%CI: 0.760–0.772 and area under curve (AUC) of OS: 0.759, 95%CI: 0.658–0.859 in the training set and with C-index of OS: 0.741, 95%CI: 0.740–0.765, and AUC of OS: 0.744, 95%CI: 0.576–0.913 in the validation set.

Conclusions

Adults GS is a group of extremely rare tumors with poor prognosis. Of all histological types, LMS is sensitive to chemotherapy. We highlighted the cardinal role of surgical resection and the importance of achieving negative margins. We identified the efficacy of chemotherapy for patients with positive margins and those without surgery as well. A nomogram is validated as an effective tool predicting short-term outcomes.

Describing Treatment Patterns for Elderly Patients with Intrahepatic Cholangiocarcinoma and Predicting Prognosis by a Validated Model: A Population-Based Study

Background: Elderly patients with Intrahepatic Cholangiocarcinoma (ICC) are frequently under-represented in clinical trials, which leads to the unclear management of ICC in elderly patients. This study aimed to describe treatment patterns and establish a reliable nomogram in elderly ICC patients.

Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, we conducted a retrospective analysis of 1651 elderly patients (≥65 years) diagnosed with ICC between 2004 and 2016.

Results: For the whole study population, 29.3% received only chemotherapy, 26.7% no tumor-directed therapy, 19.1% surgery alone, 17.5% radiotherapy, and 7.4% surgery plus chemotherapy. Compared with the age group of 65-74 years, patients aged ≥75 years were less likely to accept treatment. Among patients 66-74 years of age, surgery alone resulted in a median overall survival (OS) of 30 months, surgery combined with chemotherapy 26 months, radiotherapy 17 months, chemotherapy alone 10 months and no therapy 3 months; while among patients ≥75 years of age, the median OS was 21, 25, 14, 9 and 4, respectively. Moreover, independent prognostic indicators including age, gender, grade, tumor size, T stage, N stage, M stage, and treatment were incorporated to construct a nomogram. The C-indexes of the OS nomogram were 0.725 and 0.724 for the training and validation cohorts, respectively. Importantly, the predictive model harbored a better discriminative power than the American Joint Committee on Cancer TNM staging system.

Conclusion: Active treatment should not be abandoned among all the elderly patients with ICC. The validated nomogram provided an effective and practical tool to accurately evaluate prognosis and to guide personalized treatment for elderly ICC patients.

Establishment and validation of a novel nomogram to predict overall survival in nasopharyngeal carcinoma with lymph node metastasis

BACKGROUND

The aim of the present study was to establish and validate a nomogram to predict the overall survival (OS) in nasopharyngeal carcinoma (NPC) patients with lymph node metastasis (LNM).

METHODS

A novel nomogram was constructed using 863 patients with LNM-positive NPC from the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors in the nomograms were determined using multivariate Cox risk analysis. The predictive capability was evaluated using calibration curves and decision curve analysis (DCA).

RESULTS

Multivariate analysis identified seven factors that could be used to construct the nomogram: age, pathological type, T stage, M stage, surgery of primary site, radiotherapy, and chemotherapy. The calibration curves and DCA demonstrated optimal agreement. Based on the nomogram, all patients could be stratified into three risk groups: low, middle, and high.

CONCLUSIONS

The novel nomogram demonstrated its potential as an individualized tool to predict OS.

Characteristics of Early Death in Patients With Localized Nasopharyngeal Cancer: A Population-Based SEER Analysis

Localized nasopharyngeal cancer (NPC) is a highly curable disease, but the prognosis of certain cases is still poor. Distinguishing patients with a poor outcome is necessary when developing therapeutic strategies. The aim of this study was to investigate the characteristics of early death (ED) among patients with localized NPC, and to identify independent predictors of ED. Patients diagnosed with localized NPC were included from the Surveillance, Epidemiology, and End Results dataset, and univariate and multivariate logistic regression analyses were performed to identify ED predictors. A total of 752 patients with localized NPC were enrolled, including 198 cases of ED and 480 long-term survivors. Older age, unmarried status, and white race were risk factors for ED, whereas diagnosis in the recent period and undifferentiated non-keratinizing histology type were protective factors. In addition, for older patients, women and those without radiation treatment, there was less ED for married patients than unmarried patients. In conclusion, this population-based study provides an overview of the characteristics of ED patients with localized NPC. Age, race, marital status, year of diagnosis and histology type are risk factors for ED. Moreover, married patients are at a significantly lower risk of ED. This protective effect is especially pronounced in older people, women and those without radiation treatment.

Log ODDS (LODDS) of positive lymph nodes as a predictor of overall survival in squamous cell carcinoma of the penis

OBJECTIVE

To evaluate the role of logarithmic ODDS (LODDS) in the number of positive lymph nodes and the number of negative lymph nodes as a prognostic metric in the squamous cell carcinoma (SCC) penis.

METHODS

Data were retrospectively collected from 96 cases of SCC penis that underwent bilateral groin dissection between 2010 and 2015 at our institute. Lymph node density (LND) and LODDS were calculated for all the patients and classified according to American Joint Committee on Cancer (AJCC) pN staging. Thresholds for LND (24% and 46%) and LODDS (-0.75 and 0) were established. Multivariate analysis of various cofactors was done with overall survival (OS) as a dependent factor. Three classification systems were compared using receiver operative characteristic (ROC) curve analysis.

RESULTS

Univariate analysis showed that AJCC pN, LND, and LODDS were all significantly correlated with OS. However, only LODDS (HR, 11.185; p = .023) remained an independent prognostic factor through multivariate analysis. LODDS (log-likelihood = 3832 vs. 3798; p < .001) had better prognostic performance than pN and better discriminatory ability than LND (AIC = 3902 vs. 3928). LODDS had better power of discrimination than LND and pN. LODDS could predict survival in lymph node yield (LNY) < 15 (p < .001).

CONCLUSION

LODDS is an independent predictor of OS in the SCC penis and has superior prognostic significance than the AJCC pN and LND classification systems.

Development and external validation of a nomogram for predicting the effect of tumor size on cancer-specific survival of resected gallbladder cancer: a population-based study

BACKGROUND

The impact of tumor size on account of the long-term survival results in gallbladder cancer (GBC) patients has been controversial. It is urgent necessary to identify the optimal cut-off value of tumor size in resected GBC, and we attempted to integrate tumor size with other prognostic factors into a prognostic nomogram to predict the cancer-specific survival (CSS) of GBC patients.

METHODS

1639 patients with resected GBC were extracted from the Surveillance, Epidemiology and End Results (SEER) database. X-tile program was used to identify the optimal cut-off value of tumor size. A nomogram including tumor size was established to predict 1-, 3- and 5-year CSS based on the independent risk factors chosen by univariate and multivariable cox analyses. The precision of the nomogram for predicting survival was validated with Harrell's concordance index (C-index), calibration curves, and receiver operating characteristic curve (ROC) internally and externally.

RESULTS

Patients with GBC were classified into 1-13 mm, 14-63 mm and 64 mm subgroup based on the optimal cut-off for tumor size in terms of CSS. The nomogram according to the independent factors was well calibrated and displayed better discrimination power than 7th tumor-node-metastasis (TNM) stage systems.

CONCLUSIONS

The results demonstrated that increased tumor size is closely associated with the worse CSS. Our novel nomogram, which outperforms the conventional TNM staging system, showed satisfactory accuracy and clinically practicality for predicting the outcome of resected GBC patients.

Papillary Thyroid Microcarcinoma: A Nomogram Based on Clinical and Ultrasound Features to Improve the Prediction of Lymph Node Metastases in the Central Compartment

BACKGROUND

Accurate preoperative identification of central lymph node metastasis (CLNM) is essential for surgical protocol establishment for patients with papillary thyroid microcarcinoma (PTMC). We aimed to develop a clinical and ultrasound characteristics-based nomogram for predicting CLNM.

METHODS

Our study included 399 patients who were pathologically diagnosed with PTMC between January 2011 and June 2018. Clinical and ultrasound features were collected for univariate and multivariate analyses to determine risk factors of CLNM. A nomogram comprising the prognostic model to predict the CLNM was established, and internal validation in the cohort was performed. The Cox regression model was used to determine the risk factors for recurrence-free survival (RFS) and cumulative hazard was calculated to predict prognosis.

RESULTS

Three variables of clinical and US features as potential predictors including sex (odd ratio [OR] = 1.888, 95% confidence interval [CI], 1.160-3.075; P =0.011), tumor size (OR = 1.933, 95% CI, 1.250-2.990; P =0.003) and ETE (OR = 6.829, 95% CI, 3.250-14.350; P <0.001) were taken into account. The predictive nomogram was established by involving all the factors above used for preoperative prediction of CLNM in patients with PTMC. The nomogram showed excellent calibration in predicting CLNM, with area under curves (AUC) of 0.684 (95% CI, 0.635 to 0.774). Furthermore, tumor size, multifocality, presence of ETE, vascular invasion, and CLNM were the significant factors related to the RFS.

CONCLUSION

Through this easy-to-use nomogram by combining clinical and US risk factor, the possibility of CLNM can be objectively quantified preoperatively. This prediction model may serve as a useful clinical tool to help clinicians determine an individual's risk of CLNM in PTMC, thus make individualized treatment plans accordingly.

Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients

Background: The present study was aimed at developing nomograms estimating the overall survival (OS) and cancer-specific survival (CSS) of endometrial cancer (EC)-affected patients.

Patients and Methods: We retrospectively collected 145,445 EC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These risk factors were used to establish nomograms to predict 3- and 5-year OS and CSS rates. Internal and external data were used for validation. The predictive accuracy and discriminative ability were measured by using concordance index (C-index) and risk group stratification.

Results: A total of 63,510 patients were collected and randomly assigned into the training cohort (n = 42,340) and the validation cohort (n = 21,170). Age at diagnosis, marital status, tumor size, histologic type, lymph node metastasis, tumor grade, and clinical stage were identified as independent prognostic factors for OS and CSS (p < 0.05 according to multivariate Cox analysis) and were further used to construct the nomograms. The area under the receiver operating characteristics (ROC) curve was greater than that of International Federation of Gynecology and Obstetrics (FIGO) staging system for predicting OS (0.83 vs. 0.73, p < 0.01) and CSS (0.87 vs. 0.79, p < 0.01) in the training cohort. The stratification into different risk groups ensured a significant distinction between survival curves within different FIGO staging categories.

Conclusion: We constructed and validated nomograms that accurately predicting OS and CSS in EC patients. The nomograms can be used for estimating OS and CSS of individual patients and establishing their risk stratification.

Construction and validation of a nomogram for predicting cancer-specific survival in hepatocellular carcinoma patients

The prognosis of patients with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a research hotspot. This study aimed to incorporate important factors obtained from SEER database to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of patients with HCC and ICC. We obtained patient data from SEER database. The nomogram was constructed base on six prognostic factors for predicting CSS rates in HCC patients. The nomogram was validated by concordance index (C-index), the receiver operating characteristic (ROC) curve and calibration curves. A total of 3227 patients diagnosed with HCC (3038) and ICC (189) between 2010 and 2015 were included in this study. The C-index of the nomogram for HCC patients was 0.790 in the training cohort and 0.806 in the validation cohort. The 3- and 5-year AUCs were 0.811 and 0.793 in the training cohort. The calibration plots indicated that there was good agreement between the actual observations and predictions. In conclusion, we constructed and validated a nomogram for predicting the 3- and 5-year CSS in HCC patients. We have confirmed the precise calibration and excellent discrimination power of our nomogram.

Development and validation of a nomogram to predict overall survival for patients with metastatic renal cell carcinoma

Background

Heterogeneity of metastatic renal cell carcinoma (RCC) constraints accurate prognosis prediction of the tumor. We therefore aimed at developing a novel nomogram for accurate prediction of overall survival (OS) of patients with metastatic RCC.

Methods

We extracted 2010 to 2016 data for metastatic RCC patients in the Surveillance, Epidemiology, and End Results (SEER) database, and randomly stratified them equally into training and validation sets. Prognostic factors for OS were analyzed using Cox regression models, and thereafter integrated into a 1, 3 and 5-year OS predictive nomogram. The nomogram was validated using the training and validation sets. The performance of this model was evaluated by the Harrell’s concordance index (C-index), calibration curve, integrated discrimination improvement (IDI), category-free net reclassification improvement (NRI), index of prediction accuracy (IPA), and decision curve analysis (DCA).

Results

Overall, 2315 metastatic RCC patients in the SEER database who fulfilled our inclusion criteria were utilized in constructing a nomogram for predicting OS of newly diagnosed metastatic RCC patients. The nomogram incorporated eight clinical factors: Fuhrman grade, lymph node status, sarcomatoid feature, cancer-directed surgery and bone, brain, liver, and lung metastases, all significantly associated with OS. The model was superior to the American Joint Committee on Cancer (AJCC) staging system (7th edition) both in training (C-indices, 0.701 vs. 0.612, P < 0.001) and validation sets (C-indices, 0.676 vs. 0.600, P < 0.001). The calibration plots of the nomogram corresponded well between predicted and observed values. NRI, IDI, and IPA further validated the superior predictive capability of the nomogram relative to the AJCC staging system. The DCA plots revealed reliable clinical application of our model in prognosis prediction of metastatic RCC patients.

Conclusions

We developed and validated an accurate nomogram for individual OS prediction of metastatic RCC patients. This nomogram can be applied in design of clinical trials, patient counseling, and rationalizing therapeutic modalities.

The Prognostic Prediction Value of Systemic Inflammation Score and the Development of a Nomogram for Patients With Surgically Treated Breast Cancer

Background: Systemic inflammation score (SIS) has been verified as a novel prognostic indicator in several cancer types. However, its prognostic value in breast cancer remains unknown. Furthermore, a nomogram based on SIS is yet to be constructed for breast cancer. We conducted this study to explore the association between SIS and prognosis of breast cancer, and to construct a good prognostic nomogram model. Methods: A total of 1,180 breast cancer patients who underwent curative surgery between December 2010 and January 2013 were recruited. They were randomly assigned to the training set (n = 944) or the validation set (n = 236). All patient blood samples were collected within 1 week prior to operation. According to previous reports, SIS was calculated for all patients, who were then classified into two groups: high-SIS and low-SIS. The Kaplan-Meier method was employed for survival analyses, and univariate and multivariate analyses (Cox proportional hazards regression model) were used for prognostic assessment. A nomogram was constructed based on the results of multivariate analysis. Calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. Results: In the training set, the median follow-up time was 6.07 years. Patients in the high-SIS group had an average OS time of 68.05 months, which is shorter than that of the low-SIS group (72.87 months; P = 0.033). Patients in the high-SIS group had average RFS and DMFS times of 56.04 and 54.46 months, respectively, which are shorter than those of the low-SIS group (60.85 and 59.47 months, respectively; P = 0.247 and P = 0.032). Univariate and multivariate analyses revealed SIS to be an independent prognostic factor for OS and DMFS time. The nomogram for the training set indicated OS and DMFS C-indexes of 0.794 (95% CI, 0.772-0.816) and 0.712 (95% CI, 0.684-0.740), respectively. In the validation set, the OS and DMFS C-indexes were 0.889 (95% CI, 0.845-0.933) and 0.696 (95%. CI, 0.611-0.781), respectively. Conclusions: SIS was confirmed as an independent prognostic predictor among patients with breast cancer who had undergone surgery with curative intent. Higher preoperative SIS may indicate higher risk of metastasis and shorter overall survival time. The prognostic nomogram based on SIS was dependable for breast cancer patients who underwent curative surgery.

A Predicting Nomogram for Mortality in Patients With COVID-19

Background: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (1). Purpose: Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention. Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness. Results: The predictors included in the nomogram were c-reactive protein, PaO2/FiO2, and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972-1.000) and 0.956 (95% CI, 0.874-1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness. Conclusion: This study provides a nomogram containing c-reactive protein, PaO2/FiO2, and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients.

Development and Validation of a Prognostic Nomogram to Predict Cancer-Specific Survival in Adult Patients With Pineoblastoma

Pineoblastoma (PB) is a rare neoplasm of the central nervous system. This analysis aimed to identify factors and establish a predictive model for the prognosis of adult patients with PB. Data for 213 adult patients with PB (Surveillance, Epidemiology, and End Results database) were randomly divided into primary and validation cohorts. A predictive model was established and optimized based on the Akaike Information Criterion and visualized by a nomogram. Its predictive performance (concordance index and receiver operating characteristic curve) and clinical utility (decision curve analyses) were evaluated. We internally and externally validated the model using calibration curves. Multivariate Cox regression analysis identified age, year of diagnosis, therapy, tumor size, and tumor extension as independent predictors of PB. The model exhibited great discriminative ability (concordance index of the nomogram: 0.802; 95% confidence interval: 0.78-0.83; area under the receiver operating characteristic curve: ranging from 0.7 to 0.8). Calibration plots (probability of survival) showed good consistency between the actual observation and the nomogram prediction in both cohorts, and the decision curve analyses demonstrated great clinical utility of the nomogram. The nomogram is a useful and practical tool for evaluating prognosis and determining appropriate therapy strategies.

Nomogram prediction of overall survival based on log odds of positive lymph nodes for patients with penile squamous cell carcinoma

Purpose

This study aimed to establish a nomogram to predict the long‐term overall survival (OS) for patients with penile squamous cell carcinoma (PSCC).

Method

The PSCC patients receiving regional lymph node dissection (RLND) were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The dataset of all eligible patients were used to develop the predictive model. The significant independent predictors were identified through Cox regression modeling based on the Bayesian information criterion and then incorporated into a nomogram to predicted 1‐, 3‐, and 5‐year OS. Internal validation was performed using the bootstrap resampling method. The model performance was evaluated using Harrell's concordance index (C‐index), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

Totally, 384 eligible PSCC patients were enrolled from the SEER database. A nomogram for OS prediction was developed, in which three clinical variables significantly associated with OS were integrated, including age, N classification, and log odds of positive lymph nodes (LODDS). The C‐index of the nomogram (0.746, 95% CI: 0.702‐0.790) was significantly higher than that of the American Joint Committee on Cancer (AJCC) staging system (0.692, 95% CI: 0.646‐0.738, P = .020). The bootstrap optimism‐corrected C‐index for the nomogram was 0.739 (95% CI: 0.690‐0.784). The bias‐corrected calibration plots showed the predicted risks were in good accordance with the actual risks. The results of NRI, IDI, and DCA exhibited superior predictive capability and higher clinical use of the nomogram compared with the AJCC staging system.

Conclusion

We successfully constructed a simple and reliable nomogram for OS prediction among PSCC patients receiving RLND, which would be beneficial to clinical trial design, patient counseling, and therapeutic modality selection.

Prognostic Factors Analysis and Nomogram Construction of Dual Primary Lung Cancer: A Population Study

As a special type of lung cancer, multiple primary lung cancer (MPLC) has unique biological characteristics, and its research remains limited. The aim of our research was to identify prognostic factors and construct a prognostic nomogram of dual primary lung cancer (DPLC). A population cohort study of patients with DPLC was conducted using the extracted data from the Surveillance, Epidemiology, and End Results (SEER) database. Relevant survival variables were identified using the Cox proportional hazard model. Prognostic nomogram was performed and its predictive performance was validated via the modeling and validating cohort data. Additionally, propensity score matching (PSM) was also applied to evaluate whether surgery affected the OS of this study population. 5411 eligible DPLC patients were included in this study cohort, with 41.0% of 3-year OS rate and 27.7% of 5-year OS rate. Age, sex, race, grade, stage, lymph node (LN) metastasis, histological type, primary site, and surgery were considered to be prognostic factors of OS. The C-indexes of the established nomogram were 0.70 (95% CI (0.69, 0.71)) in the modeling group and 0.70 (95% CI (0.68, 0.72)) in the validation group, which showed an ideal model discrimination ability. AUC and calibration plots of 3- and 5-year OS also proved the good performance of the established nomogram. After 1 : 1 PSM, surgery can potentially reduce the risk of OS (HR = 0.63, 95% CI: 0.56–0.72) of DPLC. The prognostic nomogram with reliable performance was developed to predict 3- and 5-year OS rates, which could assist clinicians to make more reasonable survival prediction for DPLC patients. For patients without absolute surgical contraindications, surgery should be actively considered.

Score for the Overall Survival Probability of Patients With First-Diagnosed Distantly Metastatic Cervical Cancer: A Novel Nomogram-Based Risk Assessment System

Background: Metastatic cervical cancer (mCEC) is the end stage of cervical cancer. This study aimed to establish and validate a nomogram to predict the overall survival (OS) of mCEC patients. Methods: We investigated the Surveillance, Epidemiology, and End Results (SEER) database for mCEC patients diagnosed between 2010 and 2014. Univariate and multivariable Cox analyses was performed to select the clinically important predictors of OS when developing the nomogram. The performance of nomogram was validated with Harrell's concordance index (C-index), calibration curves, receiver operating characteristic curve (ROC), and decision curve analysis (DCA). Results: One thousand two hundred and fifty-two mCEC patients were included and were divided into training (n = 880) and independent validation (n = 372) cohorts. Age, race, pathological type, histology grade, radiotherapy, and chemotherapy were independent predictors of OS and used to develop the nomogram for predicting 1- and 3-year OS. This nomogram had a C-index of 0.753 (95% confidence interval [CI]: 0.780-0.726) and 0.751 (95% CI: 0.794-0.708) in the training and the validation cohorts, respectively. Internal and external calibration curves indicated satisfactory agreement between nomogram prediction and actual survival, and DCA indicated its clinical usefulness. Furthermore, a risk stratification system was established that was able to accurately stratify mCEC patients into three risk subgroups with significantly different prognosis. Conclusions: We constructed the first nomogram and corresponding risk classification system to predict the OS of mCEC patients. These tools showed satisfactory accuracy, and clinical utility, and could aid in patient counseling and individualized clinical decision-making.