Nomograms for prediction of overall and cancer-specific survival in young breast cancer

Secretory breast carcinoma: a multicenter clinicopathologic study of 80 cases with emphasis on prognostic analysis and chemotherapy benefit

PURPOSE

To investigate clinicopathologic characteristics and prognosis in secretory breast carcinoma (SBC) and to determine chemotherapy benefits stratified by different subgroups.

METHODS

SBCs and triple-negative invasive ductal carcinoma patients (TN-IDCs) were enrolled from three cancer centers between January 2011 and December 2020. SBCs were further divided into two subgroups: those with triple negativity (TN-SBCs) and those without (non-TN-SBCs). Clinicopathologic characteristics were thoroughly compared among the three subgroups associated with triple negativity. Kaplan-Meier estimates and Cox proportional hazard models were performed for survival analysis.

RESULTS

A total of 80 SBCs and 310 TN-IDCs were included in the study. The TN-SBC subgroup consisted of 35 individuals (43.75%) with mild clinical behaviors and a satisfying prognosis in comparison to non-TN-SBCs and TN-IDCs. In SBCs, N stage (N1 vs. N0: HR = 11.176, 95% CI 0.843-148.132, p = 0.067; N2-3 vs. N0: HR = 30.409, 95% CI 1.378-671.169, p = 0.031), LNR (HR = 23.894, 95% CI 1.614-353.835, p = 0.021), and histological grade (HR = 28.634, 95% CI 2.745-298.703, p = 0.005) were significantly correlated with disease-free survival (DFS). Patients in high LNR group receiving chemotherapy achieved a prolonged DFS (p = 0.025), while chemotherapy did not confer a survival benefit in TN-SBCs of our interest (p = 0.12).

CONCLUSION

TN-SBC is a unique entity with low malignant potential. Advanced N stage, high LNR, and advanced histological grade are adverse determinants of DFS in SBC. Adjuvant chemotherapy provides superior DFS in high LNR SBCs rather than TN-SBCs, hence it is recommended for high LNR SBCs.

Prediction model for survival of younger patients with breast cancer using the breast cancer public staging database

Breast cancer (BC) is a major contributor to female mortality worldwide, particularly in young women with aggressive tumors. Despite the need for accurate prognosis in this demographic, existing studies primarily focus on broader age groups, often using the SEER database, which has limitations in variable selection. This study aimed to develop an ML-based model to predict survival outcomes in young BC patients using the BC public staging database. A total of 3,401 patients with BC were included in the study. Patients were categorized as younger (n = 1574) and older (n = 1827). We applied several survival models-Random Survival Forest, Gradient Boosting Survival, Extra Survival Trees (EST), and penalized Cox models (Lasso and ElasticNet)-to compare mortality characteristics. The EST model outperformed others in predicting mortality for both age groups. Older patients exhibited a higher prevalence of comorbidities compared to younger patients. Tumor stage was the primary variable used to train the model for mortality prediction in both groups. COPD was a significant variable only in younger patients with BC. Other variables exhibited varying degrees of consistency in each group. These findings can help identify high-risk young female patients with BC who require aggressive treatment by predicting the risk of mortality.

Nomograms to predict the long-term prognosis for non-metastatic invasive lobular breast carcinoma: a population-based study

Invasive lobular breast carcinoma (ILC) is one potential subset that "clinicopathologic features" can conflict with "long-term outcome" and the optimal management strategy is unknown in such discordant situations. The present study aims to predict the long-term, overall survival (OS) and cancer-specific survival (CSS) of ILC. The clinical information of patients with non-metastatic ILC was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2020. A total of 31451 patients were enrolled and divided into the training cohort (n=22,017) and validation cohort (n=9434). The last follow-up was December, 31, 2020 and the median follow-up period was 99 months (1-203). Age, marriage, estrogen (ER) status, progesterone (PR) status, grade, tumor size, lymph node ratio (LNR) and combined summary (CS) stage were prognostic factors for both OS and CSS of ILC, whereas chemotherapy and radiation were independent protect factors for OS. The nomograms exhibited satisfactory discriminative ability. For the training and validation cohorts, the C-index of the OS nomogram was 0.765 (95% CI 0.762-0.768) and 0.757 (95% CI 0.747-0.767), and the C-index of the CSS nomogram were 0.812 (95% CI 0.804-0.820) and 0.813 (95% CI 0.799-0.827), respectively. Additionally, decision curve analysis (DCA) demonstrated that the nomograms had superior predictive performance than traditional American Joint Committee on Cancer (AJCC) TNM stage. The novel nomograms to predict long-term prognosis based on LNR are reliable tools to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments for patients with ILC. Our findings should aid public health prevention strategies to reduce cancer burden. We provide two R/Shiny apps ( https://ilc-survival2024.shinyapps.io/osnomogram/ ; https://ilc-survival2024.shinyapps.io/cssnomogram/ ) to visualize findings.

A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care

PURPOSE

We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings.

METHODS

The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS) instrument.

RESULTS

We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63).

CONCLUSIONS

There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing.

IMPLICATIONS FOR CANCER SURVIVORS

There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.

Progress of the application clinical prediction model in polycystic ovary syndrome

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

New Personal Model for Forecasting the Outcome of Patients with Histological Grade III-IV Colorectal Cancer Based on Regional Lymph Nodes

Background

Metastases at regional lymph nodes could easily occur in patients with high-histological-grade colorectal cancer (CRC). However, few models were built on the basis of lymph nodes to predict the outcome of patients with histological grades III-IV CRC.

Methods

Data in the Surveillance, Epidemiology, and End Results databases were used. Univariate and multivariate analyses were performed. A personalized prediction model was built in accordance with the results of the analyses. A nomogram was tested in two datasets and assessed using a calibration curve, a consistency index (C-index), and an area under the curve (AUC).

Results

A total of 14,039 cases were obtained from the database. They were separated into two groups (9828 cases for constructing the model and 4211 cases for validation). Logistic and Cox regression analyses were then conducted. Factors such as log odds of positive lymph nodes (LODDS) were utilized. Then, a personalized prediction model was established. The C-index in the construction and validation groups was 0.770. The 1-, 3-, and 5-year AUCs were 0793, 0.828, and 0.830 in the construction group, respectively, and 0.796, 0.833, and 0.832 in the validation group, respectively. The calibration curves showed well consistency in the 1-, 3- and 5-year OS between prediction and reality in both groups.

Conclusion

The nomogram built based on LODDS exhibited considerable reliability and accuracy.

Prognostic Factors and a Model for Occult Breast Cancer: A Population-Based Cohort Study

Occult breast cancer (OBC) is a special type of breast cancer of an unknown primary origin. Early stage OBC is treated as stage II−III breast cancer. Currently, there are no models for predicting the survival outcomes. Hence, we aimed to evaluate the role of the positive lymph node ratio (PLNR) in OBC and further establish and validate a prognostic nomogram. Patients with stage T0N+M0 breast cancer were enrolled from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analyses were used to evaluate the effects of prognostic factors on breast-cancer-specific survival (BCSS), and a nomogram was established and validated for OBC. Overall, 843 patients were included, and the 5-year BCSS rate was 92.4%. Patients with a PLNR < 0.54 had better BCSS rates than those with a PLNR ≥ 0.54. The nomogram combined clinicopathological parameters, including the PLNR, pN stage, and estrogen receptor status, and showed a higher accuracy than the TNM staging system in predicting the BCSS. The patients could be stratified into different risk groups based on their prognostic scores. Patients in the low-risk subgroup showed an improved BCSS compared those in the high-risk subgroup. In conclusion, the PLNR is an independent prognostic factor for OBC. The PLNR-based nomogram has a better predictive ability than the TNM staging system and could be of great value for the treatment of OBC and prediction of its prognosis.

Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor

Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies.

Prognostic Nomogram for Postoperative Hypopharyngeal Squamous Cell Carcinoma to Assist Decision Making for Adjuvant Chemotherapy

We aimed to investigate the effect of lymph node parameters on postoperative hypopharyngeal squamous cell carcinoma (HSCC) and to establish a nomogram to predict its prognosis and assist in adjuvant chemotherapy decisions. A retrospective analysis of postoperative HSCC in the Surveillance, Epidemiology, and End Results database (2004-2019) was performed. Cutoff points for continuous variables were determined by X-tile software. Univariate and multivariate analyses were performed to identify prognostic factors on overall survival (OS), and these variables were used to construct a nomogram. The nomogram's accuracy was internally validated using concordance index, area under the curve, calibration plot, and decision curve analyses. Furthermore, the value of chemotherapy in each risk subgroup was assessed separately based on individualized scores from the nomogram. In total, 404 patients were eligible for analysis, and the median OS was 39 months. Age, origin, primary site, T stage, number of lymph nodes examined, lymph node ratio, and radiotherapy were identified as prognostic factors for OS and incorporated into the nomogram. In both the training and validation cohorts, favorable performance was exhibited compared with the other stage systems, and patients could be classified into low-, intermediate-, and high-risk subgroups. Chemotherapy significantly improved the OS in the high-risk subgroup, whereas chemotherapy did not confer a survival benefit in the low- or intermediate-risk groups. The lymph node parameter-based nomogram model can better stratify the prognosis of HSCC patients and screen out patients who would benefit from chemotherapy, suggesting that the model could be used as a reference for clinical decision making and to avoid overtreatment.

Survival Nomogram for Young Breast Cancer Patients Based on the SEER Database and an External Validation Cohort

Background

Young breast cancer (YBC) patients are more prone to lymph node metastasis than other age groups. Our study aimed to investigate the predictive value of lymph node ratio (LNR) in YBC patients and create a nomogram to predict overall survival (OS), thus helping clinical diagnosis and treatment.

Methods

Patients diagnosed with YBC between January 2010 and December 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into a training set and an internal validation set with a ratio of 7:3. An independent cohort from our hospital was used for external validation. Univariate and least absolute shrinkage and selection operator (LASSO) regression were used to identify the significant factors associated with prognosis, which were used to create a nomogram for predicting 3- and 5-year OS.

Results

We selected seven survival predictors (tumor grade, T-stage, N-stage, LNR, ER status, PR status, HER2 status) for nomogram construction. The C-indexes in the training set, the internal validation set, and the external validation set were 0.775, 0.778 and 0.817, respectively. The nomogram model was well calibrated, and the time-dependent ROC curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more precisely differentiate risk subgroups and improve the discrimination of YBC prognosis.

Conclusions

LNR is a strong predictor of OS in YBC patients. The novel nomogram based on LNR is a reliable tool to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments.

The Survival Effect of Radiotherapy on Stage IIB/III Pancreatic Cancer Undergone Surgery in Different Age and Tumor Site Groups: A Propensity Scores Matching Analysis Based on SEER Database

Background

It remains controversial whether radiotherapy (RT) improves survival in patients with stage IIB/III PDAC. A growing number of studies have found that patients’ age at diagnosis and tumor site not only affect prognosis, but also may lead to different treatment responses. Therefore, the purpose of this study was to verify whether the survival effect of radiotherapy in patients with stage IIB/III PDAC varies across age and tumor site groups.

Methods

The target population was selected from PDAC patients undergone surgery in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. This study performed the Pearson’s chi-square test, Cox regression analysis, Kaplan-Meier (K-M) method, and focused on propensity frequency matching analysis.

Results

Neither neoadjuvant radiotherapy (nRT) nor adjuvant radiotherapy (aRT) patient group had probably improved survival among early-onset patients. For middle-aged patients, nRT seemed to fail to extend overall survival (OS), while aRT might improve the OS. Plus, both nRT and aRT were associated with improved survival in elderly patients. The aRT might be related with survival benefits in patients with pancreatic head cancer, while nRT was not. And RT in patients with PDAC at other sites did not appear to provide a survival benefit.

Conclusion

Carefully selected data from the SEER database suggested that age and tumor location may be the reference factors to guide the selection of RT for patients with stage IIB/III PDAC. These findings are likely to contribute to the development of personalized treatment for patients with stage IIB/III PDAC.

The Survival Effect of Radiotherapy on Stage II/III Rectal Cancer in Different Age Groups: Formulating Radiotherapy Decision-Making Based on Age

Introduction

Total mesorectal excision (TME), chemotherapy (CT), and radiotherapy (RT) are usually integrated into the comprehensive treatment of stage II/III rectal cancer (RC). Neoadjuvant radiotherapy (nRT) has become the standard treatment for stage II/III RC patients to help reduce the size of a tumor or kill cancer cells that have spread. Adjuvant RT is delivered after the resection to destroy remaining cancer cells and used mainly in stage II/III RC patients who have not received preoperative radiotherapy, such as those who suffered from a bowel obstruction before surgery. It is controversial whether radiotherapy can improve the survival of stage II/III RC patients. An increasing number of studies have reported that rectal cancer exhibited mismatched biology, epidemiology, and therapeutic response to current treatment strategy in different age groups. It is necessary to investigate whether radiotherapy exhibits disparate effects in different age groups of patients with stage II/III RC.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) Program was extracted to identify stage II/III RC diagnosed in the periods of 2004-2016. The statistical methods included Pearson's chi-square test, log-rank test, Cox regression model, and propensity score matching.

Results

Neoadjuvant radiotherapy (nRT) cannot improve the prognosis, and postoperative RT may even reduce the survival time for early onset stage II/III RC. Postoperative RT was not able to improve the overall survival (OS), while nRT may provide limited survival improvement for middle-aged stage II/III RC patients. In addition, radiotherapy can significantly improve the prognosis for elderly stage II/III RC.

Conclusions

This study indicated the inconsistent survival effect of radiotherapy on stage II/III rectal cancer patients in different age groups. Hence, we formulated a novel flow chart of radiotherapy decision-making based on age in stage II/III RC patients.

[Factors affecting survival prognosis of advanced gastric cancer and establishment of a nomogram predictive model]

OBJECTIVE

To explore the factors affecting the survival of patients with advanced gastric cancer and establish a reliable predictive model of the patients' survival outcomes.

OBJECTIVE

We retrospectively collected the clinical data from patients with advanced gastric cancer treated in our department between January, 2015 and December, 2019. Univariate survival analysis was carried out using Kaplan-Meier method followed by multivariate Cox regression analysis to identify the factors associated with the survival outcomes of the patients. The R package was used to generate the survival rates, and a nomogram was established based on the results of multivariate analysis. The calibration curves and C-index were calculated to determine the predictive and discriminatory power of the model. The performance of the nomogram model for predicting the survival outcomes of the patients was evaluated using receiver- operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

OBJECTIVE

Univariate analysis showed that the number of metastatic sites, the number of treatment lines received, disease control rate (DCR) and progression-free survival (PFS) time following first-line treatment, and surgical treatment in first-line treatment were significantly correlated with the survival time of the patients (P < 0.05). Multivariate Cox regression analysis showed that surgical treatment, number of treatment lines, PFS time following first-line treatment and peritoneal metastasis, as independent prognostic factors, were significantly correlated with the patients' survival (P < 0.05). The C-index of the nomogram was 0.785 (95%CI: 0.744-0.826) for overall survival of the patients. The calibration curves showed that the actual survival rate of the patients was consistent with the predicted survival rate. The time-dependent AUC and DCA demonstrated that the nomogram had a good performance for predicting the survival outcomes of patients with advanced gastric cancer.

OBJECTIVE

Peritoneal metastasis is associated with s shorter overall survival time of patients with advanced gastric cancer, while a PFS time following first-line treatment of more than 7.0 months and third-line and posterior-line treatments are related with a longer survival time. Systematic treatment including elective surgery can improve the survival outcomes of the patients. The nomogram we established provides a reliable prognostic model for evaluating the prognosis of patients with advanced gastric cancer.

Construction and Validation of Nomograms Predicting Survival in Triple-Negative Breast Cancer Patients of Childbearing Age

Background

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer with poorest clinical outcomes. Patients of childbearing age have a higher probability of TNBC diagnosis, with more demands on maintenance and restoration of physical and psychosocial function. This study aimed to design effective and comprehensive nomograms to predict survival in these patients.

Methods

We used the SEER database to identify patients with TNBC aged between 18 and 45 and randomly classified these patients into a training (n=2,296) and a validation (n=2,297) cohort. Nomograms for estimating overall survival (OS) and breast cancer-specific survival (BCSS) were generated based on multivariate Cox proportional hazards models and competing-risk models in the training cohort. The performances of the nomograms were quantified in the validation cohort using calibration curves, time-dependent receiver operating characteristic (ROC) curves and Harrell’s concordance index (C-index).

Results

A total of 4,593 TNBC patients of childbearing age were enrolled. Four prognostic factors for OS and six for BCSS were identified and incorporated to construct nomograms. In the validation cohort, calibration curves showed excellent agreement between nomogram-predicted and actual survival data. The nomograms also achieved relatively high Harrell’s C-indexes and areas under the time-dependent ROC curves for estimating OS and BCSS in both training and validation cohorts.

Conclusions

Independent prognostic factors were identified, and used to develop nomograms to predict OS and BCSS in childbearing-age patients with TNBC. These models could enable individualized risk estimation and risk-adapted treatment for these patients.