Development and validation of a prognostic nomogram for breast cancer patients who underwent chemoradiotherapy and surgery: a retrospective cohort study based on the SEER database and two Chinese cohorts

There is no strong evidence indicating the optimal treatment for breast cancer (BC) and no specific prognostic model. The aim of this study was to establish nomograms to predict the overall survival (OS) of BC patients receiving chemoradiotherapy and surgery, thereby quantifying survival benefits and improving patient management. A total of 1877 patients with primary nonmetastatic BC who received chemoradiotherapy and surgery from 2010 to 2019 were identified from the Surveillance, Epidemiology and End Results (SEER) database as the training cohort, 804 as the internal validation cohort, and 796 patients from the First Affiliated Hospital of Zhengzhou University (n=324) and Jiaxing Maternal and Child Health Hospital (n=472) as the external validation cohort. Least absolute shrinkage and selection operator (LASSO), univariate, and multivariate Cox regression analyses were performed in the training cohort to determine independent prognostic factors for BC, and a nomogram was constructed to predict 3-year, 5-year, and 8-year OS. The final model incorporated 7 factors that significantly affect OS: race, location, positive regional nodes, T stage, N stage, subtype, and grade. The calibration curves showed good consistency between the predicted survival and actual outcomes. Time-dependent receiver operating characteristic (ROC) curves and the time-dependent area under the curve (AUC) confirmed that the accuracy and clinical usefulness of the constructed nomograms were favorable. Decision curve analysis (DCA) and net reclassification improvement (NRI) also demonstrated that this nomogram was more suitable for clinical use than the 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system and the previous prediction model. In the training cohort and the internal validation cohort, the concordance indices (C-index) of the nomogram for predicting OS (0.723 and 0.649, respectively) were greater than those of the 7th AJCC TNM staging system and the previous prediction model. In addition, based on Kaplan-Meier (K-M) survival curves, the survival differences among different risk stratifications were statistically significant, indicating that our risk model was accurate. In this study, we determined independent prognostic factors for OS in patients with primary nonmetastatic BC treated with chemoradiotherapy and surgery. A new and accurate nomogram for predicting 3-, 5-, and 8-year OS in this patient population was developed and validated for potential clinical applicability.

Four conserved amino acids on human papillomavirus E6 predict clinical high-risk types

Human papillomavirus (HPV) types included in the genus alpha papillomavirus (alpha-HPVs) are subdivided into high- and low-risk HPVs associated with tumorigenicity. According to conventional risk classification, over 30 alpha-HPVs remain unclassified and HPV groups phylogenetically classified using the L1 gene do not exactly correspond to the conventional risk classification groups. Here, we propose a novel cervical lesion progression risk classification strategy. Using four E6 risk distinguishable amino acids (E6-RDAAs), we successfully expanded the conventional classification to encompass alpha-HPVs and resolve discrepancies. We validated our classification system using alpha-HPV-targeted sequence data of 325 cervical swab specimens from participants in Japan. Clinical outcomes significantly correlated with the E6-RDAA classification. Four of five HPV types in the data set that were not conventionally classified (HPV30, 34, 67, and 69) were high-risk according to our classification criteria. This report sheds light on the carcinogenicity of rare genital HPV types using a novel risk classification strategy.

A novel nomogram for predicting long-term heart-disease specific survival among older female primary breast cancer patients that underwent chemotherapy: A real-world data retrospective cohort study

Background

The past decade has witnessed an improvement in survival rates for breast cancer, with significant inroads achieved in diagnosis and treatment approaches. Even though chemotherapy is effective for this patient population, cardiotoxicity remains a major challenge, especially in older people. It has been established that cardiovascular events are a major cause of death in older female primary breast cancer patients that underwent chemotherapy. In the present study, the independent prognostic factors were identified to develop a novel nomogram for predicting long-term heart disease-specific survival (HDSS) and improving patient management.

Method

Older female primary breast cancer patients that underwent chemotherapy from 2010 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. HDSS was the primary endpoint of this study. Univariate and multivariate Cox regression analyses were conducted on the training cohort to identify independent prognostic factors of HDSS and construct a nomogram to predict the 5- and 8-year HDSS. The performance of the constructed nomogram was evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analyses. Finally, a risk classification system was constructed to assist in patient management.

Result

A total of 16,340 patients were included in this study. Multivariate Cox regression analysis identified six independent prognostic factors: age, race, tumor stage, marital status, surgery, and radiotherapy. A nomogram based on these six factors yielded excellent performance, with areas under the curve of the ROC for 5- and 8-year HDSS of 0.759 and 0.727 in the training cohort and 0.718 and 0.747 in the validation cohort. Moreover, the established risk classification system could effectively identify patients at low-, middle-, and high- risk of heart disease-associated death and achieve targeted management.

Conclusion

Independent prognostic factors of HDSS in older female primary breast cancer patients that underwent chemotherapy were determined in this study. A novel nomogram for predicting 5- and 8-year HDSS in this patient population was also established and validated to help physicians during clinical decision-making and screen high-risk patients to improve outcomes.

Analysis of risk factors and construction of a prediction model for short stature in children

BACKGROUND

Short stature in children is an important global health issue. This study aimed to analyze the risk factors associated with short stature and to construct a clinical prediction model and risk classification system for short stature.

METHODS

This cross-sectional study included 12,504 children aged 6-14 years of age from 13 primary and secondary schools in Pingshan District, Shenzhen. A physical examination was performed to measure the height and weight of the children. Questionnaires were used to obtain information about children and their parents, including sex, age, family environment, social environment, maternal conditions during pregnancy, birth and feeding, and lifestyle. The age confounding variable was adjusted through a 1 : 1 propensity score matching (PSM) analysis and 1,076 children were selected for risk factor analysis.

RESULTS

The prevalence of short stature in children aged 6-14 years was 4.3% in the Pingshan District, Shenzhen. The multivariate logistic regression model showed that the influencing factors for short stature were father's height, mother's height, annual family income, father's level of education and parents' concern for their children's height in the future (P < 0.05). Based on the short stature multivariate logistic regression model, a short stature nomogram prediction model was constructed. The area under the ROC curve (AUC) was 0.748, indicating a good degree of discrimination of the nomogram. According to the calibration curve, the Hosmer-Lemesio test value was 0.917, and the model was considered to be accurate. Based on a risk classification system derived from the nomogram prediction model, the total score of the nomogram was 127.5, which is considered the cutoff point to divides all children into low-risk and high-risk groups.

CONCLUSION

This study analyzed the risk factors for short stature in children and constructed a nomogram prediction model and a risk classification system based on these risk factors, as well as providing short stature screening and assessment individually.

Adenosquamous Carcinoma of the Cervix: A Population-Based Analysis

Background

Due to the rarity of adenosquamous carcinoma of the cervix (ASCC), studies on the incidence, prognostic factors, and treatment outcomes of ASCC remain scarce. Therefore, we performed a retrospective population-based study to systematically investigate the characteristics of ASCC patients.

Methods

Patients with a histopathologically confirmed diagnosis of ASCC were enrolled from the Surveillance, Epidemiology, and End Results database between 1975 and 2016. Univariate and multivariate Cox regression analyses were performed to identify the potential predictors of cancer-specific survival (CSS) in patients with ASCC. Selected variables were integrated to establish a predictive nomogram and the predictive performance of the nomogram was estimated using Harrell’s concordance index (C-index), calibration curve, and decision curve analysis (DCA).

Results

A total of 1142 ASCC patients were identified and included in this study and were further randomized into the training and validation cohorts in a 7:3 ratio. The age-adjusted incidence of ASCC declined from 0.19 to 0.09 cases per 100,000 person-years between 2000 and 2017, with an annual percentage change of -4.05% (P<0.05). We identified age, tumor grade, FIGO stage, tumor size, and surgical procedure as independent predictors for CSS in ASCC patients and constructed a nomogram to predict the 3- and 5-year CSS using these prognostic factors. The calibration curve indicated an outstanding consistency between the nomogram prediction and actual observation in both the training and testing cohorts. The C-index was 0.7916 (95% CI: 0.7990-0.8042) and 0.8148 (95% CI: 0.7954-0.8342) for the training and testing cohorts, respectively, indicating an excellent discrimination ability of the nomogram. The DCA showed that the nomogram exhibited more clinical benefits than the FIGO staging system.

Conclusions

We established and validated an accurate predictive nomogram for ASCC patients based on several clinical characteristics. This model might serve as a useful tool for clinicians to estimate the prognosis of ASCC patients.

Serum ferritin and ECOG performance status predict the response and improve the prognostic value of IPSS or IPSS-R in patients with high-risk myelodysplastic syndromes and oligoblastic acute myeloid leukemia treated with 5-azacytidine: a retrospective analysis of the Hellenic national registry of myelodysplastic and hypoplastic syndromes

Background:

5-azacytidine (5-AZA) improves survival of patients with higher-risk myelodysplastic syndromes (MDSs) and oligoblastic acute myeloid leukemia (AML); however, predictive factors for response and outcome have not been consistently studied.

Methods:

This study of the Hellenic MDS Study Group included 687 consecutive patients with higher-risk MDS and oligoblastic AML treated with 5-AZA.

Results:

The International Prognostic Scoring System (IPSS) revised version (IPSS-R), Eastern Cooperative Oncology Group Performance Status (ECOG PS) (0 or 1 versus ⩾2) and baseline serum ferritin (SF) levels > 520 ng/ml were shown to independently predict response to 5-AZA. In the survival analysis, the IPSS and IPSS-R risk classification systems along with the ECOG PS and SF levels > 520 ng/ml proved to be independent prognosticators for overall survival (OS), as well as for leukemia-free survival (LFS). Next, we built new multivariate models for OS and LFS, incorporating only ECOG PS and SF levels besides IPSS or IPSS-R risk classification systems. Thereby, the new modified IPSS and IPSS-R risk classification systems (H-PSS, H-PSS-R) could each discriminate a low, an intermediate and a high-risk patient group regarding OS and LFS. The H-PSS and H-PSS-R proved to be better predictors of OS than their previous counterparts as well as the French prognostic score, while the most powerful OS predictor was the new, H-PSS-R system.

Conclusions:

ECOG PS and SF levels > 520 ng/ml independently predict response to 5-AZA, OS and LFS. Their incorporation in the IPSS and IPSS-R scores enhances these scores’ predictive power in 5-AZA-treated higher-risk MDS and oligoblastic AML patients.

The assessment of different risk classification systems for gastrointestinal stromal tumors (GISTs): the analytic results from the SEER database

BACKGROUND

Although various risk classification systems for GISTs have been proposed, the optimum one remains uncertain. In the present study, we compared the prognostic stratification of different risk classification systems for GIST patients.

METHODS

We reviewed those patients who were pathologically diagnosed with GISTs in the SEER database between 2009 and 2014. All patients were classified into different risk groups according to the NIH criteria, AFIP criteria and AJCC staging system, respectively. The prognostic differences between different risk groups were compared and clinicopathologic features were analyzed.

RESULTS

The prognosis of small intestinal GISTs was not significantly different from that of gastric GISTs. For gastric GIST patients, there was no significant prognostic difference between very low risk and low risk group according to the NIH and AFIP criteria. However, the prognostic stratification for two groups could be improved by the AJCC staging system. For small intestinal GIST patients, the prognostic difference between low risk and intermediate risk group was not stratified properly by the NIH and AFIP criteria. However, the prognostic difference between two groups could reach statistical significance according to the AJCC staging system. Unlike gastric GISTs, tumor size was not identified as an independent factor influencing the prognosis of small intestinal GISTs.

CONCLUSIONS

The AJCC staging system could provide a better prognostic stratification for GIST patients compared with the NIH and AFIP criteria, regardless of gastric or small intestinal tumor. However, primary tumor location and tumor size may be reconsidered and revised in the risk classification system.

Development of a risk grading system to identify patients with acute promyelocytic leukemia at high risk of early death

Background

Early death (ED) rate in acute promyelocytic leukemia (APL) remains high. Some studies have identified prognostic factors capable of predicting ED, whereas no risk rating system for ED has been reported in the literature. In this study, a risk classification system was built to identify subgroup at high risk of ED among patients with APL.

Methods

Totally, 364 consecutive APL patients who received arsenic trioxide as induction therapy were included. Ten baseline clinical characteristics were selected for analysis, and they were de novo/relapse, age, sex, white blood cell count, platelet count, serum fibrinogen, creatinine, uric acid, aspartate aminotransferase, and albumin. Using a training cohort (N=275), a multivariable logistic regression model was constructed, which was internally validated by the bootstrap method and externally validated using an independent cohort (N=89). Based on the model, a risk classification system was designed. Then, all patients were regrouped into de novo (N=285) and relapse (N=79) cohorts and the model and risk classification system were applied to both cohorts.

Results

The constructed model included 8 variables without platelet count and sex. The model had excellent discriminatory ability (optimism-corrected area under the receiver operator characteristic curve=0.816±0.028 in the training cohort and area under the receiver operator characteristic curve=0.798 in the independent cohort) and fit well for both the training and independent data sets (Hosmer-Lemeshow test, P=0.718 and 0.25, respectively). The optimism-corrected calibration slope was 0.817±0.12. The risk classification system could identify a subgroup comprising ~25% of patients at high risk of ED in both the training and independent cohorts (OR=0.140, P<0.001 and OR=0.224, P=0.027, respectively). The risk classification system could effectively identify patient subgroups at high risk of ED in not only de novo but also relapse cohorts (OR=0.233, P<0.001 and OR=0.105, P=0.001, respectively).

Conclusion

All the results highlight the high practical value of the risk classification system.

Novel nomograms for castration-resistant prostate cancer and survival outcome in patients with de novo bone metastatic prostate cancer

OBJECTIVES

To develop nomograms predicting the incidence of castration-resistant prostate cancer (CRPC) and overall survival (OS) for de novo metastatic prostate cancer (PCa).

PATIENTS AND METHODS

Data from 449 patients with de novo metastatic PCa were retrospectively analysed. Patients were randomly divided into a training (n = 314, 70%) and a validation cohort (n = 135, 30%). Predictive factors were selected using a Cox proportional hazards model and were further used for building predictive models. The outcomes were incidence of CRPC and OS.

RESULTS

Predictive factors included: Gleason score (GS), intraductal carcinoma of the prostate (IDC-P), Eastern Cooperative Oncology Group status, and alkaline phosphatase, haemoglobin and prostate-specific antigen levels. IDC-P and GS were the strongest prognosticators for both the incidence of CRPC and OS. Nomograms for predicting CRPC and OS had an internal validated concordance index of 0.762 and 0.723, respectively. Based on the β coefficients of the final model, risk classification systems were constructed. For those with favourable, intermediate and poor prognosis, the median time to CRPC was 62.6, 28.0 and 13.0 months (P < 0.001), respectively; and the median OS was not reached, 55.0 and 33.0 months, respectively (P < 0.001).

CONCLUSIONS

We developed two novel nomograms to predict the incidence of CRPC and OS for patients with de novo metastatic PCa. These tools may assist in physician decision-making and the designing of clinical trials.