A nomogram to predict individual prognosis in node-negative breast carcinoma

The Role of Dietary Antioxidants, Food Supplements and Functional Foods for Energy Enhancement in Healthcare Professionals

Healthcare professionals frequently experience significant work overload, which often leads to substantial physical and psychological stress. This stress is closely linked to increased oxidative stress and a corresponding decline in energy levels. This scoping review investigates the potential impact of dietary antioxidants and food supplements in conjunction with diet in controlling these negative effects. Through an analysis of the biochemical pathways involved in oxidative stress and energy metabolism, the paper emphasizes the effectiveness of targeted dietary interventions. Key dietary antioxidants, such as vitamins C and E, polyphenols, and carotenoids, are evaluated for their ability to counteract oxidative stress and enhance energy levels. Additionally, the review assesses various food supplements, including omega-3 fatty acids, coenzyme Q10, and ginseng, and their mechanisms of action in energy enhancement. Practical guidelines for incorporating energy-boost dietary strategies into the routine of healthcare professionals are provided, emphasizing the importance of dietary modifications in reducing oxidative stress and improving overall well-being and performance in high-stress healthcare environments. The review concludes by suggesting directions for future research to validate these findings and to explore new dietary interventions that may further support healthcare professionals under work overload.

The distribution of refraction by age and gender in a non-myopic Chinese children population aged 6-12 years

Background

The Prevalence of myopia is increasing in China. This study aimed to explore the distribution of spherical equivalent (SE) and its association with age, body mass index (BMI), gender in a non-myopic Chinese children population aged 6 to 12 years.

Methods

A total of 6362 students were recruited for ophthalmological investigation. Demographic and myopia related behavioral information was collected. SE value was measured by the Topcon RM-8900 or KR-800autorefractors. Potential independent risk factors were determined with Odds Ratio (OR) and 95% Confidence Interval (CI) by logistic regression analysis. We further constructed the nomogram model to predict future onset of myopia.

Results

Among the study population, 3900 (61.3%) were non-myopic. The prevalence of myopia is 38.0% for boys and 39.5% for girls. The average SE values were 0.50 ± 0.70 D for boys and 0.60 ± 0.80 D for girls. The mean SE values decreased with age, and the value of height and BMI took on a stable trend. Threshold values for myopia varied across age groups and gender. Paternal myopia (OR: 1.22, 95%CI: 1.01–1.48), near-work activities on weekends (2.56, 1.17–5.61), and outdoor activities (0.68, 0.54–0.86) were associated with potential myopic in students.

Conclusion

A series of age-gender based SE threshold values were established to predict myopia in Chinese children aged 6 to 12 years. High risk factors for myopia included paternal myopia, near-work activities on weekends, and outdoor activities. Countermeasures are encouraged to reverse the increasing trend of myopia in children.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-020-01709-1.

Development and validation of nomograms predicting survival in Chinese patients with triple negative breast cancer

Background

Triple negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Nomograms predicting outcomes of TNBC are needed for risk management.

Methods

Nomograms were based on an analysis of 296 non-metastatic TNBC patients treated at Sun Yat-sen Memorial Hospital from 2002 to 2014. The end points were disease-free survival (DFS) and overall survival (OS). Predictive accuracy and discriminative ability were evaluated by concordance index (C-index), area under the curve (AUC) and calibration curve, and compared with the American Joint Committee on Cancer (AJCC) staging system, PREDICT and CancerMath. Models were subjected to bootstrap internal validation and external validation using independent cohorts of 191 patients from the second Xiangya Hospital and Peking University Shenzhen Hospital between 2007 and 2012.

Results

On multivariable analysis of training cohort, independent prognostic factors were stromal tumor-infiltrating lymphocytes (TILs), tumor size, node status, and Ki67 index, which were then selected into the nomograms. The calibration curves for probability of DFS and OS showed optimal agreement between nomogram prediction and actual observation. The C-index of nomograms was significantly higher than that of the seventh and eighth AJCC staging system for predicting DFS (training: 0.743 vs 0.666 (P = 0.003) and 0.664 (P = 0.024); validation: 0.784 vs 0.632 (P = 0.02) and 0.607 (P = 0.002)) and OS (training: 0.791 vs 0.683 (P = 0.004) and 0.677 (P < 0.001); validation: 0.783 vs 0.656 (P = 0.006) and 0.606 (P = 0.001)). Our nomograms had larger AUCs compared with PREDICT and CancerMath. In addition, the nomograms showed good performance in stratifying different risk groups of patients both in the training and validation cohorts.

Conclusion

We have developed novel and practical nomograms that can provide individual prediction of DFS and OS for TNBC based on stromal TILs, tumor size, node status, and Ki67 index. Our nomograms may help clinicians in risk consulting and selection of long term survivors.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5703-4) contains supplementary material, which is available to authorized users.

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care

Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, low or intermediate grade patients. Our objectives were to observe clinical routine use of uPA/PAI-1 and to build a new therapeutic decision tree integrating uPA/PAI-1. Methods We observed the concordance between CT indications proposed by a canonical decision tree representative of French practices (not including uPA/PAI-1) and actual CT prescriptions decided by a medical board which included uPA/PAI-1. We used a method of machine learning for the analysis of concordant and non-concordant CT prescriptions to generate a novel scheme for CT indications. Results We observed a concordance rate of 71% between indications proposed by the canonical decision tree and actual prescriptions. Discrepancies were due to CT contraindications, high tumor grade and uPA/PAI-1 level. Altogether, uPA/PAI-1 were a decisive factor for the final decision in 17% of cases by avoiding CT prescription in two-thirds of cases and inducing CT in other cases. Remarkably, we noted that in routine practice, elevated uPA/PAI-1 levels seem not to be considered as a sufficient indication for CT for N≤3, Ki 67≤30% tumors, but are considered in association with at least one additional marker such as Ki 67>14%, vascular invasion and ER-H score <150. Conclusions This study highlights that in the routine clinical practice uPA/PAI-1 are never used as the sole indication for CT. Combined with other routinely used biomarkers, uPA/PAI-1 present an added value to orientate the therapeutic choice.

Development and Validation of Nomograms for Predicting Overall and Breast Cancer-Specific Survival in Young Women with Breast Cancer: A Population-Based Study

INTRODUCTION: The objective of current study was to develop and validate comprehensive nomograms for predicting the survival of young women with breast cancer. METHODS: Women aged <40 years diagnosed with invasive breast cancer between 1990 and 2010 were selected from the Surveillance, Epidemiology, and End Results database and randomly divided into training (n = 12,465) and validation (n = 12,424) cohorts. A competing-risks model was used to estimate the probability of breast cancer–specific survival (BCSS). We identified and integrated significant prognostic factors for overall survival (OS) and BCSS to construct nomograms. The performance of the nomograms was assessed with respect to calibration, discrimination, and risk group stratification. RESULTS: The entire cohort comprised 24,889 patients. The 5- and 10-year probabilities of breast cancer–specific mortality were 11.6% and 20.5%, respectively. Eight independent prognostic factors for both OS and BCSS were identified and integrated for the construction of the nomograms. The calibration curves showed optimal agreement between the predicted and observed probabilities. The C-indexes of the nomograms in the training cohort were higher than those of the TNM staging system for predicting OS (0.724 vs 0.694; P < .001) and BCSS (0.733 vs 0.702; P < .001). Additionally, significant differences in survival were observed in patients stratified into different risk groups within respective TNM categories. CONCLUSIONS: We developed and validated novel nomograms that can accurately predict OS and BCSS in young women with breast cancer. These nomograms may help clinicians in making decisions on an individualized basis.

Personalized Prognostic Prediction Models for Breast Cancer Recurrence and Survival Incorporating Multidimensional Data

Background

In this study, we developed integrative, personalized prognostic models for breast cancer recurrence and overall survival (OS) that consider receptor subtypes, epidemiological data, quality of life (QoL), and treatment.

Methods

A total of 15 314 women with stage I to III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1997 and 2012 were used to generate prognostic models by Cox regression analysis in a two-stage study. Model performance was assessed by calculating the area under the curve (AUC) and calibration analysis and compared with Nottingham Prognostic Index (NPI) and PREDICT.

Results

Host characteristics were assessed for 10 809 women as the discovery population (median follow-up = 6.09 years, 1144 recurrence and 1627 deaths) and 4505 women as the validation population (median follow-up = 7.95 years, 684 recurrence and 1095 deaths). In addition to the known clinical/pathological variables, the model for recurrence included alcohol consumption while the model for OS included smoking status and physical component summary score. The AUCs for recurrence and OS were 0.813 and 0.810 in the discovery and 0.807 and 0.803 in the validation, respectively, compared with AUCs of 0.761 and 0.753 in discovery and 0.777 and 0.751 in validation for NPI. Our model further showed better calibration compared with PREDICT. We also developed race-specific and receptor subtype-specific models with comparable AUCs. Racial disparity was evident in the distributions of many risk factors and clinical presentation of the disease.

Conclusions

Our integrative prognostic models for breast cancer exhibit high discriminatory accuracy and excellent calibration and are the first to incorporate receptor subtype and epidemiological and QoL data.

Nomograms to estimate long-term overall survival and breast cancer-specific survival of patients with luminal breast cancer

Luminal breast cancer constitutes a group of highly heterogeneous diseases with a sustained high risk of late recurrence. We aimed to develop comprehensive and practical nomograms to better estimate the long-term survival of luminal breast cancer.Patients with luminal breast cancer diagnosed between 1990 and 2006 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into the training (n = 87,867) and validation (n = 88,215) cohorts. The cumulative incidence function (CIF) and a competing-risks model were used to estimate the probability of breast cancer-specific survival (BCSS) and death from other causes. We integrated significant prognostic factors to build nomograms and subjected the nomograms to bootstrap internal validation and to external validation.We screened 176,082 luminal breast cancer cases. The 5- and 10-year probabilities of overall death were 0.089 and 0.202, respectively. The 5- and 10-year probabilities of breast cancer-specific mortality (BCSM) were 0.053 and 0.112, respectively. Nine independent prognostic factors for both OS and BCSS were integrated to construct the nomograms. The calibration curves for the probabilities of 5- and 10-year OS and BCSS showed excellent agreement between the nomogram prediction and actual observation. The C-indexes of the nomograms were high in both internal validation (0.732 for OS and 0.800 for BCSS) and external validation (0.731 for OS and 0.794 for BCSS).We established nomograms that accurately predict OS and BCSS for patients with luminal breast cancer. The nomograms can identify patients with higher risk of late overall mortality and BCSM, helping physicians in facilitating individualized treatment.

Comparison of robustness against missing values of alternative decision tree and multiple logistic regression for predicting clinical data in primary breast cancer

Nomogram based on multiple logistic regression (MLR) is a standard technique for predicting diagnostic and treatment outcomes in medical fields. However, the applicability of MLR to data mining of clinical information is limited. To overcome these issues, we have developed prediction models using ensembles of alternative decision trees (ADTree). Here, we compare the performance of MLR and ADTree models in terms of robustness against missing values. As a case study, we employ datasets including pathological complete response (pCR) of neoadjuvant therapy, one of the most important decision-making factors in the diagnosis and treatment of primary breast cancer. Ensembled ADTree models are more robust against missing values than MLR. Sufficient robustness is attained at low boosting and ensemble number, and is compromised as these numbers increase.