Survival estimates stratified by the Nottingham Prognostic Index for early breast cancer: a systematic review and meta-analysis of observational studies

Utility of the CPS + EG score with real-life data in patients with breast cancer undergoing neoadjuvant chemotherapy

Breast cancer is the most common type of cancer in women, and predicting disease progression through CPS and EG scoring is important for treatment and prognosis, especially after neoadjuvant chemotherapy. The present study aimed to evaluate the association between the clinical and pathological stage (CPS) + estrogen receptor status and histologic grade (EG) score and disease-free survival (DFS) and overall survival (OS) in patients with breast cancer undergoing neoadjuvant chemotherapy. Data from 148 patients with breast cancer who were treated with neoadjuvant chemotherapy in the Medical Oncology Clinic of Izmir Tepecik Training and Research Hospital between 2013-2018 were analyzed. The following variables were assessed: Demographic characteristics, tumor size, clinical staging, estrogen receptor status, tumor nuclear grade in biopsy material and postoperative pathological staging. CPS + EG scores were calculated using simultaneous estrogen receptor status and tumor nuclear grade parameters, which were developed using the Neoadjuvant Therapy Outcomes Calculator Software of the MD Anderson Cancer Center. The 5-year OS and DFS rates were evaluated, and the 5-year follow-up of the patients was analyzed. The median follow-up period was 76.5 months, and the median survival time was 104.1 months. The pathological complete response (pCR) rate was 23.6%. Patients with a pCR were revealed to have a significantly higher DFS rate compared with the non-pCR group (P=0.038). A significant decline in DFS was also demonstrated with increasing CPS + EG scores (P<0.001). Moreover, a CPS score of 3-4 (P<0.001) and a CPS + EG score of 3-4-5 (P<0.001) were significantly associated with a worse OS. In conclusion, the relationship between the CPS + EG score and survival is apparent in the real-world data in the present study. As the score increases, both the probability of recovery and OS decrease. Furthermore, the CPS + EG scoring system is an easy, free and accessible method to estimate prognosis.

Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging

Predicting long-term recurrence of disease in breast cancer (BC) patients remains a significant challenge for patients with early stage disease who are at low to intermediate risk of relapse as determined using current clinical tools. Prognostic assays which utilize bulk transcriptomics ignore the spatial context of the cellular material and are, therefore, of limited value in the development of mechanistic models. In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence. A number of deep learning models were employed, with champion models employing two-dimensional and two-dimensional-separable convolutional networks found to have predictive performance of a ROC AUC of approximately 0.64, which compares well to other clinically used prognostic assays in this space. All-digital chemical imaging may therefore provide a label-free platform for histopathological prognosis in breast cancer, opening new horizons for future deployment of these technologies.

Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis

Background

Breast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data.

Methods

A total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluate the predictive performance of the models. In addition, to investigate the sensitivity of different training/testing set radio to model performance, we examined five sets of ratios: 50:50, 60:40, 70:30, 80:20, 90:10.

Results

Among these models, XGBoost demonstrated the highest performance with receiver operating characteristic (ROC) area under the curve (AUC) of 0.813, accuracy of 0.739, sensitivity of 0.815, and specificity of 0.735. Further statistical analysis identified several significant predictors of prognosis, including age, tumor size, lymph node status, and hormone receptor status. The XGBoost model was found to exhibit superior predictive power compared to established prognostic models such as the Nottingham Prognostic Index (NPI) and Predict Breast. Based on the successful performance of the XGBoost model, we developed a prognosis prediction tool specifically designed for breast cancer, providing valuable insights to clinicians, and aiding them in making informed treatment decisions tailored to individual patients.

Conclusions

Our study highlights the potential of machine learning models in accurately predicting prognosis for breast cancer patients, ultimately facilitating personalized treatment strategies. Further research and validation are warranted to fully integrate these models into clinical practice.

A nomogram with Nottingham prognostic index for predicting locoregional recurrence in breast cancer patients

Background

The Nottingham prognostic index (NPI) has been shown to negatively impact survival in breast cancer (BC). However, its ability to predict the locoregional recurrence (LRR) of BC remains still unclear. This study aims to determine whether a higher NPI serves as a significant predictor of LRR in BC.

Methods

In total, 238 patients with BC were included in this analysis, and relevant clinicopathological features were collected. Correlation analysis was performed between NPI scores and clinicopathological characteristics. The optimal nomogram model was determined by Akaike information criterion. The accuracy of the model's predictions was evaluated using receiver operating characteristic curves (ROC curves), calibration curves and goodness of fit tests. The clinical application value was assessed through decision curve analysis.

Results

Six significant variables were identified, including age, body mass index (BMI), TNM stage, NPI, vascular invasion, perineural invasion (P<0.05). Two prediction models, namely a TNM-stage-based model and an NPI-based model, were constructed. The area under the curve (AUC) for the TNM-stage- and NPI-based models were 0.843 (0.785,0.901) and 0.830 (0.766,0.893) in training set and 0.649 (0.520,0.778) and 0.728 (0.610,0.846) in validation set, respectively. Both models exhibited good calibration and goodness of fit. The F-measures were 0.761vs 0.756 and 0.556 vs 0.696, respectively. Clinical decision curve analysis showed that both models provided clinical benefits in evaluating risk judgments based on the nomogram model.

Conclusions

a higher NPI is an independent risk factor for predicting LRR in BC. The nomogram model based on NPI demonstrates good discrimination and calibration, offering potential clinical benefits. Therefore, it merits widespread adoption and application.

External Validation of a Machine Learning Model for Schizophrenia Classification

Background and Objective: Excellent generalizability is the precondition for the widespread practical implementation of machine learning models. In our previous study, we developed the schizophrenia classification model (SZ classifier) to identify potential schizophrenia patients in the Japanese population. The SZ classifier has exhibited impressive performance during internal validation. However, ensuring the robustness and generalizability of the SZ classifier requires external validation across independent sample sets. In this study, we aimed to present an external validation of the SZ classifier using outpatient data. Methods: The SZ classifier was trained by using online survey data, which incorporate demographic, health-related, and social comorbidity features. External validation was conducted using an outpatient sample set which is independent from the sample set during the model development phase. The model performance was assessed based on the sensitivity and misclassification rates for schizophrenia, bipolar disorder, and major depression patients. Results: The SZ classifier demonstrated a sensitivity of 0.75 when applied to schizophrenia patients. The misclassification rates were 59% and 55% for bipolar disorder and major depression patients, respectively. Conclusions: The SZ classifier currently encounters challenges in accurately determining the presence or absence of schizophrenia at the individual level. Prior to widespread practical implementation, enhancements are necessary to bolster the accuracy and diminish the misclassification rates. Despite the current limitations of the model, such as poor specificity for certain psychiatric disorders, there is potential for improvement if including multiple types of psychiatric disorders during model development.

Prediction of the Nottingham prognostic index and molecular subtypes of breast cancer through multimodal magnetic resonance imaging

PURPOSE

To explore the ability of intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and background parenchyma enhancement (BPE) to predict the Nottingham prognostic index (NPI) and molecular subtypes of breast cancer (BC).

MATERIALS AND METHODS

In this study, 93 patients with BC were included, and they all underwent DKI, IVIM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examinations. The corresponding mean kurtosis value (MK), pure diffusion (MD), perfusion fraction (f), pseudo diffusion coefficient (D*), true diffusion coefficient (D), and BPE were measured. We used logistic regression analysis to investigate the relevance between the NPI, molecular subtypes and variables. The diagnostic efficacy was analyzed using receiver operating characteristic curves (ROC).

RESULTS

The MD and D values of the high-level NPI group were significantly lower than those of the low-level NPI group (p < 0.01), and the f value of the high-level NPI group was obviously higher than that of low-level NPI group (p < 0.001). The area under curve (AUC) of the combined model (f + D) was 0.824. Comparing with non-Luminal subtypes, the Luminal subtypes showed obviously lower MK, f and D*, and the AUC of the combined model (MK + f + D*) was 0.785. In comparison to other subtypes, the MK and D* values of triple-negative subtype were higher than other subtypes, and the combined model (MK + D*) represented an AUC of 0.865.

CONCLUSION

The quantitative parameters of DKI and IVIM have vital value in predicting the NPI and molecular subtypes of BC, while BPE could not provide additional information. Besides, these combined models can obviously improve the prediction performance.

Long-Noncoding-RNA HOTAIR Upregulation is Associated with Poor Breast Cancer Outcome: A Systematic Review and Meta Analysis

BACKGROUND

Breast cancer is the most frequent cancer among women worldwide with significant disproportionate mortality rates in developing countries. Although clinical management of breast cancer has been immensely improved, refinement in the prognostic and recurrent markers is still needed. Long non-coding RNAs (lncRNA) HOTAIR has recently been associated with poor outcome and is potentially used as a prognostic marker in breast cancer.

METHODS

We comprehensively reviewed studies evaluating lncRNA HOTAIR in association with overall and disease-free survivals in breast cancers. Systematic searches were performed in Pubmed, ProQuest, ScienceDirect, Scopus, Google Scholar, Semantic Scholar, Springer, Nature, Sage Journals, and Wiley databases using combination keywords "long non-coding RNA," "lncRNA," "HOX transcript antisense RNA," "HOTAIR," "breast can-cer," "carcinoma mammae," "prognosis," and "survival." Risk of bias score was used to assess quality of studies, I2 test was conducted to assess heterogeneity. Meta-analysis was performed to compare HOTAIR expression with breast cancer survival rates using STATA v.17 software.

RESULTS

Of the total 1,504 screened studies, seven studies were included in the meta-analysis involving 533 patients. High expression of HOTAIR was associated with poor survival rates (pooled HR: 1.69; 95%CI: 1.11-2.59; p=0.015), shorter overall survival (OS) (pooled HR: 1.33; 95%CI: 0.78-2.26; p=0.455), poor disease-free survival (DFS) (pooled HR: 2.40; 95%CI: 1.63-3.53; p<0.001), poor distant metastatic-free survival (MFS) (HR: 1.75; 95%CI: 1.13-2.71; p=0.012). In addition, overexpression of HOTAIR was associated with positive lymph node infiltration (pooled OR: 2.38; 95%CI: 0.53-10.69; p=0.26) and ductal type cancer (pooled OR: 3.27; 95%CI: 1.15-9.30; p=0.03).

CONCLUSION

Upregulation of lncRNA HOTAIR is associated with worse DFS aand MFS that can potentially be used as a prognostic marker in breast cancer patients.

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study

OBJECTIVE

To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches.

DESIGN

Population based cohort study.

SETTING

QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers.

PARTICIPANTS

141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020.

MAIN OUTCOME MEASURES

Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility.

RESULTS

During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches.

CONCLUSION

In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

Comparison of the Nottingham Prognostic Index and OncotypeDX© recurrence score in predicting outcome in estrogen receptor positive breast cancer

INTRODUCTION

Traditionally, Nottingham prognostic index (NPI) informed prognosis in patients with estrogen receptor positive, human epidermal growth factor receptor-2 negative, node negative (ER+/HER2-/LN-) breast cancer. At present, OncotypeDX© Recurrence Score (RS) predicts prognosis and response to adjuvant chemotherapy (AC).

AIMS

To compare NPI and RS for estimating prognosis in ER + breast cancer.

METHODS

Consecutive patients with ER+/HER2-/LN- disease were included. Disease-free (DFS) and overall survival (OS) were determined using Kaplan-Meier and Cox regression analyses.

RESULTS

1471 patients met inclusion criteria. The mean follow-up was 110.7months. NPI was calculable for 1382 patients: 19.8% had NPI≤2.4 (291/1471), 33.0% had NPI 2.41-3.4 (486/1471), 30.0% had NPI 3.41-4.4 (441/1471), 10.9% had NPI 4.41-5.4 (160/1471), and 0.3% had NPI>5.4 (4/1471). In total, 329 patients underwent RS (mean RS: 18.7) and 82.1% had RS < 25 (270/329) and 17.9% had RS ≥ 25 (59/329). Using multivariable Cox regression analyses (n = 1382), NPI independently predicted DFS (Hazard ratio (HR): 1.357, 95% confidence interval (CI): 1.140-1.616, P < 0.001) and OS (HR: 1.003, 95% CI: 1.001-1.006, P = 0.024). When performing a focused analysis of those who underwent both NPI and RS (n = 329), neither biomarker predicted DFS or OS. Using Kaplan Meier analyses, NPI category predicted DFS (P = 0.008) and (P = 0.026) OS. Conversely, 21-gene RS group failed to predict DFS (P = 0.187) and OS (P = 0.296).

CONCLUSION

In our focused analysis, neither NPI nor RS predicted survival outcomes. However, in the entire series, NPI independently predicted both DFS and OS. On the 40th anniversary since its derivation, NPI continues to provide accurate prognostication in breast cancer, outperforming RS in the current study.

Systemic Treatment of Breast Cancer. 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

This text is based on the recommendations accepted by the 4th Hungarian Consensus Conference on Breast Cancer, modified based on the international consultation and conference within the frames of the Central-Eastern European Academy of Oncology. The professional guideline primarily reflects the resolutions and recommendations of the current ESMO, NCCN and ABC5, as well as that of the St. Gallen Consensus Conference statements. The recommendations cover classical prognostic factors and certain multigene tests, which play an important role in therapeutic decision-making. From a didactic point of view, the text first addresses early and then locally advanced breast cancer, followed by locoregionally recurrent and metastatic breast cancer. Within these, we discuss each group according to the available therapeutic options. At the end of the recommendations, we summarize the criteria for treatment in certain rare clinical situations.

Blockchain and artificial intelligence technology in e-Health

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

Altona Prognostic Index: A New Prognostic Index for ER-Positive and Her2-Negative Breast Cancer of No Special Type

Simple Summary

Breast cancer is the most common tumor-related cause of death in women in Europe and worldwide. The aim of our retrospective study, including 6654 women, was on the one hand to verify the validity of the worldwide known Nottingham prognostic index (NPI), and on the other hand to create a new model with even more prognostic validity. Our newly developed Altona prognostic index (API) shows significantly superior outcome in calculating progression free survival. In contrast to the NPI, the API considers characteristics such as subtypes of breast cancer, as this disease is heterogenous involving different entities, and patient’s age. Evaluating progression free survival in different subgroups, our study shows that both these prognostic indices should only be applied on a patient collective that is ≤70 years old with first primary, unifocal, unilateral breast cancer that is of no special type (NST), estrogen receptor-positive and Her2-negative to get valid prediction data.

Abstract

Breast cancer is a heterogeneous disease representing a number of different histopathologic and molecular types which should be taken into consideration if prognostic or predictive models are to be developed. The aim of the present study was to demonstrate the validity of the long-known Nottingham prognostic index (NPI) in a large retrospective study (n = 6654 women with a first primary unilateral and unifocal invasive breast cancer diagnosed and treated between April 1996 and October 2018; median follow-up time of breast cancer cases was 15.5 years [14.9–16.8]) from a single pathological institution. Furthermore, it was intended to develop an even superior risk stratification model considering an additional variable, namely the patient’s age at the time of diagnosis. Heterogeneity of these cases was addressed by focusing on estrogen receptor-positive as well as Her2-negative cases and taking the WHO-defined different tumor types into account. Calculating progression free survival Cox-regression and CART-analysis revealed significantly superior iAUC as well as concordance values in comparison to the NPI based stratification, leading to an alternative, namely the Altona prognostic index (API). The importance of the histopathological tumor type was corroborated by the fact that when calculated separately and in contrast to the most frequent so-called “No Special Type” (NST) carcinomas, neither NPI nor API could show valid prognostic stratification.

Staging computerized tomography before delayed breast reconstruction could alter the management plan

BACKGROUND

Delayed breast reconstruction (DBR) comprises a significant proportion of breast reconstruction practice post completion of breast cancer treatment. The tumour's biology, staging, time constraints, ongoing treatment, and patient and surgeon's preference influence the decision to pursue DBR. There are no guidelines for assessing the oncological status before DBR in otherwise asymptomatic patients, particularly in those with a higher risk of recurrence. The purpose of this study was to identify the cohort of patients who could potentially benefit from staging CT scan before DBR regardless of the reconstructive modality and its impact on the overall management.

MATERIAL AND METHODS

A retrospective review on 207 consecutive patients, who underwent staging CT scan before DBR in the period between 2009 and 2019 was performed. The CT scan findings were correlated with the breast prognostication scoring model (Nottingham Prognostic Index [NPI]) as an indicator factor for staging reasons.

RESULTS

Incidental findings were reported in 34% (71/207) of the reviewed CT scans (incidentaloma group). There was no statistical significance in the NPI scores between non incidentaloma and incidentaloma groups. However, 5.7% (12/207) had their DBR procedure cancelled or the surgical plan altered.

CONCLUSION

The patients with moderate to poor prognosis (NPI score 3.4 and above) could benefit from CT staging scan before DBR. This scan could detect adverse prognostic features precluding major surgery, which saves patients from unnecessary surgical risks and discomfort, and direct them towards the relevant management pathway.

Prognostic value of the 6-gene OncoMasTR test in hormone receptor-positive HER2-negative early-stage breast cancer: Comparative analysis with standard clinicopathological factors

AIM

The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade.

METHODS

Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones.

RESULTS

OMm and OM (both with likelihood ratio statistic [LRS] P < 0.001; C indexes = 0.84 and 0.85, respectively) were more prognostic for DRFS and provided significant additional prognostic information to all other assessment tools/factors assessed (all LRS P ≤ 0.002). In addition, the OM correctly classified more patients with distant recurrences (DRs) into the high-risk category than other risk classification tools. Similar results were observed for IDFS.

DISCUSSION

Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions.