Predictor Of Survival In Breast Cancer Research Articles

MSR49 Long-Term Survival Prediction in Early Breast Cancer: A Machine Learning Approach With Random Survival Forest

MSR49 Long-Term Survival Prediction in Early Breast Cancer: A Machine Learning Approach With Random Survival Forest

Triple-negative breast cancer survival prediction: population-based research using the SEER database and an external validation cohort.

Triple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis. We included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis. In the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately. These nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests.

Breast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management. We examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies). The STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient's total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient. Combining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

Triple-negative breast cancer survival prediction using artificial intelligence through integrated analysis of tertiary lymphoid structures and tumor budding.

Triple-negative breast cancer (TNBC) is a highly heterogeneous and clinically aggressive disease. Accumulating evidence indicates that tertiary lymphoid structures (TLSs) and tumor budding (TB) are significantly correlated with the outcomes of patients who have TNBC, but no integrated TLS-TB profile has been established to predict their survival. The objective of this study was to investigate the relationship between the TLS/TB ratio and clinical outcomes of patients with TNBC using artificial intelligence (AI)-based analysis. The infiltration levels of TLSs and TB were evaluated using hematoxylin and eosin staining, immunohistochemistry staining, and AI-based analysis. Various cellular subtypes within TLS were determined by multiplex immunofluorescence. Subsequently, the authors established a nomogram model, conducted calibration curve analyses, and performed decision curve analyses using R software. In both the training and validation cohorts, the antitumor/protumor model established by the authors demonstrated a positive correlation between the TLS/TB index and the overall survival (OS) and relapse-free survival (RFS) of patients with TNBC. Notably, patients who had a high percentage of CD8-positive T cells, CD45RO-positive T cells, or CD20-positive B cells within the TLSs experienced improved OS and RFS. Furthermore, the authors developed a comprehensive TLS-TB profile nomogram based on the TLS/TB index. This novel model outperformed the classical tumor-lymph node-metastasis staging system in predicting the OS and RFS of patients with TNBC. A novel strategy for predicting the prognosis of patients with TNBC was established through integrated AI-based analysis and a machine-learning workflow. The TLS/TB index was identified as an independent prognostic factor for TNBC. This nomogram-based TLS-TB profile would help improve the accuracy of predicting the prognosis of patients who have TNBC.

A prognostic nomogram for predicting breast cancer survival based on mammography and AJCC staging

Rationale and objectivesTo develop a prognostic nomogram using mammography data and AJCC staging to predict breast cancer survival. Materials and methodsA prognostic nomogram was created using data from 1000 women diagnosed with breast cancer at a medical cancer center in Taiwan between 2011 and 2015. The variables included age at diagnosis (≤60 or > 60 years), mammography purpose (screening or diagnostic), mammography modality (digital mammogram or digital breast tomosynthesis), and the 7th American Joint Committee on Cancer (AJCC) stage. The outcome predicted was breast cancer-related mortality. The nomogram utilized Kaplan-Meier analysis for all subsets and Cox proportional hazards regression analysis for prediction. The nomogram's accuracy was internally validated using the concordance index and receiver operating characteristic (ROC) curve analysis, focusing on 3-year and 5-year survival predictions. ResultsParticipants' mean age at breast cancer diagnosis was 54 years (SD = 11.2 years). The 1-year, 3-year, and 5-year overall survival (OS) rates were found to be 99.7%, 95.3%, and 91.4%, respectively. The bootstrap-corrected concordance indices indicated the following: nomogram, 0.807 and AJCC, 0.759. A significant difference was observed between the nomogram's area under the curve (AUC) and the AJCC stage in predicting the probability of 5-year survival (p = 0.005). A nomogram, constructed based on mammography and AJCC, demonstrated excellent calibration through internal validation using bootstrapping. ConclusionThe utilization of a nomogram that incorporates mammography data and the AJCC registry data has been demonstrated to be a reliable predictor of breast cancer survival.

Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model

ObjectivesAnti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy.MethodsA total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy.ResultsDeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation.ConclusionsDeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation.Clinical relevance statementWe built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients.Key Points• DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation.• DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival.• To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.

Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction

Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.

Identification of a glycolysis-related miRNA Signature for Predicting Breast cancer Survival.

Breast cancer (BC) is a common type of cancer and has a poor prognosis. In this study, we collected the mRNA and miRNA expression profiles of BC patients were obtained from The Cancer Genome Atlas (TCGA) to explore a novel prognostic strategy for BC patients using bioinformatics tools. We found that six glycolysis-related miRNAs (GRmiRs, including hsa-mir-1247, hsa-mir148b, hsa-mir-133a-2, has-mir-1307, hsa-mir-195 and hsa-mir-1258) were correlated with prognosis of BC samples. The risk score model was established based on 6 prognosis-associated GRmiRs. The outcome of high risk group was significantly poorer. Cox regression analysis showed that risk score was an independent prognostic factor. Differentially expressed genes identified between high and low risk groups were mainly enriched in inflammation and immune-related signaling pathways. The proportion of infiltration of 12 kinds of immune cells in high and low risk groups were significantly different. Risk score was closely associated with many immune indexes. Multiple DEGRGs and miRNAs were associated with drugs. In conclusion, glycolysis-related miRNA signature effectively predicts BC prognosis.

The expression profiles of signature genes from CD103+LAG3+ tumour-infiltrating lymphocyte subsets predict breast cancer survival.

Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response. Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts. We identified two TIL subsets with high expression of CD103 and LAG3 (CD103+LAG3+), including a CD8+ T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103+LAG3+ TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy. Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy.

Population factors predicting survival in breast cancer: analysis according to the data of the Arkhangelsk Regional Cancer Registry in 2000–2020

Background. Breast cancer (BC) is the leading cancer in the world and in Russia. Epidemiological factors predicting breast cancer survival in Russia have not been studied enough. Aim. To evaluate the factors predicting survival in BC by the data of the Arkhangelsk Regional Cancer Registry in 2000–2020 and the survival during the COVID-19 pandemic Materials and methods. Data on all cases of Вс for 2000–2020 retrieved from the Arkhangelsk Regional Cancer Registry database. Cancer-specific survival was estimated using the Kaplan–Meier and the life tables method. The prognostic value of variables available in Arkhangelsk Regional Cancer Registry was assessed in Cox regression. Results. The final analysis included 9,089 cases of breast cancer. During the observation period, 2723 deaths from breast cancer were registered. The one-, five- and ten-year survival rates in the entire population were 91.6 %, 73.0 % and 64.0 %, respectively. In a multivariate analysis, the risk of death from BC increased by 2.9, 8.2, 21.3 and 6.8 at 2, 3, 4 and unknown stages; 2.1 times with the localization of the tumor in the area of the nipple and areola; by 33 % in ductal cancer compared with lobular cancer, by 1.8 and 2.6 times in those who received palliative treatment and did not receive treatment; by 33 % in women aged 80 years and older; by 22 % for those living in rural areas. In the period 2013–2019, the 5-year cancer-specific survival reached 79.1 %, which is 14 % and 6 % higher than in the periods 2000–2006 and 2007–2012. Survival rates have not declined during the COVID-19 pandemic. Conclusion. The risk of death from BC is increased in advanced stages, ductal morphology, localization in the nipple-areolar region, age over 80 years, and living in rural areas. During the COVID-19 pandemic BC survival did not worsen, but this requires further study.

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.

Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up. This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype. The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals. This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.

An Ensembled Framework for Human Breast Cancer Survivability Prediction Using Deep Learning.

Breast cancer is categorized as an aggressive disease, and it is one of the leading causes of death. Accurate survival predictions for both long-term and short-term survivors, when delivered on time, can help physicians make effective treatment decisions for their patients. Therefore, there is a dire need to design an efficient and rapid computational model for breast cancer prognosis. In this study, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks. Specifically, we design a convolutional neural network (CNN) for clinical modalities, a deep neural network (DNN) for copy number variations (CNV), and a long short-term memory (LSTM) architecture for gene expression modalities to effectively handle multi-dimensional data. The independent models' results are then used for binary classification (long term > 5 years and short term < 5 years) based on survivability using the random forest method. The EBCSP model's successful application outperforms models that utilize a single data modality for prediction and existing benchmarks.

Deep multi-modal fusion network with gated unit for breast cancer survival prediction

Accurate survival prediction is a critical goal in the prognosis of breast cancer patients because it can help physicians make more patient-friendly decisions and further guide appropriate treatment. Breast cancer is often caused by genetic abnormalities, which prompts researchers to consider information such as gene expression and copy number variation in addition to clinical data in their studies. The integration of these multi-modal data can improve the predictive power of models. However, with the highly unbalanced information of breast cancer patient data, it becomes a new challenge for breast cancer patient survival prediction to fully extract the characteristic information of these multi-modal data and to consider the complementarity of this information. To this end, we propose a deep multi-modal fusion network (DMMFN) to predict the five-year survival of breast cancer patients by integrating clinical data, copy number variation data, and gene expression data. The imbalanced dataset is first processed using the oversampling method SMOTE-NC. Then the abstract modal features of the multi-modal data are extracted by the two-layer one-dimensional convolutional neural network and the bi-directional long short-term memory network. Next, the weight coefficients of each modal data are dynamically adjusted using gated multimodal units to obtain fusion features. Finally, the fusion features are fed into the MaxoutMLP classifier to obtain the final prediction results. We conducted experiments on the METABRIC dataset to verify the validity of the multi-modal data and compared it with other methods. The comprehensive performance evaluation shows that DMMFN has better prediction performance.

Cytoplasmic YAP1-mediated ESCRT-III assembly promotes autophagic cell death and is ubiquitinated by NEDD4L in breast cancer.

Nuclear Yes1-associated transcriptional regulator (YAP1) promotes tumor progression. However, the function of cytoplasmic YAP1 in breast cancer cells and its impact on the survival of breast cancer patients remain unclear. Our research aimed to explore the biological function of cytoplasmic YAP1 in breast cancer cells and the possibility of cytoplasmic YAP1 as a predictive marker of breast cancer survival. We constructed cell mutant models, including NLS-YAP15SA (nuclear localized), YAP1S94A (incapable of binding to the TEA domain transcription factor family) and YAP1S127D (cytoplasmic localized), and used Cell Counting Kit-8 (CCK-8) assays, 5-ethynyl-2'-deoxyuridine (EdU) incorporation assays, and Western blotting (WB) analysis to detect cell proliferation and apoptosis. The specific mechanism of cytoplasmic YAP1-mediated endosomal sorting complexes required for transport III (ESCRT-III) assembly was studied by co-immunoprecipitation, immunofluorescence staining, and WB analysis. Epigallocatechin gallate (EGCG) was used to simulate YAP1 retention in the cytoplasm in in vitro and in vivo experiments to study the function of cytoplasmic YAP1. YAP1 binding to NEDD4-like E3 ubiquitin protein ligase (NEDD4L) was identified using mass spectrometry and was verified in vitro. Breast tissue microarrays were used to analyze the relationship between cytoplasmic YAP1 expression and the survival of breast cancer patients. YAP1 was mainly expressed in the cytoplasm in breast cancer cells. Cytoplasmic YAP1 promoted autophagic death of breast cancer cells. Cytoplasmic YAP1 bound to the ESCRT-III complex subunits charged multivesicular body protein 2B (CHMP2B) and vacuolar protein sorting 4 homolog B (VPS4B), promoting assembly of CHMP2B-VPS4B and activating autophagosome formation. EGCG retained YAP1 in the cytoplasm, promoting the assembly of CHMP2B-VPS4B to promote autophagic death of breast cancer cells. YAP1 bound to NEDD4L, and NEDD4L mediated ubiquitination and degradation of YAP1. Breast tissue microarrays revealed that high levels of cytoplasmic YAP1 were beneficial to the survival of breast cancer patients. Cytoplasmic YAP1 mediated autophagic death of breast cancer cells by promoting assembly of the ESCRT-III complex; furthermore, we established a new breast cancer survival prediction model based on cytoplasmic YAP1 expression.

A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer Survival Prediction

Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types of cancer in women and is. Early breast cancer detection and classification are crucial. Early detection considerably increases the likelihood of survival, which motivates us to contribute to different detection techniques from a technical standpoint. Additionally, manual detection requires a lot of time and effort and carries the risk of pathologist error and inaccurate classification. To address these problems, in this study, a hybrid deep learning model that enables decision making based on data from multiple data sources is proposed and used with two different classifiers. By incorporating multi-omics data (clinical data, gene expression data, and copy number alteration data) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, the accuracy of patient survival predictions is expected to be improved relative to prediction utilizing only one modality of data. A convolutional neural network (CNN) architecture is used for feature extraction. LSTM and GRU are used as classifiers. The accuracy achieved by LSTM is 97.0%, and that achieved by GRU is 97.5, while using decision fusion (LSTM and GRU) achieves the best accuracy of 98.0%. The prediction performance assessed using various performance indicators demonstrates that our model outperforms currently used methodologies.

73P Automated detection of typical and atypical mitotic figures for improving survival prediction in breast cancer

73P Automated detection of typical and atypical mitotic figures for improving survival prediction in breast cancer

Proposal of SVM Utility Kernel for Breast Cancer Survival Estimation.

The advancement of medical research in the field of cancer prognosis and diagnosis using various modalities has put oncologists under tremendous stress. The complexity and heterogeneity involved in multiple modalities and their significantly varied clinical outcomes make it difficult to analyze the disease and provide the correct treatment. Breast cancer is the major concern among all cancers worldwide, specifically for females. To help oncologists and cancer patients, research for breast cancer survival estimation has been proposed. It ranges from complex deep neural networks to simple and interpretable architectures. We propose a utility kernel for a support vector machine (SVM) in this article. It is a simple yet powerful function, which performs better than other popular machine learning algorithms and deep neural networks in the task of breast cancer survival prediction using the TCGA-BRCA dataset. This study validates the proposed utility kernel using four different modalities (gene expression, copy number variation, clinical, and histopathological tissue images) and their multi-modal combinations. The SVM based on our utility kernel empirically proves its efficacy by achieving the highest value on various performance measures, whereas advanced deep neural networks fail to train on small and highly imbalanced breast cancer data.

A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, cross validation-based training, testing and performance evaluation. The results obtained are compared in terms of the following classification metrics: Accuracy, Precision, F1 score, True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Area under the Receiver Operating Characteristics curve, Area under the Precision-Recall curve and Mathews Correlation Coefficient. The comparison shows that the proposed feature selection sequence produces better results from all supervised classifiers than all other feature selection sequences considered in the analysis.

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast cancer prediction has been studied by a number of researchers, and is considered as a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models which predicts the outcome in early stages by analyzing history of patient’s data. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR)are used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting(XGBoost). For performance comparison, we employed k-fold cross validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms.

External Validation of the Individualized Prediction of Breast Cancer Survival (IPBS) Model for Estimating Survival after Surgery for Patients with Breast Cancer in Northern Thailand.

The individualized prediction of breast cancer survival (IPBS) model was recently developed. Although the model showed acceptable performance during derivation, its external performance remained unknown. This study aimed to validate the IPBS model using the data of breast cancer patients in Northern Thailand. An external validation study was conducted based on female patients with breast cancer who underwent surgery at Maharaj Nakorn Chiang Mai hospital from 2005 to 2015. Data on IPBS predictors were collected. The endpoints were 5-year overall survival (OS) and disease-free survival (DFS). The model performance was evaluated in terms of discrimination and calibration. Missing data were handled with multiple imputation. Of all 3581 eligible patients, 1868 were included. The 5-year OS and DFS were 85.2% and 81.9%. The IPBS model showed acceptable discrimination: C-statistics 0.706 to 0.728 for OS and 0.675 to 0.689 for DFS at 5 years. However, the IPBS model minimally overestimated both OS and DFS predictions. These overestimations were corrected after model recalibration. In this external validation study, the IPBS model exhibited good discriminative ability. Although it may provide minimal overestimation, recalibrating the model to the local context is a practical solution to improve the model calibration.