Breast cancer prediction and categorization in the molecular era of histologic grade

Lamba, Monika; Munjal, Geetika; Gigras, Yogita; Kumar, Manoj

doi:10.1007/s11042-023-14918-9

Cited by 8 publications

(6 citation statements)

References 45 publications

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“…Growth [8] Mitotic Count Tubular Differentiation data [7]. To express a tumor's antagonistic behaviour, HG aims to combine measurements of cellular differentiation and replicative potential into a composite score.…”

Section: Grade Typesmentioning

confidence: 99%

“…The Nottingham Grading System (NGS) is the utmost extensively used technique for BC tumour grading. The grading system of tumour cells is grounded on a microscopic estimation of cytologic and morphologic characteristics, which also include nuclear pleomorphism, mitotic count, and degree of tubule formation [7] [8] [9].…”

Section: Grade Typesmentioning

confidence: 99%

“…To deal with dimensionality reduction, Gene Selection [7] [15] [17] [28] is a potent method. GeS is utilized to discover an "optimum" subgroup of significant features, therefore the comprehensive accuracy is amplified although the data size is made smaller, and the comprehensibility is enhanced in the case of classification.…”

Section: Ges Techniquementioning

confidence: 99%

“…The HG scheme shown in Table I typically takes the patient's severity into account. These anomalies give clinicians tasks to look for likely targets for the best BC detection and diagnosis [7].…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Gene Selection Technique For Identifying Significant Prognosis Biomarkers In Breast Cancer

Lamba,

Munjal,

Gigras

2024

IJCDS

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One crucial stage in the data preparation procedure for breast cancer classification involves extracting a selection of meaningful genes from microarray gene expression data. This stage is crucial because it discovers genes whose expression patterns can differentiate between different types or stages of breast cancer. Two highly effective algorithms, CONSISTENCY-BFS and CFS-BFS, have been developed for gene selection. These algorithms are designed to identify the genes that are most crucial in distinguishing between different types and stages of breast cancer by analysing large volumes of genetic data. A noteworthy advancement is a refined 2-Stage Gene Selection technique specifically designed for predicting subtypes in breast cancer. The initial phase of the 2-Stage Gene Selection (GeS) approach relies on the CFS-BFS algorithm, which plays a crucial role in effectively eliminating unnecessary, distracting, and redundant genes. The initial filtering process plays a crucial role in simplifying the dataset and identifying the genes that have the highest potential to shed light on the category of breast cancer. The CONSISTENCY-BFS algorithm guarantees that only the most pertinent genes are retained by further refining the gene selection process. This stage is essential for eliminating any remaining uncertainty and enhancing the overall efficiency of the algorithm. This innovative approach represents a significant advancement in the field of bioinformatics as it offers a more accurate and targeted method for selecting genes based on their relevance to breast cancer classification. When the 2-Stage GeS is constructed using Hidden Weight Naive Bayes, remarkably, it yields more precise and dependable outcomes. The indicators that demonstrate positive outcomes encompass recollection, accuracy, f-score, and fallout rankings. The Kaplan-Meier Survival Model was employed to further validate the top four genes, namely E2F3, PSMC3IP, GINS1, and PLAGL2. Presumably, precision therapy will specifically focus on targeting the genes E2F3 and GINS1.

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Section: Grade Typesmentioning

confidence: 99%

Section: Grade Typesmentioning

confidence: 99%

Section: Ges Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two-Stage Gene Selection Technique For Identifying Significant Prognosis Biomarkers In Breast Cancer

Lamba,

Munjal,

Gigras

2024

IJCDS

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“…Cancer ranks second in mortality behind cardiovascular disease. Cancer such as breast, 1 blood 2 and brain 3 have the worst likelihood of being cured among all types of cancer. The number of newly diagnosed instances of brain tumours in 2019 reached 86 000, and about 17 000 individuals lost their lives to this illness in 2019 4 .…”

Section: Introductionmentioning

confidence: 99%

MultiFeNet: Multi‐scale feature scaling in deep neural network for the brain tumour classification in MRI images

Agrawal,

Choudhary,

Shankar

et al. 2023

Int J Imaging Syst Tech

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One of the most fatal and prevalent diseases of the central nervous system is a brain tumour. Different subgrades exist for each type of brain tumour because of the broad variety of brain tumours and tumour pathologies. Manual diagnosis may be error‐prone and time‐consuming, both of which are becoming more challenging as the medical community's workload grows. There is a need for automatic diagnosis. In this study, we have proposed a deep learning model (MultiFeNet) based on a convolutional neural network for the classification of brain tumours. MultiFeNet uses multi‐scale feature scaling for feature extraction in magnetic resonance imaging (MRI) images. Multi‐scaling helps to learn the better feature representation of the MRI image for enhanced model performance. To evaluate the proposed model, 3064 MRI scans of three distinct categories of brain tumours (meningiomas, gliomas and pituitary tumours) were used. The MultiFeNet obtained 96.4% sensitivity, 96.4% F1‐score, 96.4% precision and 96.4% accuracy on the benchmark Figshare dataset. In addition, an ablation study is conducted with the objective of evaluating the role of multi‐scaling in model performance.

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Prediction of Breast Cancer Grade Using Explainable Machine Learning

Lamba,

Munjal,

Gigras

2024

Lecture Notes in Networks and Systems

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Breast cancer prediction and categorization in the molecular era of histologic grade

Cited by 8 publications

References 45 publications

Two-Stage Gene Selection Technique For Identifying Significant Prognosis Biomarkers In Breast Cancer

Two-Stage Gene Selection Technique For Identifying Significant Prognosis Biomarkers In Breast Cancer

MultiFeNet: Multi‐scale feature scaling in deep neural network for the brain tumour classification in MRI images

Prediction of Breast Cancer Grade Using Explainable Machine Learning

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