Cancer classification

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Traditional Cancer Classification: Histology, Site, and Morphology

Cancer classification has historically relied on the tumor’s histological type, site of origin, morphological grade, and extent of spread. Internationally recognized systems, such as those developed by the World Health Organization (WHO) and the Union for International Cancer Control (UICC), form the basis for cancer diagnosis and management. These systems have evolved to include not only traditional pathological features but also, since 2000, biological and molecular-genetic characteristics, reflecting the growing impact of genomics on cancer classification and treatment decisions 19.

Molecular and Genomic Approaches in Cancer Classification

Gene Expression Profiling and Microarrays

The introduction of DNA microarray technology enabled the simultaneous monitoring of thousands of gene expressions, providing a systematic and objective approach to cancer classification. Gene expression-based methods have been shown to accurately distinguish between cancer types and subtypes, sometimes even discovering new classes without prior biological knowledge. These approaches have improved diagnostic precision and facilitated the identification of cancer-specific marker genes, which are crucial for both diagnosis and targeted therapy 5678.

Multiplatform and Pan-Cancer Molecular Classification

Recent studies have used integrative analyses across multiple genomic and proteomic platforms to classify cancers. These analyses reveal that while some molecular subtypes align closely with tissue of origin, others cut across traditional boundaries, grouping cancers from different tissues into common molecular subtypes. This pan-cancer approach provides additional information for predicting clinical outcomes and can uncover shared therapeutic targets among seemingly distinct cancers .

Machine Learning and Deep Learning in Cancer Classification

Classical and Ensemble Machine Learning Methods

Machine learning (ML) algorithms, including support vector machines, k-nearest neighbors, decision trees, and ensemble methods, have been widely applied to gene expression data for cancer classification. These methods have demonstrated high accuracy in distinguishing between cancer types and subtypes, and ensemble approaches often improve both performance and confidence in classification results 6810.

Deep Learning and Graph Convolutional Neural Networks

Deep learning, particularly convolutional neural networks (CNNs) and graph convolutional neural networks (GCNNs), has further advanced cancer classification. These models can handle complex, high-dimensional gene expression data and have achieved classification accuracies above 94% across multiple cancer types. GCNNs, in particular, can identify cancer-specific marker genes and are robust to tissue-of-origin differences, making them powerful tools for both diagnosis and research 34.

Evolving Pathologic and Molecular Classifications in Clinical Practice

The integration of molecular and genetic information into traditional pathology has led to significant changes in cancer classification, especially for specific cancers like lung and breast cancer. For example, new lung cancer classifications now emphasize molecular testing for actionable mutations, and breast cancer classification increasingly relies on molecular subtypes to guide treatment. These changes ensure that classification systems remain relevant for both clinical care and research, supporting precision medicine approaches 1910.

Conclusion

Cancer classification has transitioned from purely histological and anatomical systems to sophisticated molecular and computational approaches. Integrating gene expression profiling, multiplatform molecular data, and advanced machine learning techniques has greatly improved the accuracy, objectivity, and clinical relevance of cancer classification. These advances support more precise diagnoses, better prognostic predictions, and the development of targeted therapies, ultimately improving patient outcomes 12345678+2 MORE.

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Most relevant research papers on this topic

Cancer Classification at the Crossroads

Cancer classifications are evolving to include biologic and molecular-genetic features, enhancing diagnosis and treatment options in the era of cancer genomics.

Rigorous Journal

2020·

44citations

·A. Carbone

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

A multiplatform analysis of 12 cancer types reveals 11 major subtypes, with some distinct cancer types converging into common subtypes, providing independent information for predicting clinical outcomes.

Highly Cited

2014·

1312citations

·K. Hoadley et al.

Classification of Cancer Types Using Graph Convolutional Neural Networks

Graph convolutional neural network models can accurately predict cancer types or normal tissue based on gene expression profiles, with a 94.7% classification accuracy using cancer-specific markers genes.

Highly Cited

2020·

98citations

·Ricardo Ramirez et al.

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

Machine Learning and Deep Learning algorithms can accurately classify cancer types using gene expression data, with accuracies ranging from 90.6% to 94.43%.

Highly Cited

2020·

75citations

·Reinel Tabares-Soto et al.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Gene expression monitoring by DNA microarrays can effectively discover and predict cancer classes, independent of previous biological knowledge.

Highly Cited

1999·

12173citations

·Todd R. Golub et al.

Gene expression based cancer classification

Ensemble classifiers improve cancer classification performance and confidence by combining multiple base classifiers, reducing single training set influence.

2017·

60citations

·Sara Tarek et al.

Cancer classification using gene expression data

Cancer classification using gene expression data shows promise for improving diagnosis and drug discovery, but requires further research on computational time, accuracy, and biological significance.

Highly Cited

2003·

273citations

·Ying Lu et al.

Classification of human cancer diseases by gene expression profiles

The proposed methodology, combining Information Gain and Standard Genetic Algorithm, effectively classifies human cancer diseases using gene expression profiles, outperforming other machine learning methods.

Highly Cited

2017·

190citations

·Hanaa Salem et al.

New pathologic classification of lung cancer: relevance for clinical practice and clinical trials.

The 2011 IASLC/ATS/ERS lung adenocarcinoma classification significantly changes pathologic diagnosis, focusing on distinguishing squamous cell carcinoma from adenocarcinoma and molecular testing for EGFR mutations and ALK rearrangement.

Rigorous JournalHighly Cited

2013·

536citations

·W. Travis et al.

Breast Cancer Type Classification Using Machine Learning

Support Vector Machines are more accurate and efficient than other machine learning algorithms for accurately classifying breast cancer into triple negative and non-triple negative types using gene expression data.

Rigorous JournalHighly Cited

2021·

153citations

·Jiande Wu et al.

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