Types of cancer classification
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Cancer Classification: Histological, Molecular, and Machine Learning Approaches
Histological and Tissue-of-Origin Cancer Classification
Traditionally, cancer classification has relied on the tumor’s site of origin, histological type, morphologic grade, and the extent of spread in the body. The World Health Organization (WHO) and Union for International Cancer Control (UICC) have established internationally accepted systems based on these criteria, forming the foundation for cancer diagnosis and management. These systems categorize cancers by their tissue of origin (e.g., breast, lung, colon) and further by histological subtypes (e.g., ductal, lobular, squamous) .
Molecular and Genomic Cancer Classification
With advances in genomics, cancer classification has expanded to include molecular and genetic features. Molecular profiling can reveal subtypes within a single tissue type and identify shared genomic signatures across different tissues. For example, multiplatform analyses have shown that some cancers from different organs can converge into common molecular subtypes, while others split into distinct groups despite similar tissue origins. This molecular classification provides additional information for predicting clinical outcomes and guiding therapy, sometimes independent of traditional histological categories 37.
In breast cancer, molecular classification (such as luminal, basal-like, and HER2+) is now used alongside histological typing. Some special histological types of breast cancer are found to be molecularly homogeneous, while others, like invasive ductal and lobular carcinomas, contain all molecular subtypes. This refined classification helps in more accurate prognosis and treatment selection .
Cancer Classification Using Gene Expression and Machine Learning
Gene expression profiling, enabled by technologies like DNA microarrays and RNA sequencing, has become a powerful tool for cancer classification. Machine learning (ML) and deep learning (DL) algorithms can analyze these large datasets to classify tumors into multiple types with high accuracy. Studies have shown that ML/DL models, such as support vector machines, convolutional neural networks, and ensemble methods, can distinguish between various cancer types (e.g., breast, lung, colorectal, thyroid, ovarian) and even subtypes within a cancer type 4568.
Graph convolutional neural networks (GCNNs) and other deep learning models have been used to classify up to 33 cancer types based on gene expression, achieving high accuracy and identifying cancer-specific marker genes. These approaches can distinguish cancer types regardless of the tissue of origin, highlighting the power of molecular data for classification 258.
Imaging-Based Cancer Classification
Deep learning models are also used to classify cancer types from medical images such as CT, MRI, and histopathological slides. Pre-trained convolutional neural networks (CNNs) can be adapted to detect and classify multiple cancer types (e.g., lung, brain, breast, cervical) from imaging data. These models can improve diagnostic accuracy and speed, especially when combined with techniques like transfer learning and learning without forgetting 110.
Subtype and Grade Classification Within Cancer Types
Within a single cancer type, further classification into subtypes and grades is crucial for prognosis and treatment. For example, breast cancer can be classified into benign and malignant types, various subtypes, and different grades using deep learning on histopathological images. Accurate subtype and grade identification supports personalized treatment strategies and improves patient outcomes 910.
Conclusion
Cancer classification has evolved from traditional histological and tissue-of-origin systems to include molecular, genetic, and imaging-based approaches. Modern classification methods use a combination of histopathology, molecular profiling, and advanced machine learning techniques to accurately identify cancer types, subtypes, and grades. These advances enable more precise diagnosis, better prognostication, and tailored therapies, ultimately improving patient care and outcomes 12345678+2 MORE.
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