Tumor classification and types
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Tumor Classification: Histology, Molecular Markers, and Machine Learning
Tumor classification is essential for accurate diagnosis and effective treatment planning. Traditionally, tumors have been classified based on their histological appearance, site of origin, and morphologic grade. However, advances in molecular genetics and machine learning have significantly improved the precision and utility of tumor classification systems .
Histological and Molecular Classification of Tumors
The World Health Organization (WHO) classification system remains the global standard for tumor classification, incorporating histological features, site of origin, and, increasingly, molecular and genetic markers . For example, the 2021 WHO classification of central nervous system (CNS) tumors now integrates molecular pathogenesis with histopathology, leading to more biologically and molecularly defined tumor entities. In adult-type diffuse gliomas, this has resulted in three main types: astrocytoma (IDH mutant), oligodendroglioma (IDH mutant and 1p/19q codeleted), and glioblastoma (IDH wild type), with classification driven by IDH mutation status . Similarly, the 2015 WHO classification of lung tumors emphasizes immunohistochemistry and genetic studies, such as molecular testing for targeted therapies, and redefines several tumor subtypes based on these advances .
Tumor Types and Subtypes
Tumors are broadly categorized by tissue of origin (e.g., brain, lung, breast) and further subdivided based on histological and molecular features. For brain tumors, common types in children include pilocytic astrocytomas, ependymomas, and medulloblastomas, while in adults, diffuse astrocytic tumors, oligodendrogliomas, and meningiomas are prevalent . The classification of tumors within and across tissues has revealed that some molecular subtypes span multiple tissue origins, as seen in multiplatform analyses of various cancers. For instance, certain lung, head and neck, and bladder cancers share molecular features, forming unified subtypes that can inform prognosis and therapy .
Machine Learning and Deep Learning in Tumor Classification
Recent years have seen the rise of machine learning (ML) and deep learning (DL) approaches for tumor classification, leveraging genomic and imaging data for improved accuracy. Deep-learning models, such as ensemble-based methods using gene panel sequencing, have achieved high accuracy in predicting tumor types, even rivaling whole genome sequencing-based models . Graph convolutional neural networks (GCNNs) and convolutional neural networks (CNNs) have also demonstrated high accuracy (often above 94%) in classifying multiple cancer types using gene expression profiles or imaging data 2569. These models can identify cancer-specific marker genes and are adaptable for multi-class classification tasks, such as distinguishing between glioma, meningioma, and pituitary tumors in brain MRI scans 69.
Implications for Diagnosis and Treatment
The integration of molecular and genetic data into tumor classification systems has improved diagnostic accuracy, enabled more precise prognostication, and facilitated the development of targeted therapies 478. Machine learning models further enhance clinical decision support by providing rapid, accurate tumor type predictions, which are especially valuable for cancers of unknown primary origin or cases with ambiguous histology 15.
Conclusion
Tumor classification has evolved from purely histological systems to sophisticated frameworks that incorporate molecular, genetic, and computational insights. These advances enable more accurate diagnosis, better prediction of clinical outcomes, and more personalized treatment strategies, ultimately improving patient care across a wide range of tumor types 12345678+2 MORE.
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