Histopathology is the study of diseased tissues using a microscope. It is a crucial tool for diagnosing cancer and other diseases, as well as for predicting their outcomes and selecting the best treatments. However, histopathology is also a challenging and time-consuming task that requires highly skilled and experienced pathologists. Moreover, histopathology is often subjective and prone to variability and errors, which can affect the accuracy and reliability of the results.
Fortunately, technologies such as artificial intelligence (AI), digital pathology, and molecular pathology can help improve histopathology in various ways. These technologies can enhance the quality, efficiency, and innovation of histopathology, as well as enable new applications and discoveries in oncology and beyond.
AI is a branch of computer science that aims to create machines or systems that can perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI can be applied to histopathology using deep learning, which is a subset of machine learning that uses multiple layers of artificial neural networks to learn from large amounts of data. Deep learning can be used to analyze histopathology images and extract features that are relevant for diagnosis, prognosis, and treatment selection. For example, deep learning can help detect and classify cancer cells, measure tumor size and grade, identify biomarkers and mutations, and predict survival and response to therapy1.
Digital pathology is the process of converting glass slides into high-resolution digital images that can be viewed, stored, shared, and analyzed on a computer or mobile device. Digital pathology can improve histopathology by increasing its accessibility, scalability, and standardization. For instance, digital pathology can enable remote consultation and collaboration among pathologists across different locations. It can also facilitate the integration of histopathology with other data sources, such as genomic, transcriptomic, or clinical data. Furthermore, digital pathology can improve the workflow efficiency and productivity of pathologists by reducing manual labor and errors.
Molecular pathology is the study of molecular abnormalities in tissues using techniques such as immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR), or next-generation sequencing (NGS). Molecular pathology can complement histopathology by providing more detailed and specific information about the molecular mechanisms and drivers of diseases. For example, molecular pathology can help identify genetic mutations or alterations that are associated with cancer development, progression, or resistance3. It can also help guide personalized medicine by selecting the most appropriate drugs or therapies based on the molecular profile of each patient3.
In conclusion, technologies such as AI, digital pathology, and molecular pathology can improve histopathology in many ways. They can enhance the quality, efficiency, and innovation of histopathology, as well as enable new applications and discoveries in oncology and beyond. These technologies can help pathologists deliver better care to patients and contribute to the advancement of medicine.