Abstract
Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.
Funder
National measurement system
Reference95 articles.
1. Whole slide imaging in pathology: Advantages, limitations, and emerging perspectives;Farahani;Pathol. Lab. Med. Int.,2015
2. Implementing the DICOM standard for digital pathology
3. PathFlowAI: A high-throughput workflow for preprocessing, deep learning and interpretation in digital pathology;Levy;Proceedings of the Pacific Symposium on Biocomputing 2020,2020
4. Color standardization and optimization in whole slide imaging;Yagi,2011
5. Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification;AlZubaidi;Proceedings of the 2017 Annual Conference on New Trends In Information & Communications Technology Applications (NTICT),2017