Editorial: Computational pathology for precision diagnosis, treatment, and prognosis of cancer.

Histopathology is considered the gold standard in determining the presence and nature of tumors. Technological advances in automated high-speed and high-resolution whole-slide imaging have laid the foundation for a digital revolution in microscopy. Digital histopathological images can be analyzed efficiently with image analysis and machine learning techniques. These techniques have shown great potential for extracting sub-visual, quantitative, and valuable features from whole-slide images to characterize tumors and support clinical decision (1,2). Besides histopathological images, other data modalities, such as radiological images and multi-omics data, are also used to assist the decision-making process for cancer diagnosis, treatment, and prognosis (3,4). At present, it is not clear how these macroscopic, microscopic, and molecular features are related. Exploring the association between different data modalities can give new insights into diseases. This Research Topic is to highlight some latest developments in computational pathology that use either classical image analysis or state-of-the-art deep learning solutions for improved clinical decision making. A brief summary of the articles in this collection is provided below. Segmentation of regions of interest is usually an important step in the workflow of computer-aided diagnosis. Shi