# Pathological Digital Biomarkers: Validation and Application
## 3 Validation of Pathological Digital Biomarker
### Clinical Validation  
Pathological digital biomarkers must be clinically validated after analytic validation. Patients should be able to be divided into two or more groups using biomarkers. Such divided groups can include those with or without disease, those who are expected to have a good or poor result [31], and those who are at a higher or lower risk of disease recurrence [32]. Central pathology review is the main benefit of digital pathology clinical validation. Digital pathology allows a simultaneous case review and quick access to inter national experts. Digital imaging eliminates the need to physically transfer slides and tissue blocks, preventing damage or loss [33]. Digital pathology provides new information from histological samples. Digital image analysis can be used to quickly quantify and reproduce immune cell infiltrates [34; 35]. Pathologists can mine and link novel digital morphometric signatures to clinical outcome [36]. However, pathologists face significant challenges in the arduous process of manually assessing specimens to determine their clinical utility. Methods based on artificial intelligence can help make accurate quantitative assessments of digital images at a level that could be better than that of a human observer [37]. Examples of clinical validation of digital pathological digital biomarker are shown below.  
[PERSON] et al. developed a diagnostic algorithm using digital pathology to quantitatively evaluate tumor-infiltrating CD8+ T cell spatial densities, which is one of the most important predictive indicators in immuno-oncology [31]. The diagnostic algorithm translated spatial densities of tumor-infiltrating CD8+ T cells into the clinically relevant immunological diagnostic categories \"inflamed\", \"excluded\", and \"desert\" based on intra-tumoral and stromal CD8+ T cell densities in the tumor center compartment. For tumor classification in the desert category, a precision of 100 percent was achieved, with a few excluded tumors (sensitivity 83 percent, specificity 100 percent) allocated to the inflamed tumors. (sensitivity 100 percent, specificity 62.5 percent)  
Surgically resected hepatocellular carcinoma (HCC) has a significant recurrence rate but cannot be predicted. Based on digital pathologic images and machine learning using a support vector machine (SVM), [PERSON] et al. predicted the early recurrence of HCC following resection [32]. For the prediction, three integrated SVM models were used: one based on the region of interest (ROI) of HCC area, one based on the ROI of non-HCC area, and one based on nuclei characteristics. The prediction was precise 89.9% of the time. The potential clinical benefits for disease-specific digital biomarkers included a more rapid and accurate disease diagnosis, prediction, and potential reduction in size and duration of clinical trials. Continuous efforts and clinical trials (summarized in Table 1) are currently being performed. Due to the fact that clinical validation of pathological digital biomarkers is possible under digital pathology systems, slide scanners and image analysis algorithms, with or without the assistance of artificial intelligence, which are intended for medical use and are classified as medical devices, have been utilized [38]. Regulatory requirements for clinical performance studies of in vitro diagnostics (IVDs) and for the use of IVDs as digital pathology in clinical trials of medicinal products are debated and evolving [39].