# Pathological Digital Biomarkers: Validation and Application
## 3 Validation of Pathological Digital Biomarker
### Clinical Utility  
Pathological digital biomarkers must be shown to be therapeutically beneficial after analytical and clinical validity has been established. Biomarkers must either improve clinical outcomes or show that they can achieve the same results at a lower cost or with less toxicity [40]. The clinical utility of biomarkers can be demonstrated in two ways: by directly demonstrating the utility of novel biomarkers or by demonstrating the correlation between existing and new biomarkers [41]. Among published studies, the following are examples of establishing utility in clinical practice employing digital pathology technology. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**NCT No.** & **Specienen** & **Primary Outcome** & **Source** & **Intervention** & **Country** \\ \hline NCT02470572 & Surgical specimen & Diagnosis & WSI & Omys IDP system & N/A \\ NCT05447221 & Gastric biopsy & Diagnosis of intestinal metaplasia & WSI & DEAIOS & China \\ NCT0484693 & Ovarian cancer tissue & Diagnosis, therapeutic prediction & WSI & Multilayer data analysis & Finland \\ NCT0504636 & Not specified & Diagnosis, therapeutic prediction & Digital image & Multimodal AI & China \\ NCT0487056 & Not specified & Diagnosis & WSI & PIP5.1 scanner & USA \\ NCT05221814 & Lung surgical specimen & Diagnosis & Gross photo image & Deep learning model & USA \\ NCT04695015 & Ocular tumor tissue & Diagnosis & Digital image & Multimodal AI & China \\ NCT0482941 & Colon polyp & Diagnosis & Colonoscopic image & A-aided diagnosis & Hungary \\ NCT0484056 & Gastric biopsy & Diagnosis & WSI & Machine learning & China \\ NCT04217044 & Brain tumor tissue & Molecular profile prediction & Histopathology image & Deep learning algorithm & China \\ NCT05300113 & Brain tumor tissue & Diagnosis & WSI & AI-aided Diagnosis & China \\ \hline \hline \end{tabular} Search date: 18 September 2022; ClinicalTrials.gov.  
\end{table} 
Table 1: Currently registered clinical trials with digital pathology. Studies utilized pathological digital biomarker to predict tumor response to immune checkpoint inhibitor in advanced non-small-cell lung cancer (NSCLC). When the programmed death ligand-1 (PDL-1) tumor proposition score (TPS) was greater than 50%, previous trials demonstrated that Pembrolizumab had a higher tumor response rate (TRR) and survival in advanced NSCLC than chemotherapy [42]. When the PDL-1 TPS was 1%-49%, however, the effects of Pembrolizumab and chemotherapy treatments were identical [43], indicating the need for a novel biomarker to predict tumor response in patients. [PERSON] et al. used an artificial-intelligence-powered spatial analysis of tumor-infiltrating lymphocytes (TILs) as an immune checkpoint inhibitor (ICI)'s complementary biomarker in advanced NSCLC to achieve this goal [44]. Patients with advanced NSCLC who had received ICI monotherapy were studied retrospectively. When compared to patients with immune-excluded or immunological-desert phenotypes, patients with the inflamed immune phenotype (IP) had more local immune cytolytic activity, a higher TRR, and a longer progression-free survival. There is disagreement over the regulation and inspection of clinical trials using these technologies as medical devices. Although there are AI algorithms in clinical practice that have received regulatory clearance, constantly evolving AI applications pose additional difficulties [45]. To the best of our knowledge, there are no regulations governing the application of image analysis or digital pathology in clinical trials. The development of the REMARK recommendations for biomarker studies was prompted by the low yield of clinically actionable biomarkers from a large number of research studies involving significant resource expenditure [46]. Additionally, there is no consensus on the best way for pathologists to integrate computational pathology systems into their daily operations [47].