Application of whole slide image markup and annotation for pathologist knowledge capture

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

BACKGROUND

The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way.

RECENT FINDINGS

Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker.

CONCLUSIONS

The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

Whole Slide Imaging of Pap Cell Block Preparations versus Liquid-Based Thin-Layer Cervical Cytology: A Comparative Study Evaluating the Detection of Organisms and Nonneoplastic Findings

OBJECTIVE

Cervical cancer is one of the most common malignancies worldwide, yet it is preventable by population screening. In a previous study, we confirmed the feasibility of utilizing whole slide imaging (WSI) of cell block (CB) preparations to overcome the limitations of digitizing cytologic samples. In this study, we evaluated the accuracy of WSI in identifying various organisms and nonneoplastic findings.

STUDY DESIGN

A total of 335 WS images from Pap CB preparations were analyzed using the Aperio system. The test performance characteristics of ThinPrep (TP) and WSI samples were compared for adequacy, for the presence of bacterial vaginosis (BV), fungi, Trichomonas vaginalis (TV) and herpes simplex virus (HSV) and for nonneoplastic findings.

RESULTS

The WSI samples contained optimal material from all preparations. BV was diagnosed in 33 WSI versus 36 TP samples. Budding yeasts and/or pseudohyphal forms were noted in 18 WSI versus 19 TP samples. TV organisms (10 of 11 samples) and 1 HSV case were accurately identified in the WSI and TP samples. Squamous metaplasia, keratosis and reactive/reparative and inflammatory changes were easily identified by WSI.

CONCLUSIONS

The concept of WSI from Pap CB preparations is potentially feasible for adoption. Digital remote web-based technology eliminates the need for an individual on site, saving time and resources.