Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology

Abstract

Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of staging in oncology has been the TNM (tumor-node-metastasis) staging system. Though histopathological grading has shown prognostic significance, it is subjective and limited by interobserver variability even among experienced surgical pathologists. Recently, artificial intelligence (AI) approaches have been applied to pathology images toward diagnostic-, prognostic-, and treatment prediction-related tasks in cancer. AI approaches have the potential to overcome the limitations of conventional TNM staging and tumor grading approaches, providing a direct prognostic prediction of disease outcome independent of tumor stage and grade. Broadly speaking, these AI approaches involve extracting patterns from images that are then compared against previously defined disease signatures. These patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (DL)-based representations, which tend to be more abstract. DL approaches have particularly gained considerable popularity because of the minimal domain knowledge needed for training, mostly only requiring annotated examples corresponding to the categories of interest. In this article, we discuss AI approaches for digital pathology, especially as they relate to disease prognosis, prediction of genomic and molecular alterations in the tumor, and prediction of treatment response in oncology. We also discuss some of the potential challenges with validation, interpretability, and reimbursement that must be addressed before widespread clinical deployment. The article concludes with a brief discussion of potential future opportunities in the field of AI for digital pathology and oncology.

FIG 1.

Representative handcrafted artificial intelligence approaches used in digital pathology. (A) Cell cluster graph approaches used in lung adenocarcinomas prognosis. (B) Global and local graph approaches used in lung squamous cell carcinomas recurrence prediction. (C) Tumor cell multinucleation index as a prognostic marker in oropharyngeal squamous cell carcinoma. (D) Local cell graph of cellular shape and architecture for prognosis prediction in oral cancer. (E) Architecture of tumor nuclei clusters in non–small-cell lung cancer, which are morphologically correlated with radiomic features. (F) Cellular diversity approaches reflecting on differences in orientation of tumor nuclei in oral cancer. (G) Diversity of nuclear shape in tumor cells as a prognostic indicator in breast cancer. (H) Spatial architecture and orientation of tumor-infiltrating lymphocytes in non–small-cell lung cancer treated with immunotherapy.