Tumor Cell Proportion Assessment in Advanced Non-Squamous Non-Small Cell Lung Cancer Tissue Samples in Real-World...

Area of Science:

• Oncology
• Pathology
• Medical Diagnostics

Background:

• Accurate driver gene alteration identification is crucial for non-small cell lung cancer (NSCLC) treatment selection.
• Precise tumor cell proportion assessment is essential for reliable gene alteration detection in NSCLC.
• The ASTRAL study evaluated inter-rater agreement in tumor cell proportion assessments for advanced NSCLC.

Purpose of the Study:

• To investigate the agreement in tumor cell proportion assessments among local pathologists, a Central Pathology Committee (CPC), and an artificial intelligence (AI) algorithm.
• To determine the reliability of AI in estimating tumor cell proportion compared to human expert assessments.
• To assess the clinical utility of AI in improving diagnostic accuracy for NSCLC.

Main Methods:

• Prospective, observational, multicenter study (ASTRAL) involving 204 advanced NSCLC patients.
• Tumor tissues assessed by local pathologists (H&E slides), CPC (digitized slides), and an AI algorithm (digitized slides).
• Intraclass correlation coefficient (ICC) used to measure agreement between raters.

Main Results:

• Poor to moderate agreement (ICC=0.588) between local pathologists and the CPC.
• Moderate agreement (ICC=0.652) between the AI algorithm and the CPC.
• Poor to moderate agreement (ICC=0.465) between the AI algorithm and local pathologists.

Conclusions:

• The AI algorithm demonstrated the highest numerical agreement with the CPC, indicating potential usefulness in clinical practice.
• Current agreement levels highlight the need for continued efforts to refine AI algorithms for accurate tumor cell proportion estimation.
• Integrating AI tools may enhance the consistency and accuracy of diagnostic assessments in real-world NSCLC management.