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AI Algorithm for Lung Adenocarcinoma Pattern Quantification (PATQUANT): International Validation and Advanced Risk

Yuan Wang1, Kris Lami2, Waleed Ahmad1

  • 1Institute of Pathology University Hospital Cologne Cologne Germany.

Medcomm
|September 10, 2025
PubMed
Summary

A new AI tool, PATQUANT, accurately classifies lung adenocarcinoma (LUAD) patterns, outperforming pathologists. This tool aids in developing superior grading systems for better patient risk stratification in LUAD.

Keywords:
AIPATQUANTgradinglung adenocarcinomalung cancerpattern

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Area of Science:

  • Oncology
  • Pathology
  • Artificial Intelligence

Background:

  • Morphological patterns in lung adenocarcinoma (LUAD) are crucial for prognosis, yet optimal grading remains debated.
  • Current grading systems for LUAD require refinement for improved accuracy and prognostic value.

Purpose of the Study:

  • To develop and validate a fully automated, quantitative AI tool (PATQUANT) for LUAD pattern classification.
  • To evaluate existing LUAD grading strategies and identify the most effective system.
  • To propose and validate novel, explainable grading principles for enhanced patient risk stratification.

Main Methods:

  • Training PATQUANT on a pathologist-annotated dataset for LUAD pattern classification.
  • Validating PATQUANT using independent test datasets and comparing its performance against 13 expert pathologists.
  • Analyzing five multinational cohorts (n=1120) of resectable LUAD to assess the prognostic value of identified patterns and grading systems.

Main Results:

  • PATQUANT achieved high accuracy in LUAD pattern segmentation and classification, surpassing 8 out of 13 pathologists.
  • The complex glandular pattern in LUAD showed a distinct prognostic profile.
  • Predominant pattern-based and simplified IASLC grading systems demonstrated superior prognostic value compared to others.
  • Two new, explainable grading principles were validated, offering fine-grained, independent risk stratification.

Conclusions:

  • The developed AI tool, PATQUANT, offers a robust and automated solution for LUAD pattern analysis, exceeding expert pathologist performance.
  • Novel grading approaches, informed by AI-driven pattern quantification, provide superior prognostic capabilities over traditional methods.
  • Publicly releasing the agreement dataset will foster further advancements in LUAD grading and analysis.