AI Algorithm for Lung Adenocarcinoma Pattern Quantification (PATQUANT): International Validation and Advanced Risk Stratification Superior to Conventional Grading

The morphological patterns of lung adenocarcinoma (LUAD) are recognized for their prognostic significance, with ongoing debate regarding the optimal grading strategy. This study aimed to develop a clinical-grade, fully quantitative, and automated tool for pattern classification/quantification (PATQUANT), to evaluate existing grading strategies, and determine the optimal grading system. PATQUANT was trained on a high-quality dataset, manually annotated by expert pathologists. Several independent test datasets and 13 expert pathologists were involved in validation. Five large, multinational cohorts of resectable LUAD (patient <i>n</i> = 1120) were analyzed concerning prognostic value. PATQUANT demonstrated excellent pattern segmentation/classification accuracy and outperformed 8 out of 13 pathologists. The prognostic study revealed a distinct prognostic profile for the complex glandular pattern. While all contemporary grading systems had prognostic value, the predominant pattern-based and simplified IASLC systems were superior. We propose and validate two new, fully explainable grading principles, providing fine-grained, statistically independent patient risk stratification. We developed a fully automated, robust AI tool for pattern analysis/quantification that surpasses the performance of experienced pathologists. Additionally, we demonstrate the excellent prognostic capabilities of two new grading approaches that outperform traditional grading methods. We make our extensive agreement dataset publicly available to advance the developments in the field.

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Publication Details

Journal
MedComm
Published
2025-09-01
DOI
https://doi.org/10.1002/mco2.70380
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

AI Algorithm for Lung Adenocarcinoma Pattern Quantification (PATQUANT): International Validation and Advanced Risk Stratification Superior to Conventional Grading

Reinhard Büttner, Sofia Campelos, André L. Moreira, Matthias Heldwein et al.
MedComm
Radiomics and Machine Learning in Medical Imaging
article

AI Algorithm for Lung Adenocarcinoma Pattern Quantification (PATQUANT): International Validation and Advanced Risk Stratification Superior to Conventional Grading

Reinhard Büttner, Sofia Campelos, André L. Moreira, Matthias Heldwein, Junya Fukuoka, Alexander Quaas, Yuanzi Ye, Andrey Bychkov, Simon Schallenberg, Kris Lami, Yuri Tolkach, Anne M. Schultheis, Danny Jonigk, Aleš Ryška, Waleed Ahmad, Yuan Wang, Xiaoya Zhu
article en

Abstract

The morphological patterns of lung adenocarcinoma (LUAD) are recognized for their prognostic significance, with ongoing debate regarding the optimal grading strategy. This study aimed to develop a clinical-grade, fully quantitative, and automated tool for pattern classification/quantification (PATQUANT), to evaluate existing grading strategies, and determine the optimal grading system. PATQUANT was trained on a high-quality dataset, manually annotated by expert pathologists. Several independent test datasets and 13 expert pathologists were involved in validation. Five large, multinational cohorts of resectable LUAD (patient <i>n</i> = 1120) were analyzed concerning prognostic value. PATQUANT demonstrated excellent pattern segmentation/classification accuracy and outperformed 8 out of 13 pathologists. The prognostic study revealed a distinct prognostic profile for the complex glandular pattern. While all contemporary grading systems had prognostic value, the predominant pattern-based and simplified IASLC systems were superior. We propose and validate two new, fully explainable grading principles, providing fine-grained, statistically independent patient risk stratification. We developed a fully automated, robust AI tool for pattern analysis/quantification that surpasses the performance of experienced pathologists. Additionally, we demonstrate the excellent prognostic capabilities of two new grading approaches that outperform traditional grading methods. We make our extensive agreement dataset publicly available to advance the developments in the field.

MedCommVol. 6(9)
University of Nagasaki (JP), Anhui Medical University (CN), University Medical Center Freiburg (DE), Sir Run Run Shaw Hospital (CN), Kameda Medical Center (JP), MBio Diagnostics (United States) (US), Universitätsklinikum Aachen (DE), German Center for Lung Research (DE), First Affiliated Hospital of Anhui Medical University (CN), University Hospital Hradec Králové (CZ), University Hospital Cologne (DE), Nagasaki University (JP), New York University (US), Charité - Universitätsmedizin Berlin (DE), RWTH Aachen University (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Universität zu Köln, European Regional Development Fund, Interreg
Openalex Percentile: Top 67%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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