Evaluating generative AI models for explainable pathological feature extraction in lung adenocarcinoma: grading assessment and prognostic model construction Article Swipe

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Junyi Shen , Anqi Lin , Ting Wei , J. Andrew Zhang , Peng Luo ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1016/j.lanwpc.2024.101352

Background: With the widespread application of generative AI (GenAI) models, it is crucial to systematically evaluate their performance in lung adenocarcinoma histopathological assessment. This study aimed to evaluate and compare the performance of three GenAI models with visual capabilities (GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro) in lung adenocarcinoma histological pattern recognition and grading, and to explore the construction of prognostic prediction models based on GenAI feature extraction. Methods: This retrospective study extracted 310 diagnostic slides from the TCGA-LUAD database for model evaluation. An additional 87 diagnostic pathology slides from local lung adenocarcinoma surgical patients were used for external validation of the prognostic model. Primary outcomes were GenAI grading accuracy and stability, measured by the area under the receiver operating characteristic curve (AUC) and intraclass correlation coefficient (ICC), respectively. Secondary outcomes included the construction and assessment of machine learning-based prognostic prediction models, utilizing features extracted by GenAI, with model performance evaluated using the Concordance index (C-index). Findings: Claude-3.5-Sonnet demonstrated the best overall performance, combining high grading accuracy (average AUC = 0.82) with moderate stability (ICC = 0.59) The optimal machine learning-based prognostic model, constructed using features extracted by Claude-3.5-Sonnet and incorporating clinical variables, showed good performance in both internal and external validation, with an average C-index of 0.72. Meta-analysis demonstrated that this prognostic model effectively stratified patients into risk groups, with the high-risk group showing significantly worse outcomes (Hazard ratio = 6.44, 95% confidence interval = 3.42-12.14). Interpretation: This study demonstrates the potential application value of GenAI models in lung adenocarcinoma histopathological assessment. Claude-3.5-Sonnet demonstrated the highest grading accuracy, and the machine learning-based prognostic model that utilized its feature extraction showed good predictive capabilities. These findings provide new research directions for AI-assisted pathological diagnosis and prognostic prediction, with the potential to improve the management of lung adenocarcinoma patients.

Related Topics

Generative Grammar

Artificial Intelligence

Concepts

Grading (engineering) Pathological Generative grammar Generative model Artificial intelligence Adenocarcinoma Computer science Lung Pathology Medicine Natural language processing Internal medicine Biology Cancer Ecology

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.lanwpc.2024.101352
OA Status: gold
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OpenAlex ID: https://openalex.org/W4407631405

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DOI: https://doi.org/10.1016/j.lanwpc.2024.101352

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Title: Evaluating generative AI models for explainable pathological feature extraction in lung adenocarcinoma: grading assessment and prognostic model construction

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Language: en

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Publication year: 2025

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Publication date: 2025-02-01

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Authors: Junyi Shen, Anqi Lin, Ting Wei, J. Andrew Zhang, Peng Luo

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Concepts: Grading (engineering), Pathological, Generative grammar, Generative model, Artificial intelligence, Adenocarcinoma, Computer science, Lung, Pathology, Medicine, Natural language processing, Internal medicine, Biology, Cancer, Ecology

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abstract_inverted_index.AI-assisted	278
abstract_inverted_index.Background:	0
abstract_inverted_index.Concordance	150
abstract_inverted_index.application	4, 240
abstract_inverted_index.assessment.	22, 249
abstract_inverted_index.coefficient	123
abstract_inverted_index.constructed	180
abstract_inverted_index.correlation	122
abstract_inverted_index.effectively	211
abstract_inverted_index.evaluation.	79
abstract_inverted_index.extraction.	64
abstract_inverted_index.performance	17, 31, 146, 192
abstract_inverted_index.prediction,	283
abstract_inverted_index.recognition	48
abstract_inverted_index.validation,	198
abstract_inverted_index.3.42-12.14).	233
abstract_inverted_index.capabilities	38
abstract_inverted_index.construction	55, 130
abstract_inverted_index.demonstrated	155, 206, 251
abstract_inverted_index.demonstrates	237
abstract_inverted_index.histological	46
abstract_inverted_index.pathological	279
abstract_inverted_index.performance,	159
abstract_inverted_index.Meta-analysis	205
abstract_inverted_index.capabilities.	270
abstract_inverted_index.incorporating	187
abstract_inverted_index.respectively.	125
abstract_inverted_index.retrospective	67
abstract_inverted_index.significantly	222
abstract_inverted_index.adenocarcinoma	20, 45, 89, 247, 293
abstract_inverted_index.characteristic	117
abstract_inverted_index.learning-based	135, 177, 259
abstract_inverted_index.systematically	14
abstract_inverted_index.Gemini-1.5-Pro)	42
abstract_inverted_index.Interpretation:	234
abstract_inverted_index.Claude-3.5-Sonnet	154, 185, 250
abstract_inverted_index.histopathological	21, 248
abstract_inverted_index.Claude-3.5-Sonnet,	40
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
sustainable_development_goals[0].id	https://metadata.un.org/sdg/2
sustainable_development_goals[0].score	0.5199999809265137
sustainable_development_goals[0].display_name	Zero hunger
citation_normalized_percentile.value	0.06454101
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citation_normalized_percentile.is_in_top_10_percent	False