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MetAssist 2.0: A Generalizable AI Framework for Lymph Node Metastasis Detection Across Multiple Cancer Types.

Year: 2026

Type: article

Abstract: Lymph node metastasis assessment is critical for cancer staging, yet the process is labor-intensive and prone to variability, particularly when evaluating micro-metastases or isolated tumor cells that can directly alter treatment decisions. We present MetAssist 2.0, a modular AI system that combines a pathology foundation model with transformer-based segmentation to automate metastasis detection across cancer types. Trained on colorectal and upper gastrointestinal cancers, it was validated on 8,144 slides spanning seven cancer types and 14 multi-institutional cohorts. MetAssist 2.0 achieved at least 90% sensitivity in 13 cohorts and 91% specificity in 11, including challenging subtypes such as mucinous adenocarcinoma and tumor deposits. With only 10 annotated slides, the system adapted to unseen cancer types via few-shot fine-tuning. As a triage tool for colorectal cancer, it could reduce pathologist workload by up to 72% with a 98% sensitivity and revealed metastases missed in routine reporting. These results demonstrate broad generalizability and near clinical-grade performance, positioning MetAssist 2.0 for integration into the pathology workflows.

Source: Open Access CRIS of the University of Bern

DOI: 10.48620/97157

Authors: Garcia-Baroja, Javier, Dislich, Bastian, Zens, Philipp, Zagrapan, Branislav, Parokkaran, Fammi Maria, Wütschert, Léo, Weber, Synes Elias, Christe, Lucine, Neppl, Christina, Rau, Tilman, Perren, Aurel, Tolkach, Yuri, Zlobec, Inti, Khan, Amjad

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7158136204

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