Artificial Intelligence-Driven Quantification of Tumor-Stroma Ratio and Fibroblasts Enables Precise Classification of Stroma Quality and Quantity in Predicting Colorectal Cancer Recurrence
AI-powered histopathological quantification of TSR and CAF density improves prognostic accuracy in CRC and offers a promising approach for refining risk stratification. These findings support the integration of AI-based pathology into clinical practice to enhance diagnostic precision and patient management.
Authors
- Minsun Jung (ORCID: https://orcid.org/0000-0002-8701-4282)
- Soo Ick Cho (ORCID: https://orcid.org/0000-0003-3414-9869)
- Aaron Valero Puche (ORCID: https://orcid.org/0009-0003-0777-9942)
- Jun Yong Kim (ORCID: https://orcid.org/0000-0002-4313-9032)
- Hoein Jeong (ORCID: https://orcid.org/0009-0009-3087-2479)
- Sang-Hoon Song (ORCID: https://orcid.org/0000-0002-0812-5444)
Institutions
- Yonsei University (KR)
Publication Details
- Journal
- Pathobiology
- Published
- 2025-04-19
- DOI
- https://doi.org/10.1159/000546021
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08