Personalized Prediction of Postoperative Recurrence in Lung Squamous Cell Carcinoma: Integrating AI-Based Nuclear Morphometry and Clinical Data
The integration of AI-based nuclear morphology analysis and clinical data provides an objective and practical tool for personalized postoperative management in LSQCC. This approach enables tailored clinical decision-making by identifying patients at high risk for early recurrence and customizing postoperative treatment plans to meet the specific needs of each individual.
Authors
- Masahiko Kuroda (ORCID: https://orcid.org/0000-0001-7052-4289)
- Yujin Kudo (ORCID: https://orcid.org/0000-0003-3187-9970)
- J Matsubayashi
- Yoshihisa Shimada
- Norihiko Ikeda
- Akira Saito
- Tomokazu Omori
- Toshitaka Nagao
Institutions
- Tokyo Medical University (JP)
- Quantitative BioSciences (US)
Publication Details
- Journal
- Journal of Personalized Medicine
- Published
- 2026-04-06
- DOI
- https://doi.org/10.3390/jpm16040205
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science