Prediction recurrence in stage I epidermal growth factor receptor-mutated non-small cell lung cancer using multi-modal data

Clinical and radiological factors are effective predictors of recurrence risk in Stage I EGFR-mutated NSCLC, and incorporating TP53 mutation data further improves the prognostic accuracy.

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Publication Details

Journal
Lung Cancer
Published
2025-08-25
DOI
https://doi.org/10.1016/j.lungcan.2025.108727
Citations
1
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
Field-Weighted Citation Impact
1.52
Controls
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article

Prediction recurrence in stage I epidermal growth factor receptor-mutated non-small cell lung cancer using multi-modal data

Akiko Tateishi, M. Miyake, Taro Shibata, Ryuji Hamamoto et al.
1 citations
Lung Cancer
Lung Cancer Treatments and Mutations
1.52
article

Prediction recurrence in stage I epidermal growth factor receptor-mutated non-small cell lung cancer using multi-modal data

Akiko Tateishi, M. Miyake, Taro Shibata, Ryuji Hamamoto, Nobuji Kouno, Masahiko Kusumoto, Kouya Shiraishi, Aya Kuchiba, Hiroshi Igaki, Jo Nishino, Yasushi Yatabe, Koji Inaba, Masami Mukai, Hidehito Horinouchi, Naoki Mihara, Ken Takasawa, Yuichiro Ohe, Masaaki Komatsu, Yu Okubo, Yukihiro Yoshida, Takaaki Mizuno, Katsuji Takeda, Takashi Kohno, Shun-ichi Watanabe
article en
1 citations

Abstract

Clinical and radiological factors are effective predictors of recurrence risk in Stage I EGFR-mutated NSCLC, and incorporating TP53 mutation data further improves the prognostic accuracy.

Lung CancerVol. 207
National Cancer Centre Japan (JP), National Cancer Center (US), RIKEN Center for Advanced Intelligence Project (JP), National Cancer Research Institute (GB)
Openalex Percentile: Top 15%
Lung Cancer Treatments and Mutations
1.52
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