CT-based deep learning model for improved disease-free survival prediction in clinical stage I lung cancer: a real-world multicenter study.
코호트
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
68 patients in the SBRT cohort identified as high-risk had significantly worse DFS compared to the low-risk group (p < 0.
I · Intervention 중재 / 시술
surgical resection using pre-treatment CT images, and further validate it in patients receiving stereotactic body radiation therapy (SBRT)
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Clinical relevance The CT-based DL model is a reliable predictive tool for the prognosis of early-stage lung cancer. Its accurate risk stratification assists clinicians in identifying specific patients for personalized clinical decision making.
[OBJECTIVES] To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent surgical resection using pre-treatment CT images, a
- p-value p < 0.01
- 95% CI 0.80-0.89
APA
Fu Y, Hou R, et al. (2025). CT-based deep learning model for improved disease-free survival prediction in clinical stage I lung cancer: a real-world multicenter study.. European radiology, 35(12), 8126-8139. https://doi.org/10.1007/s00330-025-11682-2
MLA
Fu Y, et al.. "CT-based deep learning model for improved disease-free survival prediction in clinical stage I lung cancer: a real-world multicenter study.." European radiology, vol. 35, no. 12, 2025, pp. 8126-8139.
PMID
40506642
Abstract
[OBJECTIVES] To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent surgical resection using pre-treatment CT images, and further validate it in patients receiving stereotactic body radiation therapy (SBRT).
[MATERIALS AND METHODS] A retrospective cohort of 2489 clinical stage I non-small cell lung cancer (NSCLC) patients treated with operation (2015-2017) was enrolled to develop a DL-based DFS prediction model. Tumor features were extracted from CT images using a three-dimensional convolutional neural network. External validation was performed on 248 clinical stage I patients receiving SBRT from two hospitals. A clinical model was constructed by multivariable Cox regression for comparison. Model performance was evaluated with Harrell's concordance index (C-index), which measures the model's ability to correctly rank survival times by comparing all possible pairs of subjects.
[RESULTS] In the surgical cohort, the DL model effectively predicted DFS with a C-index of 0.85 (95% CI: 0.80-0.89) in the internal testing set, significantly outperforming the clinical model (C-index: 0.76). Based on the DL model, 68 patients in the SBRT cohort identified as high-risk had significantly worse DFS compared to the low-risk group (p < 0.01, 5-year DFS rate: 34.7% vs 77.4%). The DL-score was demonstrated to be an independent predictor of DFS in both cohorts (p < 0.01).
[CONCLUSION] The CT-based DL model improved DFS prediction in clinical stage I lung cancer patients, identifying populations at high risk of recurrence and metastasis to guide clinical decision-making.
[KEY POINTS] Question The recurrence or metastasis rate of early-stage lung cancer remains high and varies among patients following radical treatments such as surgery or SBRT. Findings This CT-based DL model successfully predicted DFS and stratified varying disease risks in clinical stage I lung cancer patients undergoing surgery or SBRT. Clinical relevance The CT-based DL model is a reliable predictive tool for the prognosis of early-stage lung cancer. Its accurate risk stratification assists clinicians in identifying specific patients for personalized clinical decision making.
[MATERIALS AND METHODS] A retrospective cohort of 2489 clinical stage I non-small cell lung cancer (NSCLC) patients treated with operation (2015-2017) was enrolled to develop a DL-based DFS prediction model. Tumor features were extracted from CT images using a three-dimensional convolutional neural network. External validation was performed on 248 clinical stage I patients receiving SBRT from two hospitals. A clinical model was constructed by multivariable Cox regression for comparison. Model performance was evaluated with Harrell's concordance index (C-index), which measures the model's ability to correctly rank survival times by comparing all possible pairs of subjects.
[RESULTS] In the surgical cohort, the DL model effectively predicted DFS with a C-index of 0.85 (95% CI: 0.80-0.89) in the internal testing set, significantly outperforming the clinical model (C-index: 0.76). Based on the DL model, 68 patients in the SBRT cohort identified as high-risk had significantly worse DFS compared to the low-risk group (p < 0.01, 5-year DFS rate: 34.7% vs 77.4%). The DL-score was demonstrated to be an independent predictor of DFS in both cohorts (p < 0.01).
[CONCLUSION] The CT-based DL model improved DFS prediction in clinical stage I lung cancer patients, identifying populations at high risk of recurrence and metastasis to guide clinical decision-making.
[KEY POINTS] Question The recurrence or metastasis rate of early-stage lung cancer remains high and varies among patients following radical treatments such as surgery or SBRT. Findings This CT-based DL model successfully predicted DFS and stratified varying disease risks in clinical stage I lung cancer patients undergoing surgery or SBRT. Clinical relevance The CT-based DL model is a reliable predictive tool for the prognosis of early-stage lung cancer. Its accurate risk stratification assists clinicians in identifying specific patients for personalized clinical decision making.
🏷️ 키워드 / MeSH
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