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Published on: June 7, 2015
Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I
Jianjiao Ni1, Hongru Chen1, Lu Yu2
1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China.
A radiomics model accurately predicts occult lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). This model aids in stratifying risk for regional recurrence, improving patient management.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Risk stratification for regional recurrence (RR) is crucial for managing clinical stage I non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT).
- Accurate prediction of occult lymph node metastasis (OLNM) is essential for tailoring adjuvant treatments and surveillance strategies.
Purpose of the Study:
- To develop and validate a radiomics model for predicting OLNM in early-stage NSCLC patients.
- To assess the radiomics model's utility in predicting RR in SBRT-treated early-stage NSCLC patients.
Main Methods:
- A preoperative computed tomography (CT)-based radiomics model was constructed using surgical data from training (2013-2018) and validation (2019-2020) cohorts.
- The radiomics model's performance in predicting OLNM was evaluated, and it was subsequently applied to predict RR in an independent cohort of SBRT-treated NSCLC patients.
Main Results:
- The radiomics model, utilizing eight CT features, achieved high predictive performance for OLNM (AUC 0.85 training, 0.83 validation).
- The model effectively identified patients at high risk for RR in the SBRT cohort, demonstrating significantly shorter regional recurrence-free survival, progression-free survival, and overall survival compared to the low-risk group.
Conclusions:
- A CT-based radiomics model can accurately identify patients with OLNM in early-stage NSCLC.
- This radiomics model shows promise for risk stratification and improving management strategies for SBRT-treated early-stage NSCLC patients at risk of regional recurrence.
