Extended application of a CT-based artificial intelligence prognostication model in patients with primary lung cancer undergoing stereotactic ablative radiotherapy

  • Hyungjin Kim
    Affiliations
    Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea
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  • Joo Ho Lee
    Correspondence
    Corresponding author at: Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
    Affiliations
    Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea

    Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea

    Cancer Research Institute, Seoul National University, Republic of Korea
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  • Hak Jae Kim
    Affiliations
    Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea

    Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea

    Cancer Research Institute, Seoul National University, Republic of Korea
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  • Chang Min Park
    Affiliations
    Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea

    Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea

    Cancer Research Institute, Seoul National University, Republic of Korea
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  • Hong-Gyun Wu
    Affiliations
    Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea

    Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea

    Cancer Research Institute, Seoul National University, Republic of Korea
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  • Jin Mo Goo
    Affiliations
    Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea

    Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea

    Cancer Research Institute, Seoul National University, Republic of Korea
    Search for articles by this author
Published:November 04, 2021DOI:https://doi.org/10.1016/j.radonc.2021.10.022

      Highlights

      • The target population of a deep learning prognostication model could be extended.
      • The model predicted survival in patients receiving stereotactic radiotherapy for lung cancer.
      • The deep learning model output was an independent prognostic factor for survival.
      • Heat map visualized the association of intra- and peri-tumoral features with survival.

      Abstract

      Background and purpose

      To validate a computed tomography (CT)-based deep learning prognostication model, originally developed for a surgical cohort, in patients with primary lung cancer undergoing stereotactic ablative radiotherapy (SABR).

      Materials and methods

      This retrospective study identified patients with clinical stage T1-2N0M0 lung cancer treated with SABR between 2013 and 2018. The outcomes were local recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS). The discrimination performance of the model, which extracted a quantitative score of cumulative risk for an adverse event up to 900 days, was evaluated using time-dependent receiver operating characteristic curve analysis. Multivariable Cox regression was performed to investigate the independent prognostic value of the model output adjusting for clinical factors including age, sex, smoking status, and clinical T category.

      Results

      In total, 135 patients (median age, 78 years; 101 men; 78 [57.8%] adenocarcinomas and 57 [42.2%] squamous cell carcinomas) were evaluated. Most patients (117/135) were treated with 48–60 Gy in four fractions. Median biologically effective dose was 150.0 Gy (interquartile range, 126.9, 150.0 Gy). For LRFS, the area under the curve (AUC) was 0.72 (95% confidence interval [CI]: 0.58, 0.87). The AUCs were 0.70 (95% CI: 0.60, 0.81) for DFS and 0.66 (95% CI: 0.54, 0.77) for OS. Model output was associated with LRFS (adjusted hazard ratio [HR], 1.043; 95% CI: 1.003, 1.085; P = 0.04), DFS (adjusted HR, 1.03; 95% CI: 1.01, 1.05; P = 0.008), and OS (adjusted HR, 1.025; 95% CI: 1.002, 1.047; P = 0.03).

      Conclusion

      This study showed external validity and transportability of the CT-based deep learning prognostication model for radiotherapy candidates.

      Abbreviations:

      AUC (area under the time-dependent receiver operating characteristic curve), CI (confidence interval), DFS (disease-free survival), DLPM (deep learning prognostication model), HR (hazard ratio), IQR (interquartile range), OS (overall survival), SABR (stereotactic ablative radiotherapy)

      Keywords

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