Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

Background: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studies have shown that a high proportion of these patients will experience a recurrence after their resection, leading to an increased risk of death. Cancer staging is currently the gold standard for establishing a patient's prognosis and can help clinicians determine patients who may benefit from additional therapy. However, medical images which are used to help determine the cancer stage, have been shown to hold unutilized prognostic information that can augment clinical data and better identify high-risk NSCLC patients. There remains an unmet need for models to incorporate clinical, pathological, surgical, and imaging information, and extend beyond the current staging system to assist clinicians in identifying patients who could benefit from additional therapy immediately after surgery.

Purpose: We aimed to determine whether a deep learning model (DLM) integrating FDG PET and CT imaging from the thoracic cavity along with clinical, surgical, and pathological information can predict NSCLC recurrence-free survival (RFS) and stratify patients into risk groups better than conventional staging.

Materials and methods: Surgically resected NSCLC patients enrolled between 2009 and 2018 were retrospectively analyzed from two academic institutions (local institution: 305 patients; external validation: 195 patients). The thoracic cavity (including the lungs, mediastinum, pleural interfaces, and thoracic vertebrae) was delineated on the preoperative FDG PET and CT images and combined with each patient's clinical, surgical, and pathological information. Using the local cohort of patients, a multi-modal DLM using these features was built in a training cohort (n = 225), tuned on a validation cohort (n = 45), and evaluated on testing (n = 35) and external validation (n = 195) cohorts to predict RFS and stratify patients into risk groups. The area under the curve (AUC), Kaplan-Meier curves, and log-rank test were used to assess the prognostic value of the model. The DLM's stratification performance was compared to the conventional staging stratification.

Results: The multi-modal DLM incorporating imaging, pathological, surgical, and clinical data predicted RFS in the testing cohort (AUC = 0.78 [95% CI:0.63-0.94]) and external validation cohort (AUC = 0.66 [95% CI:0.58-0.73]). The DLM significantly stratified patients into high, medium, and low-risk groups of RFS in both the testing and external validation cohorts (multivariable log-rank p < 0.001) and outperformed conventional staging. Conventional staging was unable to stratify patients into three distinct risk groups of RFS (testing: p = 0.94; external validation: p = 0.38). Lastly, the DLM displayed the ability to further stratify patients significantly into sub-risk groups within each stage in the testing (stage I: p = 0.02, stage II: p = 0.03) and external validation (stage I: p = 0.05, stage II: p = 0.03) cohorts.

Conclusion: This is the first study to use multi-modality imaging along with clinical, surgical, and pathological data to predict RFS of NSCLC patients after surgery. The multi-modal DLM better stratified patients into risk groups of poor outcomes when compared to conventional staging and further stratified patients within each staging classification. This model has the potential to assist clinicians in better identifying patients that may benefit from additional therapy.