Total nodule number is an independent prognostic factor in resected stage III non-small cell lung cancer: a deep learning powered study

Almost every lung cancer patient has multiple pulmonary nodules while the significance of nodule multiplicity in locally advanced non-small cell lung cancer (NSCLC) remained unclear. This study explores the relationship between deep learning detected total nodule number (TNN) and survival outcomes in patients with surgical resected stage I-III NSCLC. Patients who underwent surgical resection for stage I-III NSCLC with accessible preoperative chest CT scan from 2005 to 2018 were identified from our database. Deep learning-based AI algorithms using convolutional neural networks (CNN) was applied for pulmonary nodule (PN) detection and classification. Of the 2126 patients, a total number of 33410 PNs were detected by AI. Median TNN detected per person was 12 (IQR 7-20). AI-detected TNN (analyzed as continuous variable) was independent prognostic factor for both RFS (HR 1.012, 95% CI 1.002-1.022, p = 0.021) and OS (HR 1.013, 95% CI 1.002-1.025, p = 0.021) in multivariate analyses of stage III cohort; while it was not significantly associated with survival in stage I and II cohorts. In terms of nodule categories, the numbers of upper-lobe nodule, same-side nodule, other-side nodule, solid nodule, and even solid nodule at small size (≤ 6mm) were independent prognostic factors; while the numbers of middle/lower-lobe nodule, same-lobe nodule, subsolid nodule, calcific nodule and perifissural nodule were not associated with survival. In survival tree analysis, rather than using traditional IIIA and IIIB classification, the model grouped cases by AI-detected TNN (lower vs. higher: log-rank p < 0.001), which showed superior discrimination of survival in stage III cohort. In conclusion, AI-detected TNN was significantly associated with survival in patients with surgical resected stage III NSCLC. Lower TNN detected on preoperative CT scan indicated better prognosis in patients who underwent complete surgical resection. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No external funding was received. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study involving human participants were reviewed and approved by the Institutional Review Board of Peking University People's Hospital (2020PHB385-01). Since only de-identified data were used in this study, informed consents for participants were waived by the committee. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data used to support the findings of this study are available from the corresponding author upon request. * AI : artificial intelligence CAD : computer aided detection/diagnosis CNN : convolutional neural network GGN : ground-glass nodule HR : hazard ratio IPM : intrapulmonary metastasis LDCT : low-dose computed tomography MPLC : multiple primary lung cancer NSCLC : non-small cell lung cancer OS : overall survival PET : positron emission tomography PN : pulmonary nodule RFS : recurrence-free survival TNN : total nodule number