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X. Cai



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    MA 17 - Locally Advanced NSCLC (ID 671)

    • Event: WCLC 2017
    • Type: Mini Oral
    • Track: Locally Advanced NSCLC
    • Presentations: 1
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      MA 17.03 - Prognostic Value of the New IASLC/ATS/ERS Lung Adenocarcinoma Classification in Completely Resected Stage IIIA(N2) NSCLC (ID 10180)

      15:45 - 17:30  |  Author(s): X. Cai

      • Abstract
      • Presentation
      • Slides

      Background:
      Completely resected stage IIIA(N2) non-small cell lung cancer (NSCLC) patients are a heterogeneous population, with 5-year survival rates ranging from 10% to 30%. The aim of this study was to investigate the relationship between the predominant subtype according to the new IASLC/ATS/ERS pathologic classification and prognosis in completely resected stage IIIA(N2) lung adenocarcinoma.

      Method:
      The medical records of 179 consecutive patients with completely resected stage IIIA(N2) NSCLC were reviewed between January 2005 and July 2012. According to the new pathologic classification, each tumor was reviewed using the comprehensive histological subtyping while recording the percentage in 5% increments for each histological component. Adenocarcinoma was divided into lepidic predominant, papillary predominant, acinar predominant, micropapillary predominant and solid-predominant. The predominant pattern was defined as the pattern with the largest percentage. To compare progression-free survival (PFS) and overall survival (OS) time between difference subtypes in lung adenocarcinomas, log-rank test was used for univariate analysis, and cox regression was used for multivariate analysis.

      Result:
      The median follow-up time was 42.7 months (range, 4.4–96.7months). The median PFS and OS time was 19.6 and 45.5 months, respectively. The 5-year PFS and OS rates were 16.4% and 34.6%, respectively. Patients with micropapillary and solid predominant tumors had poorer PFS (p=0.027) and OS (p=0.003) as compared to those with other subtypes predominant tumors. Micropapillary and solid predominant tumors were also significantly associated with an increased risk of locoregional recurrence (P=0.025), while not significantly associated with distant metastasis (P=0.21) than other subtypes predominant tumors. Multivariate analysis revealed that the new classification, chemotherapy, clinical N stage and LN ratio were independent prognostic factors for OS. Figure 1



      Conclusion:
      In patients with completely resected stage IIIA(N2) NSCLC, the predominant subtype according to new IASLC/ATS/ERS classification was an independent prognostic factor. It is valuable of screening out high risk patients to receive postoperative adjuvant therapy.

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    P3.13 - Radiology/Staging/Screening (ID 729)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      P3.13-022 - 3D CNNs for Recognition of Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma (ID 9799)

      09:30 - 16:00  |  Author(s): X. Cai

      • Abstract
      • Slides

      Background:
      In this study, we built three 3-dimensional convolutional neural networks (CNN) for recognition of epidermal growth factor receptor (EGFR) mutation status in Chinese patients with lung adenocarcinomas based on non-enhanced computed tomography (CT) images.

      Method:
      From October 2008 to December 2015, 405 patients with lung adenocarcinomas were included in this retrospective study. Their pathological phenotypes and EGFR mutation status were gained from surgical resections. Their CT images used in this study were taken before any invasive operation. Tumors with a diameter smaller than 8 mm or have ground glass component were excluded. Region of interest that includes tumors were segmented manually by clinicians and preprocessed to have uniform size and grey-level range before applied to CNNs. The three CNNs have 4 convolutional and 1 full connection layers between input and output layers. The inputs size of three CNNs are 21×21×21, 31×31×31, and 41×41×41, respectively. The outputs of the CNN are the probabilities of mutant and wild status. The CNN classifier’s performance was then validated using an independent set and evaluated using area under curve (AUC) values of the receiver operating characteristic.

      Result:
      405 patients diagnosed with lung adenocarcinoma staging I to IV were included in this study (195 male, 210 female; 61 smokers, 344 non-smokers). The patients received surgery based treatment and their tumor stage was based on pathological reports. EGFR mutations (mainly 19del and 21L858R) were found in 198/320(61.9%) and 56/85(65.9%) patients in training and validation sets, respectively. The CNN showed an AUC of 0.767 (95% confidence interval: 0.668-0.866, p<0.001) in the validation set. The sensitivity and specificity are 62.5% and 89.7% at best diagnostic decision point. These results were highest among published results of only using images to recognize EGFR.

      Conclusion:
      The CNN showed potential ability to recognize EGFR mutation status in patients with lung adenocarcinomas and could be improved in the future works to help make clinical decisions.

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