Abstract 3048: Deep learning to predict survival prognosis for patients with non-small cell lung cancer using images and clinical data
Authors: Lee Edward H. , Zhou Mu , Gamboa Noah , Brennan Kevin , Itakura Haruka , Nair Viswam , Napel Sandy , Wong Simon , Gevaert Olivier 
Published: August 2018

Goal

We present a deep learning framework to predict survival prognosis of patients with non-small cell lung cancer using CT images and clinical data developed and validated in multi-institutional cohorts.

Methods

We developed 3D convolutional neural networks (CNNs) using CT images, tumor nodule segmentation, clinical data (i.e. age, sex, histology, and stage) and survival times as input to predict survival of non-small cell lung cancer. Both the CT images and the segmentations were used as inputs for the CNN, the latter to localize the volumes-of-interests (VOIs) in the CT scans. We used three cohorts to evaluate our framework: Moffitt (n=186), Maastro (n=311), and Stanford (n=130). We investigated pre-training with the LIDC-IDRI dataset (n=1010) and compared deep learning performance with 1674 manually-curated radiomics features and clinical data. We used two ways to evaluate the models. First, we split the three cohorts into training and test sets by selecting two of the cohorts for training and the remaining for testing. Secondly, we combined all cohorts and performed a 10-fold stratified cross-validation. We used the Concordance Index (CI) to estimate model performance. Next, we evaluated priming whereby we use 10% of the validation cohort during training and test on the remaining patients in the test cohort. To ensure that the test set was identical for both priming and non-priming experiments, we discarded these samples for the non-priming tests.

Results

Table 1 shows the results of the two evaluation strategies. We observed consistently high CIs for all three cohorts. LIDC pretraining and priming yielded the highest CIs as compared to radiomic and clinical-only pipelines. For the 10-fold cross-validation experiment, our pipeline achieved an average CI=0.64.

Conclusion

Our study highlights the promising results of our pipeline to predict survival prognosis across three cohorts without the need to define radiomics features.

Train on two cohorts, test on 3rd cohortpretrain on LIDC-IDRItest w/o primingtest w/ primingtest w/o priming (only radiomics features / clinical)test w/ priming (only radiomics features / clinical)train on Moffitt + Maastro, test on Stanfordyes0.620.630.52 (radiomics only)0.53 (radiomics only)train on Moffitt + Maastro, test on Stanfordno0.610.620.57 (clinical only)0.57 (clinical only)train on Moffitt + Stanford, test on Maastroyes0.60.60.55 (radiomics only)0.55 (radiomics only)train on Moffitt + Stanford, test on Maastrono0.580.60.56 (clinical only)0.56 (clinical only)train on Stanford + Maastro, test on Moffittyes0.580.60.53 (radiomics only)0.53 (radiomics only)train on Stanford + Maastro, test on Moffittno0.60.570.56 (clinical only)0.56 (clinical only)

Citation Format: Edward H. Lee, Mu Zhou, Noah Gamboa, Kevin Brennan, Haruka Itakura, Viswam Nair, Sandy Napel, Simon Wong, Olivier Gevaert. Deep learning to predict survival prognosis for patients with non-small cell lung cancer using images and clinical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3048.