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Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.

ABSTRACT: Importance:There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC). Objectives:To compare the performances of DeepSurv, a deep learning survival neural network with a tumor, node, and metastasis staging system in the prediction of survival and test the reliability of individual treatment recommendations provided by the deep learning survival neural network. Design, Setting, and Participants:In this population-based cohort study, a deep learning-based algorithm was developed and validated using consecutive cases of newly diagnosed stages I to IV NSCLC between January 2010 and December 2015 in a Surveillance, Epidemiology, and End Results database. A total of 127 features, including patient characteristics, tumor stage, and treatment strategies, were assessed for analysis. The algorithm was externally validated on an independent test cohort, comprising 1182 patients with stage I to III NSCLC diagnosed between January 2009 and December 2013 in Shanghai Pulmonary Hospital. Analysis began January 2018 and ended June 2019. Main Outcomes and Measures:The deep learning survival neural network model was compared with the tumor, node, and metastasis staging system for lung cancer-specific survival. The C statistic was used to assess the performance of models. A user-friendly interface was provided to facilitate the survival predictions and treatment recommendations of the deep learning survival neural network model. Results:Of 17 322 patients with NSCLC included in the study, 13 361 (77.1%) were white and the median (interquartile range) age was 68 (61-74) years. The majority of tumors were stage I disease (10 273 [59.3%]) and adenocarcinoma (11 985 [69.2%]). The median (interquartile range) follow-up time was 24 (10-43) months. There were 3119 patients who had lung cancer-related death during the follow-up period. The deep learning survival neural network model showed more promising results in the prediction of lung cancer-specific survival than the tumor, node, and metastasis stage on the test data set (C statistic = 0.739 vs 0.706). The population who received the recommended treatments had superior survival rates than those who received treatments not recommended (hazard ratio, 2.99; 95% CI, 2.49-3.59; P < .001), which was verified by propensity score-matched groups. The deep learning survival neural network model visualization was realized by a user-friendly graphic interface. Conclusions and Relevance:The deep learning survival neural network model shows potential benefits in prognostic evaluation and treatment recommendation with respect to lung cancer-specific survival. This novel analytical approach may provide reliable individual survival information and treatment recommendations.

SUBMITTER: She Y

PROVIDER: S-EPMC7272121 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Lung cancer is a leading cause of cancer-related death, and >80% of lung cancer diagnoses are non-small-cell lung cancer (NSCLC). However, when using current staging and prognostic indices, the prognosis can vary significantly. In the present study, we calculated a prognostic index for predicting overall survival (OS) in NSCLC patients. The data of 545 NSCLC patients were retrospectively reviewed. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of clinicopathological factors. Age (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.02-1.54), TNM stage (III, HR = 1.64, 95% CI = 1.08-2.48; IV, HR = 2.33, 95% CI = 1.48-3.69), lung lobectomy (HR = 1.96, 95% CI = 1.45-2.66), chemotherapy (HR = 1.42, 95% CI = 1.15-1.74), and pretreatment hemoglobin level (HR = 1.61, 95% CI = 1.28-2.02) were independent prognosticators. A prognostic index for NSCLC (PInscl, 0-6 points) was calculated based on age (≥65 years, 1 point), tumor-node-metastasis (TNM) stage (III, 1 point; IV, 2 points), lung lobectomy (no, 1 point), chemotherapy (no, 1 point), and pretreatment hemoglobin level (low, 1 point). In comparison with the "PInscl = 0" subgroup (survival time = 2.71 ± 1.86 years), the "PInscl = 2" subgroup (survival time = 1.86 ± 1.24 years), "PInscl = 3" subgroup (survival time = 1.45 ± 1.07 years), "PInscl = 4" subgroup (survival time = 1.17 ± 1.06 years), "PInscl = 5" subgroup (survival time = 0.81 ± 0.78 years), and "PInscl = 6" subgroup (survival time = 0.65 ± 0.56 years) exhibited significantly shorter survival times. Kaplan-Meier survival analysis showed that patients with higher PInscl scores had poorer OS than those with lower scores (log-rank test: χ2 = 155.82, P < 0.0001). The area under the curve of PInscl for predicting the 1-year OS was 0.73 (95 % CI = 0.69-0.77, P < 0.001), and the PInscl had a better diagnostic performance than the Karnofsky performance status or TNM stage (P < 0.01). In conclusion, the PInscl, which is calculated from age, TNM stage, lung lobectomy, chemotherapy, and pretreatment hemoglobin level, significantly predicted OS in NSCLC patients.

Project description:Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Project description:Importance:Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials. Objective:The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort. Design:1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots. Setting:General community. Main outcome:A nomogram model was developed to predict overall survival in NSCLC patients. Results:We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e-11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e-6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e-11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access. Conclusions:This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

Project description:BackgroundDespite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice.MethodsHere, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations.ResultsWe achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification.ConclusionsWe presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

Project description:BackgroundThe frequent recurrence of early-stage non-small-cell lung cancer (NSCLC) is generally attributable to metastatic disease undetected at complete resection. Management of such patients depends on prognostic staging to identify the individuals most likely to have occult disease. We aimed to develop and validate a practical, reliable assay that improves risk stratification compared with conventional staging.MethodsA 14-gene expression assay that uses quantitative PCR, runs on formalin-fixed paraffin-embedded tissue samples, and differentiates patients with heterogeneous statistical prognoses was developed in a cohort of 361 patients with non-squamous NSCLC resected at the University of California, San Francisco. The assay was then independently validated by the Kaiser Permanente Division of Research in a masked cohort of 433 patients with stage I non-squamous NSCLC resected at Kaiser Permanente Northern California hospitals, and on a cohort of 1006 patients with stage I-III non-squamous NSCLC resected in several leading Chinese cancer centres that are part of the China Clinical Trials Consortium (CCTC).FindingsKaplan-Meier analysis of the Kaiser validation cohort showed 5 year overall survival of 71·4% (95% CI 60·5-80·0) in low-risk, 58·3% (48·9-66·6) in intermediate-risk, and 49·2% (42·2-55·8) in high-risk patients (p(trend)=0·0003). Similar analysis of the CCTC cohort indicated 5 year overall survivals of 74·1% (66·0-80·6) in low-risk, 57·4% (48·3-65·5) in intermediate-risk, and 44·6% (40·2-48·9) in high-risk patients (p(trend)<0·0001). Multivariate analysis in both cohorts indicated that no standard clinical risk factors could account for, or provide, the prognostic information derived from tumour gene expression. The assay improved prognostic accuracy beyond National Comprehensive Cancer Network criteria for stage I high-risk tumours (p<0·0001), and differentiated low-risk, intermediate-risk, and high-risk patients within all disease stages.InterpretationOur practical, quantitative-PCR-based assay reliably identified patients with early-stage non-squamous NSCLC at high risk for mortality after surgical resection.FundingUCSF Thoracic Oncology Laboratory and Pinpoint Genomics.

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Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.

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