Deep Learning Based Long Term Mortality Prediction in the National Lung Screening Trial

In this study, the long-term mortality in the National Lung Screening Trial (NLST) was investigated using a deep learning-based method. Binary classification of the non-lung-cancer mortality (i.e. cardiovascular and respiratory mortality) was performed using neural network models centered around a 3D-ResNet. The models were trained on a participant age, gender, and smoking history matched cohort. Utilising both the 3D CT scan and clinical information, the models can achieve an AUC of 0.73 which outperforms humans at cardiovascular mortality prediction. By interpreting the trained models with 3D saliency maps, we examined the features on the CT scans that correspond to the mortality signal. The saliency maps can potentially assist the clinicians' and radiologists' to identify regions of concern on the image that may indicate the need to adopt preventative healthcare management strategies to prolong the patients' life expectancy.

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The National Lung Screening Trial: Results stratified by demographics, smoking history, and lung cancer histology

Cancer ◽

10.1002/cncr.28326 ◽

2013 ◽

Vol 119 (22) ◽

pp. 3976-3983 ◽

Cited By ~ 123

Author(s):

Paul F. Pinsky ◽

Timothy R. Church ◽

Grant Izmirlian ◽

Barnett S. Kramer

Keyword(s):

Lung Cancer ◽

Smoking History ◽

National Lung Screening Trial ◽

Lung Screening ◽

Screening Trial

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Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx012 ◽

2017 ◽

Vol 24 (6) ◽

pp. 1046-1051 ◽

Cited By ~ 1

Author(s):

Jason M Hostetter ◽

James J Morrison ◽

Michael Morris ◽

Jean Jeudy ◽

Kenneth C Wang ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Lung Cancer Risk ◽

Smoking History ◽

Malignancy Risk ◽

National Lung Screening Trial ◽

Risk Of Malignancy ◽

Lung Screening ◽

Screening Trial

Abstract Objective To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. Materials and Methods An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. Results Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. Discussion Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. Conclusion By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made.

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Long-term Outcomes After Lung Cancer Resection in Smokers: Analysis of the National Lung Screening Trial

World Journal of Surgery ◽

10.1007/s00268-021-06311-0 ◽

2021 ◽

Author(s):

Joanna Sesti ◽

Jonathan Decker ◽

Jaimie Bell ◽

Andrew Nguyen ◽

Adam Lackey ◽

...

Keyword(s):

Lung Cancer ◽

Long Term Outcomes ◽

Cancer Resection ◽

National Lung Screening Trial ◽

Lung Screening ◽

Screening Trial

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Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach

Diagnostics ◽

10.3390/diagnostics11061060 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1060

Author(s):

Yu-Hsuan Li ◽

Wayne Huey-Herng Sheu ◽

Wen-Chao Yeh ◽

Yung-Chun Chang ◽

I-Te Lee

Keyword(s):

Machine Learning ◽

Obstructive Coronary Artery Disease ◽

Mortality Prediction ◽

Smoking History ◽

Brier Score ◽

Oral Glucose ◽

Angiotensin Ii Receptor ◽

Discriminative Ability ◽

Long Term Mortality

We aimed to develop and validate a model for predicting mortality in patients with angina across the spectrum of dysglycemia. A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality served as the primary endpoint. The models were validated with five-fold cross validation to predict long-term mortality. The features selected by least absolute shrinkage and selection operator (LASSO) were age, heart rate, plasma glucose levels at 30 min and 120 min during an oral glucose tolerance test (OGTT), the use of angiotensin II receptor blockers, the use of diuretics, and smoking history. This best performing model was built using a random survival forest with selected features. It had a good discriminative ability (Harrell’s C-index: 0.829) and acceptable calibration (Brier score: 0.08) for predicting long-term mortality. Among patients with obstructive coronary artery disease confirmed by angiography, our model outperformed the Global Registry of Acute Coronary Events discharge score for mortality prediction (Harrell’s C-index: 0.829 vs. 0.739, p < 0.001). In conclusion, we developed a machine learning model to predict long-term mortality among patients with angina. With the integration of OGTT, the model could help to identify a high risk of mortality across the spectrum of dysglycemia.

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