Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model
| العنوان: | Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model |
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| المؤلفون: | Levitsky, Adrian, Pernemalm, Maria, Bernhardson, Britt-Marie, Forshed, Jenny, Kölbeck, Karl, Olin, Maria, Henriksson, Roger, Lehtiö, Janne, Tishelman, Carol, Eriksson, Lars E. |
| المصدر: | Scientific Reports. 9 |
| الوصف: | The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required >= 4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making. |
| وصف الملف: | electronic |
| URL الوصول: | https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-165778 https://doi.org/10.1038/s41598-019-52915-x https://umu.diva-portal.org/smash/get/diva2:1375214/FULLTEXT01.pdf |
| قاعدة البيانات: | SwePub |
| DOI: | 10.1038/s41598-019-52915-x |
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