Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model
Show others and affiliations
2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 16504Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019. Vol. 9, article id 16504
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:umu:diva-165778DOI: 10.1038/s41598-019-52915-xISI: 000495611100096PubMedID: 31712735OAI: oai:DiVA.org:umu-165778DiVA, id: diva2:1375214
Funder
Vårdal Foundation, 2014-0044Swedish Research Council, 2016-01712Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2019-12-04Bibliographically approved

Open Access in DiVA

fulltext(3262 kB)11 downloads
File information
File name FULLTEXT01.pdfFile size 3262 kBChecksum SHA-512
2eef6a0d71b6074cd5d3eb5589f8437ab8deb00d3b568386264632181c45ca815d1dbb7e0a98693570e6dd8c4a5acad218c150374390bdd8c504748dcc4c2347
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Henriksson, RogerLehtiö, Janne
By organisation
Oncology
In the same journal
Scientific Reports
Cancer and Oncology

Search outside of DiVA

GoogleGoogle Scholar
Total: 11 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 15 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf