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 survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
Stockholm University, Faculty of Science, Department of Physics. Karolinska Institutet, Sweden.ORCID iD: 0000-0002-7101-240X
Show others and affiliations
2019 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, p. 58-65Article in journal (Refereed) Published
Abstract [en]

Purpose

To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.

Methods

Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC).

Results

The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values.

Conclusion

A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

Place, publisher, year, edition, pages
2019. Vol. 60, p. 58-65
Keywords [en]
Survival prediction, Treatment response, Radiomics, Tumor heterogeneity
National Category
Cancer and Oncology Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-167967DOI: 10.1016/j.ejmp.2019.03.024ISI: 000464560200009OAI: oai:DiVA.org:su-167967DiVA, id: diva2:1304277
Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-05-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Astaraki, MehdiWang, ChunliangToma-Dasu, IulianaLazzeroni, MartaSmedby, Örjan
By organisation
Department of Physics
In the same journal
Physica medica (Testo stampato)
Cancer and OncologyPhysical Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 103 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