Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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 tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems. Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy..
Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, Med Radiat Phys, S-17176 Solna, Sweden..
Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, Med Radiat Phys, S-17176 Solna, Sweden..
Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy..
Show others and affiliations
2018 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, p. 21-29Article in journal (Refereed) Published
Abstract [en]

Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2018. Vol. 54, p. 21-29
Keywords [en]
Early tumor response, Feature extraction, Non-small cell lung cancer, PET/CT
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-238132DOI: 10.1016/j.ejmp.2018.09.003ISI: 000447271300003PubMedID: 30337006Scopus ID: 2-s2.0-85053799575OAI: oai:DiVA.org:kth-238132DiVA, id: diva2:1262931
Funder
Swedish Childhood Cancer Foundation, MT2016-0016The Swedish Brain FoundationEU, FP7, Seventh Framework Programme, ARTFORCEEU, European Research Council, ERC-ADG-2015EU, Horizon 2020, 733008
Note

QC 20181113

Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2018-11-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Buizza, GiuliaChang, Yong JunSmedby, ÖrjanWang, Chunliang

Search in DiVA

By author/editor
Buizza, GiuliaChang, Yong JunSmedby, ÖrjanWang, Chunliang
By organisation
Biomedical Engineering and Health SystemsMedical Imaging
In the same journal
Physica medica (Testo stampato)
Radiology, Nuclear Medicine and Medical Imaging

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 224 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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