Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning. Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.ORCID-id: 0000-0001-5125-4682
KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.ORCID-id: 0000-0002-0442-3524
Politecn Milan, Dept Elect Informat & Bioengn, Piazza Leonardo da Vinci 42, I-20133 Milan, Italy..
Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.;Stockholm Univ, Dept Phys, SE-10691 Stockholm, Sweden..
Vise andre og tillknytning
2019 (engelsk)Inngår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, s. 58-65Artikkel i tidsskrift (Fagfellevurdert) 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 (AUROC(sALop) = 0.90 vs. AUROC(radiomic) = 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.

sted, utgiver, år, opplag, sider
ELSEVIER SCI LTD , 2019. Vol. 60, s. 58-65
Emneord [en]
Survival prediction, Treatment response, Radiomics, Tumor heterogeneity, LONG ER, 1988, BIOMETRICS, V44, P837
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-251338DOI: 10.1016/j.ejmp.2019.03.024ISI: 000464560200009PubMedID: 31000087Scopus ID: 2-s2.0-85063364742OAI: oai:DiVA.org:kth-251338DiVA, id: diva2:1317575
Merknad

QC 20190523

Tilgjengelig fra: 2019-05-23 Laget: 2019-05-23 Sist oppdatert: 2019-10-09bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstPubMedScopus

Personposter BETA

Astaraki, MehdiWang, ChunliangSmedby, Örjan

Søk i DiVA

Av forfatter/redaktør
Astaraki, MehdiWang, ChunliangSmedby, Örjan
Av organisasjonen
I samme tidsskrift
Physica medica (Testo stampato)

Søk utenfor DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric

doi
pubmed
urn-nbn
Totalt: 213 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf