WFRF:(Wang Chunliang 1980 )
Sökning: WFRF:(Wang Chunliang 1980 ) > Early survival pred...
- Astaraki, Mehdi,PhD Student,1984-KTH,Medicinsk avbildning,Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden (författare)
Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
- Artikel/kapitelEngelska2019
Förlag, utgivningsår, omfång ...
- Elsevier BV,2019
- printrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:su-167967
- https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-167967URI
- https://doi.org/10.1016/j.ejmp.2019.03.024DOI
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296808URI
- http://kipublications.ki.se/Default.aspx?queryparsed=id:140721717URI
Kompletterande språkuppgifter
- Språk:engelska
- Sammanfattning på:engelska
Ingår i deldatabas
- SwePubSwePub
Klassifikation
Anmärkningar
- QC 20220405
- PurposeTo 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.MethodsLongitudinal 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).ResultsThe 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.ConclusionA 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.
Ämnesord och genrebeteckningar
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
- NATURVETENSKAP Fysik hsv//swe
- NATURAL SCIENCES Physical Sciences hsv//eng
- TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
- ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
- Survival prediction
- Treatment response
- Radiomics
- Tumor heterogeneity
- Medicinsk teknologi
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Wang, Chunliang,1980-KTH,Medicinsk avbildning(Swepub:kth)u1tbkeej (författare)
- Buizza, Giulia (författare)
- Toma-Dasu, IulianaStockholms universitet,Fysikum,Karolinska Institutet, Sweden(Swepub:su)iuda0736 (författare)
- Lazzeroni, MartaStockholms universitet,Fysikum,Karolinska Institutet, Sweden(Swepub:su)mala6377 (författare)
- Smedby, Örjan,Professor,1956-KTH,Medicinsk avbildning(Swepub:kth)u1vc2uzb (författare)
- KTHMedicinsk avbildning (creator_code:org_t)
Sammanhörande titlar
- Ingår i:Physica medica (Testo stampato): Elsevier BV60, s. 58-651120-17971724-191X
Internetlänk
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- Physica medica (Testo stampato) (Sök värdpublikationen i LIBRIS)
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