Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method

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Fältnamn	Indikatorer	Metadata
000		04521naa a2200541 4500
001		oai:DiVA.org:su-167967
003		SwePub
008		190411s2019 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
009		oai:DiVA.org:kth-296808
009		oai:prod.swepub.kib.ki.se:140721717
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1679672 URI
024	7	a https://doi.org/10.1016/j.ejmp.2019.03.0242 DOI
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2968082 URI
024	7	a http://kipublications.ki.se/Default.aspx?queryparsed=id:1407217172 URI
040		a (SwePub)sud (SwePub)kthd (SwePub)ki
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Astaraki, Mehdi,c PhD Student,d 1984-u KTH,Medicinsk avbildning,Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden4 aut0 (Swepub:kth)u1usc61v
245	1 0	a Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
264	1	b Elsevier BV,c 2019
338		a print2 rdacarrier
500		a QC 20220405
520		a 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.
650	7	a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe
650	7	a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng
650	7	a NATURVETENSKAPx Fysik0 (SwePub)1032 hsv//swe
650	7	a NATURAL SCIENCESx Physical Sciences0 (SwePub)1032 hsv//eng
650	7	a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng
653		a Survival prediction
653		a Treatment response
653		a Radiomics
653		a Tumor heterogeneity
653		a Medicinsk teknologi
700	1	a Wang, Chunliang,d 1980-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1tbkeej
700	1	a Buizza, Giulia4 aut
700	1	a Toma-Dasu, Iulianau Stockholms universitet,Fysikum,Karolinska Institutet, Sweden4 aut0 (Swepub:su)iuda0736
700	1	a Lazzeroni, Martau Stockholms universitet,Fysikum,Karolinska Institutet, Sweden4 aut0 (Swepub:su)mala6377
700	1	a Smedby, Örjan,c Professor,d 1956-u KTH,Medicinsk avbildning4 aut0 (Swepub:kth)u1vc2uzb
710	2	a KTHb Medicinsk avbildning4 org
773	0	t Physica medica (Testo stampato)d : Elsevier BVg 60, s. 58-65q 60<58-65x 1120-1797x 1724-191X
856	4	u https://www.sciencedirect.com/science/article/pii/S1120179719300687
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-167967
856	4 8	u https://doi.org/10.1016/j.ejmp.2019.03.024
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296808
856	4 8	u http://kipublications.ki.se/Default.aspx?queryparsed=id:140721717

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Av författaren/redakt...: Astaraki, Mehdi, ...; Wang, Chunliang, ...; Buizza, Giulia; Toma-Dasu, Iulia ...; Lazzeroni, Marta; Smedby, Örjan, P ...

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MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Klinisk medicin; och Cancer och onkol ...

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