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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00006677naa a2200673 4500
001oai:gup.ub.gu.se/303884
003SwePub
008240528s2021 | |||||||||||000 ||eng|
009oai:research.chalmers.se:103b8905-6065-472e-a461-4441b0e14e63
009oai:lup.lub.lu.se:64bca0a6-ae9f-46dc-81b5-2b650deb861d
0247 a https://gup.ub.gu.se/publication/3038842 URI
0247 a https://doi.org/10.1186/s40658-021-00376-52 DOI
0247 a https://research.chalmers.se/publication/5232772 URI
0247 a https://lup.lub.lu.se/record/64bca0a6-ae9f-46dc-81b5-2b650deb861d2 URI
040 a (SwePub)gud (SwePub)cthd (SwePub)lu
041 a eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Borrelli, P.u Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital4 aut
2451 0a AI-based detection of lung lesions in F-18 FDG PET-CT from lung cancer patients
264 c 2021-03-25
264 1b Springer Science and Business Media LLC,c 2021
520 a Background[F-18]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT.MethodsOne hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.ResultsThe AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R-2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from -736 to 819 g. Agreement was particularly high in smaller lesions.ConclusionsThe AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Klinisk laboratoriemedicin0 (SwePub)302232 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Clinical Laboratory Medicine0 (SwePub)302232 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
650 7a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng
653 a AI
653 a FDG
653 a PET-CT
653 a Lung cancer
653 a Segmentation
653 a Automatic
653 a Total lesion
653 a glycolysis
653 a Radiology
653 a Nuclear Medicine & Medical Imaging
653 a AI
653 a Total lesion glycolysis
7001 a Ly, Johnu Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,Nuclear medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine,Central Hospital Kristianstad4 aut0 (Swepub:lu)med-jnl
7001 a Kaboteh, R.u Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital4 aut
7001 a Ulén, Johannesu Eigenvision AB4 aut0 (Swepub:lu)math-jsu
7001 a Enqvist, Olof,d 1981u Chalmers University of Technology,Eigenvision AB4 aut0 (Swepub:cth)enolof
7001 a Trägårdh, Elinu Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Nuclear medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital4 aut0 (Swepub:lu)klin-etr
7001 a Edenbrandt, Lars,d 1957u Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital4 aut0 (Swepub:lu)klfy-led
7102 a Sahlgrenska universitetssjukhusetb Sahlgrenska University Hospital4 org
7730 t Ejnmmi Physicsd : Springer Science and Business Media LLCg 8:1q 8:1x 2197-7364
8564 u https://ejnmmiphys.springeropen.com/track/pdf/10.1186/s40658-021-00376-5
8564 u https://research.chalmers.se/publication/523277/file/523277_Fulltext.pdfx primaryx freey FULLTEXT
8564 u http://dx.doi.org/10.1186/s40658-021-00376-5x freey FULLTEXT
8564 8u https://gup.ub.gu.se/publication/303884
8564 8u https://doi.org/10.1186/s40658-021-00376-5
8564 8u https://research.chalmers.se/publication/523277
8564 8u https://lup.lub.lu.se/record/64bca0a6-ae9f-46dc-81b5-2b650deb861d

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