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Freely available co...
Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer
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- Borrelli, P. (författare)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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- Góngora, J. L. L. (författare)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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- Kaboteh, R. (författare)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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- Ulén, Johannes (författare)
- Eigenvision AB
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- Enqvist, Olof, 1981 (författare)
- Chalmers University of Technology,Eigenvision AB
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- Trägårdh, Elin (författare)
- 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 Hospital
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- Edenbrandt, Lars, 1957 (författare)
- 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 Academy,Sahlgrenska University Hospital
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(creator_code:org_t)
- 2022-02-03
- 2022
- Engelska.
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Ingår i: EJNMMI Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 9:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://research.cha...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- Background: Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data. Purpose: We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [18F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer. A secondary aim was to make the method available to other researchers. Methods: A total of 320 consecutive patients referred for FDG PET/CT due to suspected lung cancer were retrospectively selected for this study. Two nuclear medicine specialists manually segmented abnormal FDG uptake in all of the PET/CT studies. One-third of the patients were assigned to a test group. Survival data were collected for this group. The CNN was trained to segment lung tumours and thoracic lymph nodes. Total lesion glycolysis (TLG) was calculated from the CNN-based and manual segmentations. Associations between TLG and OS were investigated using a univariate Cox proportional hazards regression model. Results: The test group comprised 106 patients (median age, 76years (IQR 61–79); n = 59 female). Both CNN-based TLG (hazard ratio 1.64, 95% confidence interval 1.21–2.21; p = 0.001) and manual TLG (hazard ratio 1.54, 95% confidence interval 1.14–2.07; p = 0.004) estimations were significantly associated with OS. Conclusion: Fully automated CNN-based TLG measurements of PET/CT data showed were significantly associated with OS in patients with lung cancer. This type of measurement may be of value for the management of future patients with lung cancer. The CNN is publicly available for research purposes. © 2022, The Author(s).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Lungmedicin och allergi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Respiratory Medicine and Allergy (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Computer-assisted analysis
- Prognosis
- Total lesion glycolysis
- Tumour burden
- fluorodeoxyglucose f 18
- adult
- aged
- Article
- artificial intelligence
- automation
- cancer classification
- cancer survival
- controlled study
- convolutional neural network
- disease association
- disease burden
- drug uptake
- female
- glycolysis
- human
- image analysis
- limit of quantitation
- lung cancer
- lymph node
- major clinical study
- male
- overall survival
- positron emission tomography-computed tomography
- retrospective study
- survival time
- thoracic lymph node
- Total lesion glycolysis
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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