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- Borrelli, P., et al.
(author)
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AI-based detection of lung lesions in F-18 FDG PET-CT from lung cancer patients
- 2021
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In: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 8:1
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Journal article (peer-reviewed)abstract
- 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.
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| 2. |
- Ly, John, et al.
(author)
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Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
- 2021
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In: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 11:1
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Journal article (peer-reviewed)abstract
- Background: The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Methods: A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN. Results: Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest. Conclusions: AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.
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| 3. |
- Ly, John, et al.
(author)
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The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations
- 2019
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In: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 9:1
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Journal article (peer-reviewed)abstract
- Background: The Deauville score (DS) is a clinical tool, based on the comparison between lesion and reference organ uptake of 18F-fluorodeoxyglucose (FDG), used to stratify patients with lymphoma into categories reflecting their disease status. With a plethora of positron emission tomography with computed tomography (PET-CT) hard- and software algorithms, standard uptake value (SUV) in lesions and reference organs may differ which affects DS classification and therefore medical treatment. The EANM Research Ltd. (EARL) harmonization program from the European Association of Nuclear Medicine (EANM) partly mitigates this issue, but local preferences are common in clinical practice. We have investigated the discordance in DS calculated from patients with lymphoma referred for 18F-FDG PET-CT reconstructed with three different algorithms: the newly introduced block-sequential regularization expectation-maximization algorithm commercially sold as Q. Clear (QC, GE Healthcare, Milwaukee, WI, USA), compliant with the newly proposed updated EARL recommendations, and two settings compliant with the current EARL recommendations (EARLlower and EARLupper, representing the lower and upper limit of the EARL recommendations). Methods: Fifty-two patients with non-Hodgkin and Hodgkin lymphoma were included (18 females and 34 males). Segmentation of mediastinal blood pool and liver were semi-automatically performed, whereas segmentation of lesions was done manually. From these segmentations, SUVmax and SUVpeak were obtained and DS calculated. Results: There was a significant difference in DS between the QC algorithm and EARLlower/EARLupper (p < 0.0001 for both) but not between EARLlower and EARLupper (p = 0.102) when SUVmax was used. For SUVpeak, there was a significant difference between QC and EARLlower (p = 0.001), but not for QC vs EARLupper (p = 0.071) or EARLlower vs EARLupper (p = 0.102). Five non-responders (DS 4–5) for QC were classified as responders (DS 1–3) when EARLlower/EARLupper was used, both when SUVmax and SUVpeak were investigated. Conclusion: Using the proposed updated EARL recommendations compared with the current recommendations will significantly change DS classification. In select cases, the discordance would affect the choice of medical treatment. Specifically, the current EARL recommendations were more often prone to classify patients as responders.
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