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- Kaboteh, Reza, et al.
(author)
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Convolutional neural network based quantification of choline uptake in PET/CT studies is associated with overall survival in patients with prostate cancer
- 2017
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In: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:supplement 2
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Journal article (peer-reviewed)abstract
- Aim : To develop a convolutional neural network (CNN) based automated method for quantification of 18F-choline uptake in the prostate gland in PET/CT studies and to study the association between this measure, clinical data and overall survival in patients with prostate cancer. Methods : A CNN was trained to segment the prostate gland in CT images using manual segmentations performed by a radiologist in a group of 100 patients, who had undergone 18F-FDG PET/CT. After the training process, the CNN automatically segmented the prostate gland in the CT images and SUV values in the corresponding PET images were automatically analyzed in a separate validation group consisting of 45 patients with biopsy-proven hormone-naïve prostate cancer. All patients had undergone an 18F-choline PET/CT as part of a previous research project. Voxels localized in the prostate gland and having a SUV >2.65 were defined as abnormal, as proposed by Reske S et al. (2006). Automated calculation of the following five PET measurements was performed: maximal SUV within the prostate gland - SUVmax; average SUV for voxels with SUV >2.65 - SUVmean; volume of voxels with SUV >2.65 - VOL; fraction of VOL related to the whole volume of the prostate gland - FRAC; product SUVmean x FRAC defined as Total Lesion Uptake - TLU. The association between the automated PET measurements, age, PSA, Gleason score and overall survival (OS) was evaluated using a univariate Cox proportional hazards regression model. Kaplan-Meier analysis was used to estimate the survival difference (log-rank test). Results : TLU and FRAC were significantly associated with OS in the Cox analysis while the other three PET measurements; age, PSA and Gleason score were not. Kaplan-Meier analysis showed that patients with SUVmax <5.3, SUVmean <3.5 and TLU <1 showed significantly longer survival times than patients with values higher than these thresholds. No significant differences were found when patients were stratified based on the other two PET measurements, PSA or Gleason score. Conclusion : Measurements reflecting 18F-choline PET uptake in the prostate gland obtained using a completely automated method were significantly associated with OS in patients with hormone-naïve prostate cancer. This type of objective quantification of PET/CT studies could be of value also for other PET tracers and other cancers in the future.
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- Lind, Erica, et al.
(author)
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Automated quantification of reference levels in liver and mediastinum (blood pool) for the Deauville therapy response classification using FDG-PET/CT in lymphoma patients
- 2017
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In: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:supplement 2
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Journal article (peer-reviewed)abstract
- Aim : To develop and validate a convolutional neural network (CNN) based method for automated quantification of reference levels in liver and mediastinum (blood pool) for the Deauville therapy response classification using FDG-PET/CT in lymphoma patients. Methods : CNNs were trained to segment the liver and the mediastinum, defined as the thoracic part of the aorta, in CT images from 81 consecutive lymphoma patients, who had undergone FDG-PET/CT examinations. Trained image readers segmented the liver and aorta manually in each of the CT images and these segmentations together with the CT images were used to train the CNN. After the training process, the CNN method was applied to a separate validation group consisting of six consecutive lymphoma patients (17-82 years, 3 female). First, the liver and mediastinum were automatically segmented in the CT images. Second, voxels in the corresponding FDG-PET images, which were localized in the liver and mediastinum, were selected and the median standard uptake value (SUV) was calculated. The CNN based analysis was compared to corresponding manual segmentations by two experienced radiologists. The Dice index was used to analyse the overlap between the segmentations by the CNN and the two radiologists. A Dice index of 1.00 indicates perfect matching. Results : The mean Dice indices for the comparison between CNN based liver segmentations and those of the two radiologists in the validation group were 0.95 and 0.95. A corresponding comparison between the two radiologists also resulted in a Dice index of 0.95. The mean liver volumes were 1,752ml, 1,757ml and 1,768ml for the CNN and two radiologists, respectively. The median SUV for the liver was on average 1.8 and the differences between median SUV based on CNN and manual segmentations were less or equal to 0.1. The mean Dice indices for the mediastinum were 0.80, 0.83 (CNN vs radiologists) and 0.86 (comparing the two radiologists). The mean mediastinum (aorta) volumes were 147ml, 140ml and 125ml for the CNN and two radiologists, respectively. The median SUV for the mediastinum was on average 1.4 and the differences between median SUV based on CNN and manual segmentations were less or equal to 0.14. Conclusion : A CNN based method for automated quantification of reference levels in liver and mediastinum show good agreement with results obtained by experienced radiologists, who manually segmented the CT images. This is a first and promising step towards a completely objective treatment response evaluation in patients with lymphoma based on FDG-PET/CT.
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- Sachpekidis, C., et al.
(author)
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Application of an artificial intelligence-based tool in F-18 FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma
- 2023
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In: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 50:12, s. 3697-3708
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Journal article (peer-reviewed)abstract
- Purpose[F-18]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients.Materials and methodsWhole-body [F-18]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [F-18]FDG-avid lesions as well as the degree of diffuse [F-18]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1-6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian x 1.1 (axial skeleton), gluteal muscles SUVmedian x 4 (extremities). Approach 2: liver SUVmedian x 1.5 (axial skeleton), gluteal muscles SUVmedian x 4 (extremities). Approach 3: liver SUVmedian x 2 (axial skeleton), gluteal muscles SUVmedian x 4 (extremities). Approach 4: & GE; 2.5. Approach 5: & GE; 2.5 (axial skeleton), & GE; 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients.ResultsBM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [F-18]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of & beta;2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5.ConclusionsThe automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.
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- Sachpekidis, Christos, et al.
(author)
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Artificial intelligence–based, volumetric assessment of the bone marrow metabolic activity in [ 18 F]FDG PET/CT predicts survival in multiple myeloma
- 2024
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In: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; In Press
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Journal article (peer-reviewed)abstract
- Purpose: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)–based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. Materials and methods: Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients’ progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. Results: Median follow-up [95% CI] of the patient cohort was 110 months [105–123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. Conclusions: The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.
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| 9. |
- Sadik, May, 1970, et al.
(author)
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Analytical validation of an automated method for segmentation of the prostate gland in CT images
- 2017
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In: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:supplement issue 2
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Journal article (peer-reviewed)abstract
- Aim : Uptake of PET tracers in the prostate gland may serve as guidance for management of patients with prostate cancer. PET studies alone do, however, not allow for accurate segmentation of the gland, instead the corresponding CT images contain the required anatomical information. Our long-term aim is to develop an objectively measured PET/CT imaging biomarker reflecting PET tracer uptake. In this study we take the first step and develop and validate a completely automated method for 3D-segmentation of the prostate gland in CT images. Methods : A convolutional neural network (CNN) was trained to segment the prostate gland in CT images using manual segmentations performed by a radiologist in a group of 100 patients, who had undergone 18F-FDG PET/CT. After the training process, the CNN automatically segmented the prostate gland in CT images in a separate validation group consisting of 45 patients with prostate cancer. All patients had undergone a 18F-choline PET/CT as part of a previous research project. The CNN segmentations were compared to manual segmentations performed independently by two radiologists. The volume of the prostate gland was calculated based on segmentations by the CNN and radiologists. The Sørensen-Dice index was used to analyse the overlap between the segmentations by the CNN and the two radiologists. Results : The prostate volumes were on average 79mL (range 9-212mL) in the 45 patients, measured as mean volumes for the two radiologists. The mean difference in prostate volumes between the two radiologists was 14mL (SD 29mL). The mean volume difference between the CNN segmentation and the mean values from the two radiologists was 22mL (SD 43mL). For the subgroup of patients with prostate volumes <100 mL (n=36), the difference between the radiologists was 9mL (SD 11mL) compared to difference CNN vs radiologists of 7mL (SD 15mL). The Sørensen-Dice index was 0.69 and 0.70 for the comparison between CNN segmentation and the two radiologists, respectively and 0.83 for the comparison between the two radiologists. The corresponding Sørensen-Dice index in the 36 patients with volumes <100 mL were 0.74, 0.75 and 0.83, respectively Conclusion : Our CNN based method for automated segmentation of the prostate gland in CT images show good agreement with the corresponding manual segmentations by two radiologists especially for prostade glands with a volume less than 100 mL.
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| 10. |
- Sadik, May, 1970, et al.
(author)
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Automated evaluation of normal uptake in different skeletal parts using 18F-sodium fluoride (NaF) PET/CT using a new convolutional neural network method
- 2017
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In: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:Supplement 2, s. 479-479
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Journal article (peer-reviewed)abstract
- Introduction : Understanding normal skeletal uptake of 18F-sodium fluoride (18F-NaF) in positron emission tomography/computed tomography (PET/CT) is important for clinical interpretation. Quantification of tracer uptake in PET/CT is often performed by placing a volume of interest (VOI) to measure standard uptake values (SUVs). Manual placement of this VOI requires a subjective decision and can only measure uptake in a specific part of the bone. The aim of this study was to investigate normal 18F-NaF skeletal activity in patients with prostate cancer at a stage of the disease prior to development of bone metastases, by using a new method that quantifies uptake in entire skeletal parts. Material and Methods : Patients with biopsy-verified high-risk prostate cancer and a negative or inconclusive bone scintigraphy and no metastatic lesions on 18F-NaF PET/CT (performed March 2008 - June 2010) were retrospectively included (n=48). Whole-body PET scans were acquired 1-1.5 h after i.v. injection of 4 MBq/kg 18F-NaF (max 400 MBq). CT scans were obtained immediately after the PET scan. Thoracic and lumbar vertebrae, sacrum, pelvis, ribs, scapulae, clavicles and sternum were automatically segmented in the CT images, using a method based on a convolutional neural network, to obtain the volume of each skeletal region. The network was trained using a separate group of CT scans with manual segmentations. Mean and maximum SUV (SUVmean and SUVmax) were subsequently measured for each skeletal part in the PET scans. Results : Average (SD) SUVmean for the skeletal regions were: Thoracic vertebrae 0.98 (0.20), lumbar vertebrae 0.96 (0.19), sacrum 0.75 (0.15), pelvis 0.73 (0.16), ribs 0.41 (0.11), scapulae 0.46 (0.11), clavicles 0.50 (0.16) and sternum 0.61 (0.13). Average (SD) SUVmax for the skeletal regions were: Thoracic vertebrae 1.95 (0.66), lumbar vertebrae 2.10 (0.78), sacrum 2.22 (0.77), pelvis 1.99 (0.82), ribs 1.19 (0.35), scapulae 1.94 (0.98), clavicles 2.00 (1.03) and sternum 1.68 (0.44). Conclusion : We present a new method to segment and quantify uptake in skeletal regions in 18F-NaF PET/CT. Various parts of the bone have different SUVs in patients with regional prostate cancer. Vertebrae and pelvis have higher SUVs than ribs. The highest SUVmax were found in the thoracic and lumbar vertebrae. The findings are of importance for interpretation of 18F-NaF PET/CT.
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