| 61. |
- Reza, Mariana, et al.
(författare)
-
Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11
- 2021
-
Ingår i: European Urology Oncology. - : Elsevier BV. - 2588-9311. ; 4:1, s. 49-55
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Owing to the large variation in treatment response among patients with high-risk prostate cancer, it would be of value to use objective tools to monitor the status of bone metastases during clinical trials. Automated Bone Scan Index (aBSI) based on artificial intelligence has been proposed as an imaging biomarker for the quantification of skeletal metastases from bone scintigraphy.OBJECTIVE: To investigate how an increase in aBSI during treatment may predict clinical outcome in a randomised controlled clinical trial including patients with high-risk prostate cancer.DESIGN, SETTING, AND PARTICIPANTS: We retrospectively selected all patients from the Zometa European Study (ZEUS)/SPCG11 study with image data of sufficient quality to allow for aBSI assessment at baseline and at 48-mo follow-up. Data on aBSI were obtained using EXINIboneBSI software, blinded for clinical data and randomisation of zoledronic acid treatment. Data on age, overall survival (OS), and prostate-specific antigen (PSA) at baseline and upon follow-up were available from the study database.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Association between clinical parameters and aBSI increase during treatment was evaluated using Cox proportional-hazards regression models, Kaplan-Meier estimates, and log-rank test. Discrimination between prognostic variables was assessed using the concordance index (C-index).RESULTS AND LIMITATIONS: In this cohort, 176 patients with bone metastases and a change in aBSI from baseline to follow-up of ≤0.3 had a significantly longer median survival time than patients with an aBSI change of >0.3 (p<0.0001). The increase in aBSI was significantly associated with OS (p<0.01 and C-index=0.65), while age and PSA change were not.CONCLUSIONS: The aBSI used as an objective imaging biomarker predicted outcome in prostate cancer patients in the ZEUS/SPCG11 study. An analysis of the change in aBSI from baseline to 48-mo follow-up represents a valuable tool for prognostication and monitoring of prostate cancer patients with bone metastases.PATIENT SUMMARY: The increase in the burden of skeletal metastases, as measured by the automated Bone Scan Index (aBSI), during treatment was associated with overall survival in patients from the Zometa European Study/SPCG11 study. The aBSI may be a useful tool also in monitoring prostate cancer patients with newly developed bone metastases.
|
|
| 62. |
- Reza, Mariana, et al.
(författare)
-
Bone Scan Index as a prognostic imaging biomarker during androgen deprivation therapy.
- 2014
-
Ingår i: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 4
-
Tidskriftsartikel (refereegranskat)abstract
- Bone Scan Index (BSI) is a quantitative measurement of tumour burden in the skeleton calculated from bone scan images. When analysed at the time of diagnosis, it has been shown to provide prognostic information on survival in men with metastatic prostate cancer (PCa). In this study, we evaluated the prognostic value of BSI during androgen deprivation therapy (ADT).
|
|
| 63. |
- Sadik, May, 1970, et al.
(författare)
-
3D prostate gland uptake of 18F-choline - association with overall survival in patients with hormone-naïve prostate cancer
- 2017
-
Ingår i: Journal of Nuclear Medicine. - 0161-5505 .- 2159-662X. ; 58:supplement 1, s. 544-
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives : To develop a completely automated method to quantify prostate gland uptake of 18F-choline in PET/CT images and to study the relationship between this measure, clinical data and overall survival in patients with prostate cancer. Methods : An automated method for segmentation of the prostate gland in CT images was developed using a training group of 100 patients who had undergone PET/CT scanning. The algorithms were trained based on the manual segmentations of the prostate gland in the 100 CT scans performed by a single radiologist. A multi-atlas-based method was used applied for automated segmentation of the prostate gland. Each of a subset of the training images was registered separately to the test image. By applying the resulting transformations to the manual delineations a rough segmentation of the test image was obtained. This segmentation was refined using a random-forest classifier and the final segmentation was obtained with graph cuts. Voxels in the 18F-choline PET scans having a standard uptake value (SUV) >2.65 and localized in the prostate gland in the corresponding CT scan were defined as abnormal. Automated calculation of the following five PET measurements was performed: The maximal SUV within the prostate gland - SUVmax The average SUV within the abnormal part of the prostate gland - SUVmean The volume of abnormal uptake within the prostate gland - VOL The product SUVmean x VOL defined as Total Lesion Uptake - TLU The fraction of the prostate with abnormal uptake related to the whole volume of the prostate gland - FRAC The automated quantification method was retrospectively applied to a separate test group of 46 prostate cancer patients, aged 53-94 years, who had undergone 18F-choline PET/CT. These patients have previously been selected for a study aiming to compare whole-body bone scans, 18F-choline-PET/CT and 18F-NaF PET/CT with magnetic resonance imaging. The study entry criteria were biopsy-proven prostate cancer, a positive whole-body bone scan consistent with bone metastases, and no history of androgen deprivation. The association between the automated PET measurements, age, PSA, Gleason score and overall survival was evaluated using a univariate Cox proportional hazards regression model. Kaplan-Meier estimates were used to estimate the survival difference between patients with values above and below the median value for all variables analyzed. Results : The fraction of the prostate with abnormal uptake related to the whole volume of the prostate gland - FRAC and age were significantly associated with overall survival (Table) while PSA, Gleason score and other PET measurements were not. The patients with a FRAC above the median value (58.2%) had a significantly shorter median survival time than patients with a value below the median value (2.8 years vs. 5.5 years; p=0.04), see Figure. Conclusion : A completely automated method of quantifying 18F-choline PET uptake in the prostate gland yielded a measure of disease extent that was significantly associated with overall survival in patients with hormone-naïve prostate cancer. The method can also be applied to PET/CT scans with other tracers such as FDG or PSMA-targeted agents. It is our hope that these preliminary data will inspire further evaluation of this type of objective quantification of PET/CT scans.
|
|
| 64. |
- Sadik, May, 1970, et al.
(författare)
-
Analytical validation of an automated method for segmentation of the prostate gland in CT images
- 2017
-
Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:supplement issue 2
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 65. |
- Sadik, May, 1970, et al.
(författare)
-
Automated 3D segmentation of the prostate gland in CT images - a first step towards objective measurements of prostate uptake in PET and SPECT images
- 2017
-
Ingår i: Journal of Nuclear Medicine. - 0161-5505 .- 2159-662X. ; 58:supplement 1
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives : Uptake of PSMA-targeted tracers and choline in the prostate gland may serve as guidance for management of patients with prostate cancer. Our aim was to develop objectively measured PET/CT and SPECT/CT imaging biomarkers reflecting such uptake. In this study we took the first step by introducing and validating a completely automated algorithm for 3D-segmentation of the prostate gland in CT images. Methods : A group of 100 patients who had undergone 18F-FDG PET/CT scanning was used as training set. A single radiologist performed manual segmentations of the prostate gland in all 100 CT scans using a custom software tool. A multi-atlas-based method was used applied for automated segmentation of the prostate gland. Each of a subset of the training images was registered separately to the test image. By applying the resulting transformations to the manual delineations a rough segmentation of the test image was obtained. This segmentation was refined using a random-forest classifier and the final segmentation was obtained with graph cuts. A separate validation group comprised 46 patients (aged 53-94 years) with biopsy-proven prostate cancer, who had undergone both 18F-fluoromethylcholine PET/CT and 18F-sodiumfluoride PET/CT within a time frame of 3 weeks as part of a previous research project. A diagnostic contrast-enhanced CT scan (64-slice helical, 120 kV, ’smart mA’ maximum 400 mA) was obtained with a CT slice thickness of 3.75 mm. We speculated that the volume of the prostate gland and in particular the fraction of the gland that had abnormally high tracer accumulation, might be useful biomarkers helping to improve management and prognostication in cancer patients. The reproducibility of automated measurements of the prostate gland volume was therefore studied using the two CT scans from each patient in the validation set. Results : The automatically measured prostate gland volumes in the validation set ranged between 13 ml and 90 ml with a mean of 48 ml. The mean difference between the two volume measurements in each patient was 2.4 ml with an SD of 6.6 ml. The difference was less than 10 ml in 41 of the 46 cases. Conclusion : We have demonstrated a reproducible and automated algorithm for 3D-segmentation of the prostate gland in CT images. This is a first step towards objective measurements of prostate gland tracer uptake in PET and SPECT examinations, because PET and SPECT images alone do not allow for accurate segmentation of the prostate gland, which instead depends on proper segmentation based on the corresponding CT scans.
|
|
| 66. |
- Sadik, May, 1970, et al.
(författare)
-
Automated evaluation of normal uptake in different skeletal parts using 18F-sodium fluoride (NaF) PET/CT using a new convolutional neural network method
- 2017
-
Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:Supplement 2, s. 479-479
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 67. |
- Sadik, May, 1970, et al.
(författare)
-
Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas
- 2019
-
Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 39:1, s. 78-84
-
Tidskriftsartikel (refereegranskat)abstract
- Background 18F-FDG-PET/CT has become a standard for assessing treatment response in patients with lymphoma. A subjective interpretation of the scan based on the Deauville 5-point scale has been widely adopted. However, inter-observer variability due to the subjectivity of the interpretation is a limitation. Our main goal is to develop an objective and automated method for evaluating response. The first step is to develop and validate an artificial intelligence (AI)-based method, for the automated quantification of reference levels in the liver and mediastinal blood pool in patients with lymphoma. Methods Results The AI-based method was trained to segment the liver and the mediastinal blood pool in CT images from 80 lymphoma patients, who had undergone 18F-FDG-PET/CT, and apply this to a validation group of six lymphoma patients. CT segmentations were transferred to the PET images to obtain automatic standardized uptake values (SUV). The AI-based analysis was compared to corresponding manual segmentations performed by two radiologists. The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0 center dot 02 and 0 center dot 02, respectively, and 0 center dot 02 and 0 center dot 02 for mediastinal blood pool respectively. Conclusions An AI-based method for the automated quantification of reference levels in the liver and mediastinal blood pool shows good agreement with results obtained by experienced radiologists who had manually segmented the CT images. This is a first, promising step towards objective treatment response evaluation in patients with lymphoma based on 18F-FDG-PET/CT.
|
|
| 68. |
- Sadik, May, 1970, et al.
(författare)
-
Convolutional neural networks for segmentation of 49 selected bones in CT images show high reproducibility
- 2017
-
Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:Supplement 2
-
Tidskriftsartikel (refereegranskat)abstract
- Aim : An automated method to calculate Bone Scan Index (BSI) from bone scans has recently been established as a first imaging biomarker in patients with metastatic prostate cancer. BSI has shown to be an independent predictor of survival. PET/CT is more accurate than bone scans in detecting bone metastases. We therefore decided to develop an automated PET/CT based imaging biomarker for assessment of tumor burden in bone. The aim of this project was to develop a method for automated segmentation and volume calculation of bones in CT images, which is the first step in the process of developing a PET/CT based imaging biomarker. Materials and Methods : Convolutional neural networks (CNN) were trained to segment 49 selected bones (12 thoracic vertebrae, 5 lumbar vertebrae, sacrum, 2 hip bones, 24 ribs, 2 scapulae, 2 clavicles and the sternum) using manual segmentations in CT images from 23 patients performed by experienced image readers. Anatomical landmarks were detected using a CNN and pruned using a shape model. These landmarks and the CT image were fed to a second CNN, segmenting the 49 selected bones. After the training process, the CNN segmented the bones in CT images in a separate validation group consisting of 46 patients with prostate cancer. All patients had undergone both 18F-Choline and 18F-NaF PET/CT within a time frame of 3 weeks as part of a previous research project. The two CT scans from each patient were segmented by the CNN and the two volumes of each bone were calculated. Results : The total volume of the 49 bones was on average 3,086 mL in the 46 patients. The individual bones ranged in volume from 8 mL (left 12th rib) to 440 mL (left hip bone). The reproducibility measured as ratio volume difference/mean volume was on average less than 2% for all bones except for the ribs. The mean volumes, differences and reproducibility for the bones of five anatomical regions were as follow: thoracic vertebrae 39mL, 0.6mL, 1.5%; lumbar vertebra 71mL, 0.8 mL, 1.2%; sacrum, hip bones 386mL, 0.9mL, 0.3%; ribs 26mL, 2.0mL, 8.5%; scapulae, clavicles, sternum 97mL, -0.1mL, -0.4%. Conclusion : Our CNN based method for automated segmentation of bones in CT images showed high reproducibility. A reproducible way to segment the skeleton and to measure the bone volume will be important in the development of a PET index relating volumes of abnormal PET tracer uptake to the bone volume.
|
|
| 69. |
- Sadik, May, 1970, et al.
(författare)
-
Metabolic tumour volume in Hodgkin lymphoma - A comparison between manual and AI-based analysis
- 2024
-
Ingår i: Clinical Physiology and Functional Imaging. - 1475-0961 .- 1475-097X. ; 44:3, s. 220-227
-
Tidskriftsartikel (refereegranskat)abstract
- Aim: To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference. Methods: Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7–75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists. Results: The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79–568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10–86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively. Conclusion: The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.
|
|
| 70. |
- Sadik, May, 1970, et al.
(författare)
-
Variability in reference levels for Deauville classifications applied to lymphoma patients examined with 18F-FDG-PET/CT
- 2017
-
Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44
-
Tidskriftsartikel (refereegranskat)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.
|
|
