| 1. |
- Kaboteh, Reza, et al.
(författare)
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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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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:supplement 2
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Tidskriftsartikel (refereegranskat)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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| 2. |
- Polymeri, Erini, et al.
(författare)
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Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients
- 2021
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Ingår i: Scandinavian Journal of Urology. - : Medical Journals Sweden AB. - 2168-1805 .- 2168-1813. ; 55:6, s. 427-433
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Tidskriftsartikel (refereegranskat)abstract
- Objective Artificial intelligence (AI) offers new opportunities for objective quantitative measurements of imaging biomarkers from positron-emission tomography/computed tomography (PET/CT). Clinical image reporting relies predominantly on observer-dependent visual assessment and easily accessible measures like SUVmax, representing lesion uptake in a relatively small amount of tissue. Our hypothesis is that measurements of total volume and lesion uptake of the entire tumour would better reflect the disease`s activity with prognostic significance, compared with conventional measurements. Methods An AI-based algorithm was trained to automatically measure the prostate and its tumour content in PET/CT of 145 patients. The algorithm was then tested retrospectively on 285 high-risk patients, who were examined using F-18-choline PET/CT for primary staging between April 2008 and July 2015. Prostate tumour volume, tumour fraction of the prostate gland, lesion uptake of the entire tumour, and SUVmax were obtained automatically. Associations between these measurements, age, PSA, Gleason score and prostate cancer-specific survival were studied, using a Cox proportional-hazards regression model. Results Twenty-three patients died of prostate cancer during follow-up (median survival 3.8 years). Total tumour volume of the prostate (p = 0.008), tumour fraction of the gland (p = 0.005), total lesion uptake of the prostate (p = 0.02), and age (p = 0.01) were significantly associated with disease-specific survival, whereas SUVmax (p = 0.2), PSA (p = 0.2), and Gleason score (p = 0.8) were not. Conclusion AI-based assessments of total tumour volume and lesion uptake were significantly associated with disease-specific survival in this patient cohort, whereas SUVmax and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy.
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| 3. |
- Polymeri, Erini, et al.
(författare)
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Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography
- 2024
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Ingår i: Advances in Radiation Oncology. - 2452-1094. ; 9:3
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning. Methods and Materials: Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations. Results: The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively. Conclusions: Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.
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| 4. |
- Polymeri, Erini, et al.
(författare)
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Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
- 2020
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Ingår i: Clinical Physiology and Functional Imaging. - Chichester : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 40:2, s. 106-113
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine
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| 5. |
- Sadik, May, 1970, et al.
(författare)
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Analytical validation of an automated method for segmentation of the prostate gland in CT images
- 2017
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Ingår i: 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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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.
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| 6. |
- Sadik, May, 1970, et al.
(författare)
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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
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Ingår i: Journal of Nuclear Medicine. - 0161-5505 .- 2159-662X. ; 58:supplement 1
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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.
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