Publications by authors named "Lars Edenbrandt"

100 Publications

Convolutional neural network-based automatic heart segmentation and quantitation in I-metaiodobenzylguanidine SPECT imaging.

EJNMMI Res 2021 Oct 12;11(1):105. Epub 2021 Oct 12.

Department of Nuclear Medicine, Kanazawa University, Kanazawa, Japan.

Background: Since three-dimensional segmentation of cardiac region in I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging.

Methods: We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them.

Results: The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods.

Conclusions: The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation.
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http://dx.doi.org/10.1186/s13550-021-00847-xDOI Listing
October 2021

Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis.

Clin Imaging 2021 Aug 26;81:54-59. Epub 2021 Aug 26.

Göteborg University, SU Sahlgrenska, 413 45 Göteborg, Sweden.

Background: Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecular attenuation values for opportunistic osteoporosis screening. Meanwhile, machine learning has been used to accurately segment large parts of the human skeleton.

Purpose: To evaluate a fully automated deep learning-based method for lumbar vertebral segmentation and measurement of vertebral volumetric trabecular attenuation values.

Material And Methods: A deep learning-based method for automated segmentation of bones was retrospectively applied to non-contrast CT scans of 1008 patients (mean age 57 years, 472 female, 536 male). Each vertebral segmentation was automatically reduced by 7 mm in all directions in order to avoid cortical bone. The mean and median volumetric attenuation values from Th12 to L4 were obtained and plotted against patient age and sex. L1 values were further analyzed to facilitate comparison with previous studies.

Results: The mean L1 attenuation values decreased linearly with age by -2.2 HU per year (age > 30, 95% CI: -2.4, -2.0, R = 0.3544). The mean L1 attenuation value of the entire population cohort was 140 HU ± 54.

Conclusions: With results closely matching those of previous studies, we believe that our fully automated deep learning-based method can be used to obtain lumbar volumetric trabecular attenuation values which can be used for opportunistic screening of osteoporosis in patients undergoing CT scans for other reasons.
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http://dx.doi.org/10.1016/j.clinimag.2021.08.009DOI Listing
August 2021

Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients.

Scand J Urol 2021 Sep 25:1-7. Epub 2021 Sep 25.

Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.

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 SUV, 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-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 SUV 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 ( = 0.008), tumour fraction of the gland ( = 0.005), total lesion uptake of the prostate ( = 0.02), and age ( = 0.01) were significantly associated with disease-specific survival, whereas SUV ( = 0.2), PSA ( = 0.2), and Gleason score ( = 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 SUV 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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http://dx.doi.org/10.1080/21681805.2021.1977845DOI Listing
September 2021

Alavi-Carlsen Calcification Score (ACCS): A Simple Measure of Global Cardiac Atherosclerosis Burden.

Diagnostics (Basel) 2021 Aug 5;11(8). Epub 2021 Aug 5.

Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark.

Multislice cardiac CT characterizes late stage macrocalcification in epicardial arteries as opposed to PET/CT, which mirrors early phase arterial wall changes in epicardial and transmural coronary arteries. With regard to tracer, there has been a shift from using mainly F-fluorodeoxyglucose (FDG), indicating inflammation, to applying predominantly F-sodium fluoride (NaF) due to its high affinity for arterial wall microcalcification and more consistent association with cardiovascular risk factors. To make NaF-PET/CT an indispensable adjunct to clinical assessment of cardiac atherosclerosis, the Alavi-Carlsen Calcification Score (ACCS) has been proposed. It constitutes a global assessment of cardiac atherosclerosis burden in the individual patient, supported by an artificial intelligence (AI)-based approach for fast observer-independent segmentation. Common measures for characterizing epicardial coronary atherosclerosis by NaF-PET/CT as the maximum standardized uptake value (SUV) or target-to-background ratio are more versatile, error prone, and less reproducible than the ACCS, which equals the average cardiac SUV. The AI-based approach ensures a quick and easy delineation of the entire heart in 3D to obtain the ACCS expressing ongoing global cardiac atherosclerosis, even before it gives rise to CT-detectable coronary calcification. The quantification of global cardiac atherosclerotic burden by the ACCS is suited for management triage and monitoring of disease progression with and without intervention.
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http://dx.doi.org/10.3390/diagnostics11081421DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391812PMC
August 2021

"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison.

J Nucl Cardiol 2021 Aug 12. Epub 2021 Aug 12.

Department of Nuclear Medicine, Odense University Hospital, 5000, Odense C, Denmark.

Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.

Methods: A trained convolutional neural network (CNN) was used for cardiac segmentation in F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans.

Results: Mean (± SD) values with manual vs. CNN-based segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P = .21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P = .26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P = .34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators.

Conclusion: The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.
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http://dx.doi.org/10.1007/s12350-021-02758-9DOI Listing
August 2021

Aortic wall segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation.

J Nucl Cardiol 2021 May 12. Epub 2021 May 12.

Department of Nuclear Medicine, Odense University Hospital, 5000, Odense, Denmark.

Background: We aimed to establish and test an automated AI-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans.

Methods: For segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on F-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by manual segmentation. The following derived parameters were compared using Bland-Altman Limits of Agreement: segmented volume, and maximal, mean, and total standardized uptake values (SUVmax, SUVmean, SUVtotal). The repeatability of the manual method was examined in 25 randomly selected scans.

Results: CNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat manual segmentation was 6%.

Conclusions: The automated CNN-based approach was much faster and provided parameters that were about 15% lower than the manually obtained values, except for SUVmean values, which were comparable. AI-based segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome manual segmentation.
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http://dx.doi.org/10.1007/s12350-021-02649-zDOI Listing
May 2021

AI-based detection of lung lesions in [F]FDG PET-CT from lung cancer patients.

EJNMMI Phys 2021 Mar 25;8(1):32. Epub 2021 Mar 25.

Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.

Background: [F]-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.

Methods: One 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.

Results: The 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 = 0.74). Bias was 42 g and 95% limits of agreement ranged from - 736 to 819 g. Agreement was particularly high in smaller lesions.

Conclusions: The 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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http://dx.doi.org/10.1186/s40658-021-00376-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994489PMC
March 2021

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study.

Eur Radiol Exp 2021 Mar 11;5(1):11. Epub 2021 Mar 11.

Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.

Background: Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.

Methods: Ethical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3 days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.

Results: The accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p < 0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of ± 20%.

Conclusions: The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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http://dx.doi.org/10.1186/s41747-021-00210-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947128PMC
March 2021

Assessment of Total-Body Atherosclerosis by PET/Computed Tomography.

PET Clin 2021 Jan 5;16(1):119-128. Epub 2020 Nov 5.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, PA 19104, USA.

Atherosclerotic burden has become the focus of cardiovascular risk assessment. PET/computed tomography (CT) imaging with the tracers 18F-fluorodeoxyglucose and 18F-sodium fluoride shows arterial wall inflammation and microcalcification, respectively. Arterial uptake of both tracers is modestly age dependent. 18F-sodium fluoride uptake is consistently associated with risk factors and more easily measured in the heart. Because of extremely high sensitivity, ultrashort acquisition, and minimal radiation to the patient, total-body PET/CT provides unique opportunities for atherosclerosis imaging: disease screening and delayed and repeat imaging with global disease scoring and parametric imaging to better characterize the atherosclerosis of individual patients.
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http://dx.doi.org/10.1016/j.cpet.2020.09.013DOI Listing
January 2021

Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival.

Clin Physiol Funct Imaging 2021 Jan 18;41(1):62-67. Epub 2020 Oct 18.

Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.

Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions.

Methods: A group of 399 patients with biopsy-proven PCa who had undergone F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated.

Results: The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111; p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival.

Conclusion: This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
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http://dx.doi.org/10.1111/cpf.12666DOI Listing
January 2021

RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology.

EJNMMI Phys 2020 Aug 4;7(1):51. Epub 2020 Aug 4.

Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.

Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image.

Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones).

Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.
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http://dx.doi.org/10.1186/s40658-020-00316-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403290PMC
August 2020

Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival.

Clin Physiol Funct Imaging 2020 Mar 20;40(2):106-113. Epub 2019 Dec 20.

Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.

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 F-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.
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http://dx.doi.org/10.1111/cpf.12611DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027436PMC
March 2020

Assessing Radiographic Response to Ra with an Automated Bone Scan Index in Metastatic Castration-Resistant Prostate Cancer Patients.

J Nucl Med 2020 05 4;61(5):671-675. Epub 2019 Oct 4.

Division of Urological Cancers, Department of Translational Medicine, Skåne University Hospital, Lund University, Malmö, Sweden

For effective clinical management of patients being treated with Ra, there is a need for radiographic response biomarkers to minimize disease progression and to stratify patients for subsequent treatment options. The objective of this study was to evaluate an automated bone scan index (aBSI) as a quantitative assessment of bone scans for radiographic response in patients with metastatic castration-resistant prostate cancer (mCRPC). In a multicenter retrospective study, bone scans from patients with mCRPC treated with monthly injections of Ra were collected from 7 hospitals in Sweden. Patients with available bone scans before treatment with Ra and at treatment discontinuation were eligible for the study. The aBSI was generated at baseline and at treatment discontinuation. The Spearman rank correlation was used to correlate aBSI with the baseline covariates: alkaline phosphatase (ALP) and prostate-specific antigen (PSA). The Cox proportional-hazards model and Kaplan-Meier curve were used to evaluate the association of covariates at baseline and their change at treatment discontinuation with overall survival (OS). The concordance index (C-index) was used to evaluate the discriminating strength of covariates in predicting OS. Bone scan images at baseline were available from 156 patients, and 67 patients had both a baseline and a treatment discontinuation bone scan (median, 5 doses; interquartile range, 3-6 doses). Baseline aBSI (median, 4.5; interquartile range, 2.4-6.5) was moderately correlated with ALP ( = 0.60, < 0.0001) and with PSA ( = 0.38, = 0.003). Among baseline covariates, aBSI ( = 0.01) and ALP ( = 0.001) were significantly associated with OS, whereas PSA values were not ( = 0.059). After treatment discontinuation, 36% (24/67), 80% (54/67), and 13% (9/67) of patients demonstrated a decline in aBSI, ALP, and PSA, respectively. As a continuous variable, the relative change in aBSI after treatment, compared with baseline, was significantly associated with OS ( < 0.0001), with a C-index of 0.67. Median OS in patients with both aBSI and ALP decline (median, 134 wk) was significantly longer than in patients with ALP decline only (median, 77 wk; = 0.029). Both aBSI at baseline and its change at treatment discontinuation were significant parameters associated with OS. The study warrants prospective validation of aBSI as a quantitative imaging response biomarker to predict OS in patients with mCRPC treated with Ra.
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http://dx.doi.org/10.2967/jnumed.119.231100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198380PMC
May 2020

Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study.

Clin Physiol Funct Imaging 2019 Nov 8;39(6):399-406. Epub 2019 Sep 8.

Department of Clinical Research, University of Southern Denmark, Odense, Denmark.

Aim: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa).

Methods: A convolutional neural network (CNN) was trained for automated measurements in F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUV ), mean standardized uptake value of voxels considered abnormal (SUV ) and volume of abnormal voxels (Vol ). The product SUV  × Vol was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue.

Results: The mean (range) weight of the prostate specimens was 44 g (20-109), while CNN-estimated volume was 62 ml (31-108) with a mean difference of 13·5 g or ml (95% CI: 9·78-17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Vol (ml) and TLU were 0·37 (-0·01 to 0·75), -0·08 (-0·30 to 0·14), 1·40 (-2·26 to 5·06) and 9·61 (-3·95 to 23·17), respectively. PET findings Vol and TLU correlated with PSA (P<0·05), but not with Gleason score or stage.

Conclusion: Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.
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http://dx.doi.org/10.1111/cpf.12592DOI Listing
November 2019

The use of a proposed updated EARL harmonization of F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations.

EJNMMI Res 2019 Jul 25;9(1):65. Epub 2019 Jul 25.

Department of Translational Medicine, Lund University, Malmö, Sweden.

Background: The Deauville score (DS) is a clinical tool, based on the comparison between lesion and reference organ uptake of F-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 F-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 (EARL and EARL, 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, SUV and SUV were obtained and DS calculated.

Results: There was a significant difference in DS between the QC algorithm and EARL/EARL (p < 0.0001 for both) but not between EARL and EARL (p = 0.102) when SUV was used. For SUV, there was a significant difference between QC and EARL (p = 0.001), but not for QC vs EARL (p = 0.071) or EARL vs EARL (p = 0.102). Five non-responders (DS 4-5) for QC were classified as responders (DS 1-3) when EARL/EARL was used, both when SUV and SUV 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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http://dx.doi.org/10.1186/s13550-019-0536-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658640PMC
July 2019

Measurement of airway inflammation in current smokers by positron emission tomography.

Clin Physiol Funct Imaging 2019 Nov 24;39(6):393-398. Epub 2019 Jul 24.

Clinical Physiology and Nuclear Medicine Unit, Department of Translational Medicine, Lund University, Lund, Sweden.

Background: Accumulation of activated neutrophilic leucocytes is known to increase uptake of F-fluorodeoxyglucose ( F-FDG) into lung tissue. Available evidence suggests that smokers and subjects with chronic obstructive pulmonary disease (COPD) have neutrophilic inflammation in peripheral airways. The aim of this study was to examine whether current smokers have higher lung tissue uptake of F-FDG than never-smokers when correcting for air fraction of the lungs.

Methods: We prospectively recruited 33 current smokers and 33 never-smokers among subjects referred for diagnosis or staging of cancer, other than lung cancer, with combined positron emission tomography/computed tomography (PET/CT) with F-FDG. Subjects with focal F-FDG uptake or focal CT abnormalities in the lungs were excluded. The lungs were segmented in the CT image, and lung density measured. F-FDG uptake was measured in the corresponding volume and corrected for air fraction.

Results: Lung uptake of F-FDG, corrected for air fraction, was 12·5 and 8 per cent higher in the right and left lungs, respectively, in current smokers than in never-smokers (P<0·05). Conclusion Abnormal lung tissue uptake of F-FDG may be masked by reduced lung density if the uptake is not related to air fraction. Increased uptake of F-FDG in lung tissue in current smokers relative to never-smokers may reflect inflammation in peripheral airways. Measurements of F-FDG uptake in the lung tissue may be useful for animal and human studies of airways disease in COPD and the relation between airway and systemic inflammation.
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http://dx.doi.org/10.1111/cpf.12590DOI Listing
November 2019

Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11.

Eur Urol Oncol 2021 Feb 8;4(1):49-55. Epub 2019 Jun 8.

Department of Urology, Skåne University Hospital, Malmö, Lund University, Sweden. Electronic address:

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 EXINIbone 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.
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http://dx.doi.org/10.1016/j.euo.2019.05.002DOI Listing
February 2021

Global disease score (GDS) is the name of the game!

Eur J Nucl Med Mol Imaging 2019 Aug 10;46(9):1768-1772. Epub 2019 Jun 10.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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http://dx.doi.org/10.1007/s00259-019-04383-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6647113PMC
August 2019

Correction to: 3D skeletal uptake of F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer.

EJNMMI Res 2019 05 20;9(1):44. Epub 2019 May 20.

Department of Translational Medicine, Lund University, Malmö, Sweden.

Following publication of the original article [1], the authors flagged the that the Kaplan-Meier curve in Fig. 6 is a duplication of the Kaplan-Meier curve in Fig. 5, which is not correct.
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http://dx.doi.org/10.1186/s13550-019-0510-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527652PMC
May 2019

Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases.

Eur J Radiol 2019 Apr 1;113:89-95. Epub 2019 Feb 1.

Department of Translational Medicine, Lund University, Malmö, Sweden; Wallenberg Center for Molecular Medicine, Lund University, Malmö, Sweden.

Purpose: The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden.

Methods: Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone F-choline-PET/CT and F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method.

Results: The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5-6% for the vertebral column and ribs and ≤3% for other bones.

Conclusion: The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
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http://dx.doi.org/10.1016/j.ejrad.2019.01.028DOI Listing
April 2019

Phase 3 Assessment of the Automated Bone Scan Index as a Prognostic Imaging Biomarker of Overall Survival in Men With Metastatic Castration-Resistant Prostate Cancer: A Secondary Analysis of a Randomized Clinical Trial.

JAMA Oncol 2018 07;4(7):944-951

Memorial Sloan Kettering Cancer Center, New York, New York.

Importance: Prostate cancer commonly metastasizes to bone, and bone metastases are associated with pathologic fractures, pain, and reduced survival. Bone disease is routinely visualized using the technetium Tc 99m (99mTc) bone scan; however, the standard interpretation of bone scan data relies on subjective manual assessment of counting metastatic lesion numbers. There is an unmet need for an objective and fully quantitative assessment of bone scan data.

Objective: To clinically assess in a prospectively defined analysis plan of a clinical trial the automated Bone Scan Index (aBSI) as an independent prognostic determinant of overall survival (OS) in men with metastatic castration-resistant prostate cancer (mCRPC).

Design, Setting, And Participants: This investigation was a prospectively planned analysis of the aBSI in a phase 3 multicenter randomized, double-blind, placebo-controlled clinical trial of tasquinimod (10TASQ10). Men with bone metastatic chemotherapy-naïve CRPC were recruited at 241 sites in 37 countries between March 2011 and August 2015. The statistical analysis plan to clinically evaluate the aBSI was prospectively defined and locked before unmasking of the 10TASQ10 study. The analysis of aBSI was conducted between May 25, 2016, and June 3, 2017.

Main Outcomes And Measures: The associations of baseline aBSI with OS, radiographic progression-free survival (rPFS), time to symptomatic progression, and time to opiate use for cancer pain.

Results: Of the total 1245 men enrolled, 721 were evaluable for the aBSI. The mean (SD) age (available for 719 men) was 70.6 (8.0) years (age range, 47-90 years). The aBSI population was representative of the total study population based on baseline characteristics. The aBSI (median, 1.07; range, 0-32.60) was significantly associated with OS (hazard ratio [HR], 1.20; 95% CI, 1.14-1.26; P < .001). The median OS by aBSI quartile (lowest to highest) was 34.7, 27.3, 21.7, and 13.3 months, respectively. The discriminative ability of the aBSI (C index, 0.63) in prognosticating OS was significantly higher than that of the manual lesion counting (C index, 0.60) (P = .03). In a multivariable survival model, a higher aBSI remained independently associated with OS (HR, 1.06; 95% CI, 1.01-1.11; P = .03). A higher aBSI was also independently associated with time to symptomatic progression (HR, 1.18; 95% CI, 1.13-1.23; P < .001) and time to opiate use for cancer pain (HR, 1.21; 95% CI, 1.14-1.30; P < .001).

Conclusions And Relevance: To date, this investigation is the largest prospectively analyzed study to validate the aBSI as an independent prognostic imaging biomarker of survival in mCRPC. These data support the prognostic utility of the aBSI as an objective imaging biomarker in the design and eligibility of clinical trials of systemic therapies for patients with mCRPC.

Trial Registration: ClinicalTrials.gov Identifier: NCT01234311.
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http://dx.doi.org/10.1001/jamaoncol.2018.1093DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145727PMC
July 2018

Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database.

Ann Nucl Med 2018 Jun 7;32(5):303-310. Epub 2018 Mar 7.

Department of Clinical Physiology and Nuclear Medicine, University of Gothenburg, Gothenburg, Sweden.

Purpose: An artificial neural network (ANN) has been applied to detect myocardial perfusion defects and ischemia. The present study compares the diagnostic accuracy of a more recent ANN version (1.1) with the initial version 1.0.

Methods: We examined 106 patients (age, 77 ± 10 years) with coronary angiographic findings, comprising multi-vessel disease (≥ 50% stenosis) (52%) or old myocardial infarction (27%), or who had undergone coronary revascularization (30%). The ANN versions 1.0 and 1.1 were trained in Sweden (n = 1051) and Japan (n = 1001), respectively, using Tc-methoxyisobutylisonitrile myocardial perfusion images. The ANN probabilities (from 0.0 to 1.0) of stress defects and ischemia were calculated in candidate regions of abnormalities. The diagnostic accuracy was compared using receiver-operating characteristics (ROC) analysis and the calculated area under the ROC curve (AUC) using expert interpretation as the gold standard.

Results: Although the AUC for stress defects was 0.95 and 0.93 (p = 0.27) for versions 1.1 and 1.0, respectively, that for detecting ischemia was significantly improved in version 1.1 (p = 0.0055): AUC 0.96 for version 1.1 (sensitivity 87%, specificity 96%) vs. 0.89 for version 1.0 (sensitivity 78%, specificity 97%). The improvement in the AUC shown by version 1.1 was also significant for patients with neither coronary revascularization nor old myocardial infarction (p = 0.0093): AUC = 0.98 for version 1.1 (sensitivity 88%, specificity 100%) and 0.88 for version 1.0 (sensitivity 76%, specificity 100%). Intermediate ANN probability between 0.1 and 0.7 was more often calculated by version 1.1 compared with version 1.0, which contributed to the improved diagnostic accuracy. The diagnostic accuracy of the new version was also improved in patients with either single-vessel disease or no stenosis (n = 47; AUC, 0.81 vs. 0.66 vs. p = 0.0060) when coronary stenosis was used as a gold standard.

Conclusion: The diagnostic ability of the ANN version 1.1 was improved by retraining using the Japanese database, particularly for identifying ischemia.
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http://dx.doi.org/10.1007/s12149-018-1247-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970255PMC
June 2018

Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study.

Eur J Nucl Med Mol Imaging 2017 Dec 26;44(13):2280-2289. Epub 2017 Sep 26.

University of Gothenburg, Gothenburg, Sweden.

Purpose: Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable.

Methods: The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest Tc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis.

Results: The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were >0.80.

Conclusion: The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease.
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http://dx.doi.org/10.1007/s00259-017-3834-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5680364PMC
December 2017

Bone Scan Index and Progression-free Survival Data for Progressive Metastatic Castration-resistant Prostate Cancer Patients Who Received ODM-201 in the ARADES Multicentre Study.

Eur Urol Focus 2016 Dec 3;2(5):547-552. Epub 2016 Feb 3.

Department of Translational Medicine Division of Urological Cancers, Malmö, Lund University, Sweden; Department of Urology, Skåne University Hospital, Malmö, Sweden.

Background: ODM-201, a new-generation androgen receptor inhibitor, has shown clinical efficacy in prostate cancer (PCa). Quantitative methods are needed to accurately assess changes in bone as a measurement of treatment response. The Bone Scan Index (BSI) reflects the percentage of skeletal mass a given tumour affects.

Objective: To evaluate the predictive value of the BSI in metastatic castration-resistant PCa (mCRPC) patients undergoing treatment with ODM-201.

Design, Setting, And Participants: From a total of 134 mCRPC patients who participated in the Activity and Safety of ODM-201 in Patients with Progressive Metastatic Castration-resistant Prostate Cancer clinical trial and received ODM-201, we retrospectively selected all those patients who had bone scan image data of sufficient quality to allow for both baseline and 12-wk follow-up BSI-assessments (n=47). We used the automated EXINI bone BSI software (EXINI Diagnostics AB, Lund, Sweden) to obtain BSI data.

Outcome Measurements And Statistical Analysis: We used the Cox proportional hazards model and Kaplan-Meier estimates to investigate the association among BSI, traditional clinical parameters, disease progression, and radiographic progression-free survival (rPFS).

Results And Limitations: In the BSI assessments, at follow-up, patients who had a decrease or at most a 20% increase from BSI baseline had a significantly longer time to progression in bone (median not reached vs 23 wk, hazard ratio [HR]: 0.20; 95% confidence interval [CI], 0.07-0.58; p=0.003) and rPFS (median: 50 wk vs 14 wk; HR: 0.35; 95% CI, 0.17-0.74; p=0.006) than those who had a BSI increase >20% during treatment.

Conclusions: The on-treatment change in BSI was significantly associated with rPFS in mCRPC patients, and an increase >20% in BSI predicted reduced rPFS. BSI for quantification of bone metastases may be a valuable complementary method for evaluation of treatment response in mCRPC patients.

Patient Summary: An increase in Bone Scan Index (BSI) was associated with shorter time to disease progression in patients treated with ODM-201. BSI may be a valuable method of complementing treatment response evaluation in patients with advanced prostate cancer.
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http://dx.doi.org/10.1016/j.euf.2016.01.005DOI Listing
December 2016

Bone Scan Index as an Imaging Biomarker in Metastatic Castration-resistant Prostate Cancer: A Multicentre Study Based on Patients Treated with Abiraterone Acetate (Zytiga) in Clinical Practice.

Eur Urol Focus 2016 Dec 9;2(5):540-546. Epub 2016 Mar 9.

Department of Translational Medicine, Division of Urological Cancers, Malmö, Lund University, Lund, Sweden; Department of Urology, Skåne University Hospital, Malmö, Sweden.

Background: Abiraterone acetate (AA) prolongs survival in metastatic castration-resistant prostate cancer (mCRPC) patients. To measure treatment response accurately in bone, quantitative methods are needed. The Bone Scan Index (BSI), a prognostic imaging biomarker, reflects the tumour burden in bone as a percentage of the total skeletal mass calculated from bone scintigraphy.

Objective: To evaluate the value of BSI as a biomarker for outcome evaluation in mCRPC patients on treatment with AA according to clinical routine.

Design, Setting, And Participants: We retrospectively studied 104 mCRPC patients who received AA following disease progression after chemotherapy. All patients underwent whole-body bone scintigraphy before and during AA treatment. Baseline and follow-up BSI data were obtained using EXINI Bone software (EXINI Diagnostics AB, Lund, Sweden).

Outcome Measurements And Statistical Analysis: Associations between change in BSI, clinical parameters at follow-up, and overall survival (OS) were evaluated using the Cox proportional hazards regression models and Kaplan-Meier estimates. Discrimination between variables was assessed using the concordance index (C-index).

Results And Limitations: Patients with an increase in BSI at follow-up of at most 0.30 (n=54) had a significantly longer median survival time than those with an increase of BSI >0.30 (n=50) (median: 16 vs 10 mo; p=0.001). BSI change was also associated with OS in a multivariate Cox analysis including commonly used clinical parameters for prognosis (C-index=0.7; hazard ratio: 1.1; p=0.03). The retrospective design was a limitation.

Conclusions: Change in BSI was significantly associated with OS in mCRPC patients undergoing AA treatment following disease progression in a postchemotherapy setting. BSI may be a useful imaging biomarker for outcome evaluation in this group of patients, and it could be a valuable complementary tool in monitoring patients with mCRPC on second-line therapies.

Patient Summary: Bone Scan Index (BSI) change is related to survival time in metastatic castration-resistant prostate cancer (mCRPC) patients on abiraterone acetate. BSI may be a valuable complementary decision-making tool supporting physicians monitoring patients with mCRPC on second-line therapies.
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http://dx.doi.org/10.1016/j.euf.2016.02.013DOI Listing
December 2016

Bone scan index: A new biomarker of bone metastasis in patients with prostate cancer.

Int J Urol 2017 09 26;24(9):668-673. Epub 2017 May 26.

Department of Urology, Kanazawa University, Kanazawa, Japan.

Bone scintigraphy is one of the first-line imaging modalities for the screening and follow up of bone metastasis in patients with prostate cancer. The amount (%) of bone metastasis can be calculated using a bone scan index thanks to recent advances in quantitative bone scintigraphy. Since an artificial neural network was applied for hot-spot characterization and quantitation, the bone scan index has become a simple, reproducible and practical means of quantifying bone metastasis. The bone scan index is presently considered as an imaging biomarker of bone metastasis. The present article summarizes the principles and application of bone scan index using dedicated software (EXINI bone in Europe and North America; BONENAVI in Japan), and the advantages and cautions of using the bone scan index. The bone scan index could serve as a practical marker with which to monitor disease progression and treatment effects in multicenter studies, and to manage prostate and other types of cancer in the clinical setting.
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http://dx.doi.org/10.1111/iju.13386DOI Listing
September 2017

3D skeletal uptake of F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer.

EJNMMI Res 2017 Dec 16;7(1):15. Epub 2017 Feb 16.

Department of Translational Medicine, Lund University, Malmö, Sweden.

Background: Sodium fluoride (NaF) positron emission tomography combined with computer tomography (PET/CT) has shown to be more sensitive than the whole-body bone scan in the detection of skeletal uptake due to metastases in prostate cancer. We aimed to calculate a 3D index for NaF PET/CT and investigate its correlation to the bone scan index (BSI) and overall survival (OS) in a group of patients with prostate cancer.

Methods: NaF PET/CT and bone scans were studied in 48 patients with prostate cancer. Automated segmentation of the thoracic and lumbar spines, sacrum, pelvis, ribs, scapulae, clavicles, and sternum were made in the CT images. Hotspots in the PET images were selected using both a manual and an automated method. The volume of each hotspot localized in the skeleton in the corresponding CT image was calculated. Two PET/CT indices, based on manual (manual PET index) and automatic segmenting using a threshold of SUV 15 (automated PET index), were calculated by dividing the sum of all hotspot volumes with the volume of all segmented bones. BSI values were obtained using a software for automated calculations.

Results: BSI, manual PET index, and automated PET index were all significantly associated with OS and concordance indices were 0.68, 0.69, and 0.70, respectively. The median BSI was 0.39 and patients with a BSI >0.39 had a significantly shorter median survival time than patients with a BSI <0.39 (2.3 years vs not reached after 5 years of follow-up [p = 0.01]). The median manual PET index was 0.53 and patients with a manual PET index >0.53 had a significantly shorter median survival time than patients with a manual PET index <0.53 (2.5 years vs not reached after 5 years of follow-up [p < 0.001]). The median automated PET index was 0.11 and patients with an automated PET index >0.11 had a significantly shorter median survival time than patients with an automated PET index <0.11 (2.3 years vs not reached after 5 years of follow-up [p < 0.001]).

Conclusions: PET/CT indices based on NaF PET/CT are correlated to BSI and significantly associated with overall survival in patients with prostate cancer.
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http://dx.doi.org/10.1186/s13550-017-0264-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5313492PMC
December 2017

A Preanalytic Validation Study of Automated Bone Scan Index: Effect on Accuracy and Reproducibility Due to the Procedural Variabilities in Bone Scan Image Acquisition.

J Nucl Med 2016 Dec 21;57(12):1865-1871. Epub 2016 Jul 21.

Department of Radiation Physics, Skåne University Hospital, Malmö, Sweden.

The effect of the procedural variability in image acquisition on the quantitative assessment of bone scan is unknown. Here, we have developed and performed preanalytical studies to assess the impact of the variability in scanning speed and in vendor-specific γ-camera on reproducibility and accuracy of the automated bone scan index (BSI).

Methods: Two separate preanalytical studies were performed: a patient study and a simulation study. In the patient study, to evaluate the effect on BSI reproducibility, repeated bone scans were prospectively obtained from metastatic prostate cancer patients enrolled in 3 groups (Grp). In Grp1, the repeated scan speed and the γ-camera vendor were the same as that of the original scan. In Grp2, the repeated scan was twice the speed of the original scan. In Grp3, the repeated scan used a different γ-camera vendor than that used in the original scan. In the simulation study, to evaluate the effect on BSI accuracy, bone scans of a virtual phantom with predefined skeletal tumor burden (phantom-BSI) were simulated against the range of image counts (0.2, 0.5, 1.0, and 1.5 million) and separately against the resolution settings of the γ-cameras. The automated BSI was measured with a computer-automated platform. Reproducibility was measured as the absolute difference between the repeated BSI values, and accuracy was measured as the absolute difference between the observed BSI and the phantom-BSI values. Descriptive statistics were used to compare the generated data.

Results: In the patient study, 75 patients, 25 in each group, were enrolled. The reproducibility of Grp2 (mean ± SD, 0.35 ± 0.59) was observed to be significantly lower than that of Grp1 (mean ± SD, 0.10 ± 0.13; P < 0.0001) and that of Grp3 (mean ± SD, 0.09 ± 0.10; P < 0.0001). However, no significant difference was observed between the reproducibility of Grp3 and Grp1 (P = 0.388). In the simulation study, the accuracy at 0.5 million counts (mean ± SD, 0.57 ± 0.38) and at 0.2 million counts (mean ± SD, 4.67 ± 0.85) was significantly lower than that observed at 1.5 million counts (mean ± SD, 0.20 ± 0.26; P < 0.0001). No significant difference was observed in the accuracy data of the simulation study with vendor-specific γ-cameras (P = 0.266).

Conclusion: In this study, we observed that the automated BSI accuracy and reproducibility were dependent on scanning speed but not on the vendor-specific γ-cameras. Prospective BSI studies should standardize scanning speed of bone scans to obtain image counts at or above 1.5 million.
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http://dx.doi.org/10.2967/jnumed.116.177030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952052PMC
December 2016

Reducing the small-heart effect in pediatric gated myocardial perfusion single-photon emission computed tomography.

J Nucl Cardiol 2017 08 19;24(4):1378-1388. Epub 2016 May 19.

Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.

Background: We compared two reconstruction algorisms and two cardiac functional evaluation software programs in terms of their accuracy for estimating ejection fraction (EF) of small hearts (SH).

Methods: The study group consisted of 66 pediatric patients. Data were reconstructed using a filtered back projection (FBP) method without the resolution correction (RC) and an iterative method based on an ordered subset expectation maximization (OSEM) algorithm with the RC. EF was evaluated using two software programs of quantitative gated single-photon emission computed tomography (SPECT) (QGS) and cardioREPO. We compared the EF of gated myocardial perfusion SPECT to echocardiographic measurement (Echo).

Results: Forty-eight of 66 patients had an end-systolic volume < 20 mL which was used as the criterion for being included in the SH group, and the SH effect led to an overestimation of EF. While significant differences were observed between Echo (66.9 ± 5.0%) and QGS-FBP without RC (76.9 ± 8.4%, P < .0001), QGS-OSEM with RC (76.6 ± 8.6%, P < .0001), and cardioREPO-FBP without RC (72.1 ± 10.0%, P = .0011), no significant difference was observed between Echo and cardioREPO-OSEM with RC (67.4 ± 6.1%) in SH group.

Conclusions: In pediatric gated myocardial perfusion SPECT, the SH effect can be significantly reduced when an OSEM algorithm is used with RC in combination with the specific cardioREPO algorithm.
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http://dx.doi.org/10.1007/s12350-016-0518-zDOI Listing
August 2017

Automated Bone Scan Index as a quantitative imaging biomarker in metastatic castration-resistant prostate cancer patients being treated with enzalutamide.

EJNMMI Res 2016 Dec 9;6(1):23. Epub 2016 Mar 9.

Department of Translational Medicine, Division of Urological Cancers, Lund University, Waldenströms Gata 5, Malmö, SE 205 02, Sweden.

Background: Having performed analytical validation studies, we are now assessing the clinical utility of the upgraded automated Bone Scan Index (BSI) in metastatic castration-resistant prostate cancer (mCRPC). In the present study, we retrospectively evaluated the discriminatory strength of the automated BSI in predicting overall survival (OS) in mCRPC patients being treated with enzalutamide.

Methods: Retrospectively, we included patients who received enzalutamide as a clinically approved therapy for mCRPC and had undergone bone scan prior to starting therapy. Automated BSI, prostate-specific antigen (PSA), hemoglobin (HgB), and alkaline phosphatase (ALP) were obtained at baseline. Change in automated BSI and PSA were obtained from patients who have had bone scan at week 12 of treatment follow-up. Automated BSI was obtained using the analytically validated EXINI Bone(BSI) version 2. Kendall's tau (τ) was used to assess the correlation of BSI with other blood-based biomarkers. Concordance index (C-index) was used to evaluate the discriminating strength of automated BSI in predicting OS.

Results: Eighty mCRPC patients with baseline bone scans were included in the study. There was a weak correlation of automated BSI with PSA (τ = 0.30), with HgB (τ = -0.17), and with ALP (τ = 0.56). At baseline, the automated BSI was observed to be predictive of OS (C-index 0.72, standard error (SE) 0.03). Adding automated BSI to the blood-based model significantly improved the C-index from 0.67 to 0.72, p = 0.017. Treatment follow-up bone scans were available from 62 patients. Both change in BSI and percent change in PSA were predictive of OS. However, the combined predictive model of percent PSA change and change in automated BSI (C-index 0.77) was significantly higher than that of percent PSA change alone (C-index 0.73), p = 0.041.

Conclusions: The upgraded and analytically validated automated BSI was found to be a strong predictor of OS in mCRPC patients. Additionally, the change in automated BSI demonstrated an additive clinical value to the change in PSA in mCRPC patients being treated with enzalutamide.
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http://dx.doi.org/10.1186/s13550-016-0173-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785173PMC
December 2016
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