3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer

Artificial intelligence in skeletal metastasis imaging

In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study

RATIONALE AND OBJECTIVES

Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists.

MATERIALS AND METHODS

This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36).

RESULTS

In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%).

CONCLUSION

Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

Applications of artificial intelligence in orthopaedic surgery

The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.

Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [¹⁸F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians’ corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

Applications of Artificial Intelligence in 18F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:: Current State and Future Directions

This review discusses the current state of artificial intelligence (AI) in ¹⁸F-NaF-PET/CT imaging and the potential applications to come in diagnosis, prognostication, and improvement of care in patients with bone diseases, with emphasis on the role of AI algorithms in CT bone segmentation, relying on their prevalence in medical imaging and utility in the extraction of spatial information in combined PET/CT studies.

Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network

BACKGROUND

The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images.

METHODS

A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN.

RESULTS

Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest.

CONCLUSIONS

AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.

PET/CT-Based Response Evaluation in Cancer-a Systematic Review of Design Issues

Positron emission tomography/x-ray computed tomography (PET/CT) has long been discussed as a promising modality for response evaluation in cancer. When designing respective clinical trials, several design issues have to be addressed, especially the number/timing of PET/CT scans, the approach for quantifying metabolic activity, and the final translation of measurements into a rule. It is unclear how well these issues have been tackled in quest of an optimised use of PET/CT in response evaluation. Medline via Ovid and Science Citation Index via Web of Science were systematically searched for articles from 2015 on cancer patients scanned with PET/CT before and during/after treatment. Reports were categorised as being either developmental or evaluative, i.e. focusing on either the establishment or the evaluation of a rule discriminating responders from non-responders. Of 124 included papers, 112 (90 %) were accuracy and/or prognostic studies; the remainder were response-curve studies. No randomised controlled trials were found. Most studies were prospective (62 %) and from single centres (85 %); median number of patients was 38.5 (range 5-354). Most (69 %) of the studies employed only one post-baseline scan. Quantification was mainly based on SUVmax (91 %), while change over time was most frequently used to combine measurements into a rule (79 %). Half of the reports were categorised as developmental, the other half evaluative. Most development studies assessed only one element (35/62, 56 %), most frequently the choice of cut-off points (25/62, 40 %). In summary, the majority of studies did not address the essential open issues in establishing PET/CT for response evaluation. Reasonably sized multicentre studies are needed to systematically compare the many different options when using PET/CT for response evaluation.

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

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 ¹⁸ 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.

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

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.

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

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 ¹⁸ 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_max ), mean standardized uptake value of voxels considered abnormal (SUV_mean ) and volume of abnormal voxels (Vol_abn ). The product SUV_mean  × Vol_abn 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_abn (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_abn 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.

18 F-NaF PET/CT IMAGING IN FIBROUS DYSPLASIA OF BONE

Fibrous dysplasia (FD) is a mosaic skeletal disorder resulting in fractures, deformity, and functional impairment. Clinical evaluation has been limited by a lack of surrogate endpoints capable of quantitating disease activity. The purpose of this study was to investigate the utility of ¹⁸ F-NaF PET/CT imaging in quantifying disease activity in patients with FD. Fifteen consecutively evaluated subjects underwent whole-body ¹⁸ F-NaF PET/CT scans, and FD burden was assessed by quantifying FD-related ¹⁸ F-NaF activity. ¹⁸ F-NaF PET/CT parameters obtained included (i) SUV_max (standardized uptake value [SUV] of the FD lesion with the highest uptake); (ii) SUV_mean (average SUV of all ¹⁸ F-NaF-positive FD lesions); (iii) total volume of all ¹⁸ F-NaF-positive FD lesions (TV); and (iv) total FD lesion activity determined as the product of TV multiplied by SUV_mean (TA =  TV ×  SUV_mean ) (TA). Skeletal outcomes, functional outcomes, and bone turnover markers were correlated with ¹⁸ F-NaF PET/CT parameters. TV and TA of extracranial FD lesions correlated strongly with skeletal outcomes including fractures and surgeries (p values ≤ 0.003). Subjects with impaired ambulation and scoliosis had significantly higher TV and TA values (P < 0.05), obtained from extracranial and spinal lesions, respectively. Craniofacial surgeries correlated with TV and TA of skull FD lesions (P < 0.001). Bone turnover markers, including alkaline phosphatase, N-telopeptides, and osteocalcin, were strongly correlated with TV and TA (P < 0.05) extracted from FD lesions in the entire skeleton. No associations were identified with SUV_max or SUV_mean . Bone pain and age did not correlate with ¹⁸ F-NaF PET/CT parameters. FD burden evaluated by ¹⁸ F-NaF-PET/CT facilitates accurate assessment of FD activity, and correlates quantitatively with clinically-relevant skeletal outcomes. © 2019 American Society for Bone and Mineral Research.

Impact of Anatomic Location of Bone Metastases on Prognosis in Metastatic Castration-Resistant Prostate Cancer

BACKGROUND

Whole-body assessments of ¹⁸F-NaF positron emission tomography (PET)/computed tomography (CT) provide promising quantitative imaging biomarkers of metastatic castration-resistant prostate cancer (mCRPC). This study investigated whether the distribution of metastases across anatomic regions is prognostic of progression-free survival.

PATIENTS AND METHODS

Fifty-four mCRPC patients with osseous metastases received baseline NaF PET/CT. Patients received chemotherapy (n = 16) or androgen receptor pathway inhibitors (n = 38). Semiautomated analysis using Quantitative Total Bone Imaging software extracted imaging metrics for the whole, axial, and appendicular skeleton as well as 11 skeletal regions. Five PET metrics were extracted for each region: number of lesions (N_L), standardized maximum uptake value (SUV_max), average uptake (SUV_mean), sum of uptake (SUV_total), and diseased fraction of the skeleton (volume fraction). Progression included that discovered by clinical, biochemical, or radiographic means. Univariate and multivariate Cox proportional hazard regression analyses were performed between imaging metrics and progression-free survival, and were assessed according to their hazard ratios (HR) and concordance (C)-indices.

RESULTS

The strongest univariate models of progression-free survival were pelvic N_L and SUV_max with HR = 1.80 (N_L: false discovery rate adjusted P = .001, SUV_max: adjusted P = .001). Three other region-specific metrics (axial N_L: HR = 1.59, adjusted P = .02, axial SUV_max: HR = 1.61, adjusted P = .02, and skull SUV_max: HR = 1.58, adjusted P = .04) were found to be stronger prognosticators relative to their whole-body counterparts. Multivariate model including region-specific metrics (C-index = 0.727) outperformed that of whole-body metrics (C-index = 0.705). The best performance was obtained when region-specific and whole-body metrics were included (C-index = 0.742).

CONCLUSION

Quantitative characterization of metastatic spread by anatomic location on NaF PET/CT enhances potential prognostication. Further study is warranted to optimize the prognostic and predictive value of NaF PET/CT in mCRPC patients.

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

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 ¹⁸F-choline-PET/CT and ¹⁸F-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.

Uptake of Radium-223 Dichloride and Early [18F]NaF PET Response Are Driven by Baseline [18F]NaF Parameters: a Pilot Study in Castration-Resistant Prostate Cancer Patients

PURPOSE

The purpose of this study is to identify predictive factors on baseline [¹⁸F]NaF positron emission tomography (PET)/computed tomography (CT) of early response to radium-223 dichloride after 3 cycles of treatment in metastatic castration-resistant prostate cancer patients.

PROCEDURES

Analysis of 152 metastases was performed in six consecutive patients who underwent [¹⁸F]NaF PET/CT at baseline and for early monitoring after 3 cycles of radium-223 dichloride. All metastases depicted on whole-body [¹⁸F]NaF PET/CT were contoured and CT (density in Hounsfield units, sclerotic, mixed, or lytic appearance) as well as [¹⁸F]NaF [maximum standardized uptake value (SUV_max), SUV_mean, and lesion volume (V_18F-NaF)] patterns were recorded. Tumor response was defined as percentage change in SUV_max and SUV_mean between baseline and post-treatment PET. Bone lesions were defined as stable, responsive, or progressive, according to thresholds derived from a recent multicentre test-retest study in [¹⁸F]NaF PET/CT. Total [¹⁸F]NaF uptake in metastases, defined as MATV × SUV_mean, was correlated to uptake of radium-223 on biodistribution scintigraphy performed 7 days after the first cycle of treatment.

RESULTS

Among metastases, 116 involved the axial skeleton and 36 the appendicular skeleton. Lesions were sclerotic in 126 cases and mixed in 26 cases. No lytic lesion was depicted. ROC analysis showed that SUV_max and SUV_mean were better predictors of lesion response than V_18F-NaF and density on CT (P < 0.0001 and P = 0.001, respectively). SUV_max and SUV_mean were predictors of individual tumor response in separate multivariate models (P = 0.01 and P = 0.02, respectively). CT pattern (mixed versus sclerotic) and lesion density were independent predictors only when assessing response with delta SUV_max (P = 0.002 and 0.007, respectively). A good correlation between total [¹⁸F]NaF uptake within metastases and their relative radium-223 uptake assessed by two observers 7 days after treatment (r = 0.72 and 0.77, P < 0.0001) was found.

CONCLUSIONS

SUV_max and SUV_mean on baseline [¹⁸F]NaF PET/CT are independent predictors of bone lesions' response to 3 cycles of radium-223 dichloride, supporting the use of NaF to select patients more likely to respond to treatment.

Prospective comparative study of 18F-sodium fluoride PET/CT and planar bone scintigraphy for treatment response assessment of bone metastases in patients with prostate cancer

AIM

To compare ¹⁸F-sodium fluoride positron emission tomography/computed tomography (NaF PET/CT) and ^99mTc-labelled diphosphonate bone scan (BS) for the monitoring of bone metastases in patients with prostate cancer undergoing anti-cancer treatment.

MATERIAL AND METHODS

Data from 64 patients with prostate cancer were included. The patients received androgen-deprivation therapy (ADT), next-generation hormonal therapy (NGH) or chemotherapy. The patients had a baseline scan and 1-3 subsequent scans during six months of treatment. Images were evaluated by experienced nuclear medicine physicians and classified for progressive disease (PD) or non-PD according to the Prostate Cancer Working Group 2 (PCWG-2) criteria. The patients were also classified as having PD/non-PD according to the clinical and prostate-specific antigen (PSA) responses.

RESULTS

There was no difference between NaF PET/CT and BS in the detection of PD and non-PD during treatment (McNemar's test, p = .18). The agreement between BS and NaF PET/CT for PD/non-PD was moderate (Cohen's kappa 0.53, 95% confidence interval 0.26-0.79). Crude agreement between BS and NaF PET/CT for the assessment of PD/non-PD was 86% (89% for ADT, n = 28; 88% for NGH, n = 16, and 80% for chemotherapy, n = 20). In most discordant cases, BS found PD when NaF PET/CT did not, or BS detected PD on an earlier scan than NaF PET/CT. Biochemical progression (27%) occurred more frequently than progression on functional imaging (BS, 22% and NaF PET/CT, 14%). Clinical progression was rare (11%), and almost exclusively seen in patients receiving chemotherapy.

CONCLUSION

There was no difference between NaF PET/CT and BS in the detection of PD and non-PD; however, BS seemingly detects PD by the PCWG-2 criteria earlier than NaF-PET, which might be explained by the fact that NaF-PET is more sensitive at the baseline scan.

Assessment of Skeletal Tumor Load in Metastasized Castration-Resistant Prostate Cancer Patients: A Review of Available Methods and an Overview on Future Perspectives

Metastasized castration-resistant prostate cancer (mCRPC), is the most advanced form of prostate neoplasia, where massive spread to the skeletal tissue is frequent. Patients with this condition are benefiting from an increasing number of treatment options. However, assessing tumor response in patients with multiple localizations might be challenging. For this reason, many computational approaches have been developed in the last decades to quantify the skeletal tumor burden and treatment response. In this review, we analyzed the progressive development and diffusion of such approaches. A computerized literature search of the PubMed/Medline was conducted, including articles between January 2008 and March 2018. The search was expanded by manually reviewing the reference list of the chosen articles. Thirty-five studies were identified. The number of eligible studies greatly increased over time. Studies could be categorized in the following categories: automated analysis of 2D scans, SUV-based thresholding, hybrid CT- and SUV-based thresholding, and MRI-based thresholding. All methods are discussed in detail. Automated analysis of bone tumor burden in mCRPC is a growing field of research; when choosing the appropriate method of analysis, it is important to consider the possible advantages as well as the limitations thoroughly.

Functional and Hybrid Imaging of Bone Metastases

Bone metastases are common, cause significant morbidity, and impact on healthcare resources. Although radiography, computed tomography (CT), magnetic resonance imaging (MRI), and bone scintigraphy have frequently been used for staging the skeleton, these methods are insensitive and nonspecific for monitoring treatment response in a clinically relevant time frame. We summarize several recent reports on new functional and hybrid imaging methods including single photon emission CT/CT, positron emission tomography/CT, and whole-body MRI with diffusion-weighted imaging. These modalities generally show improvements in diagnostic accuracy for staging and response assessment over standard imaging methods, with the ability to quantify biological processes related to the bone microenvironment as well as tumor cells. As some of these methods are now being adopted into routine clinical practice and clinical trials, further evaluation with comparative studies is required to guide optimal and cost-effective clinical management of patients with skeletal metastases. © 2018 American Society for Bone and Mineral Research.

Evaluation of changes in Bone Scan Index at different acquisition time-points in bone scintigraphy

Bone Scan Index (BSI) is a validated imaging biomarker to objectively assess tumour burden in bone in patients with prostate cancer, and can be used to monitor treatment response. It is not known if BSI is significantly altered when images are acquired at a time difference of 1 h. The aim of this study was to investigate if automatic calculation of BSI is affected when images are acquired 1 hour apart, after approximately 3 and 4 h. We prospectively studied patients with prostate cancer who were referred for bone scintigraphy according to clinical routine. The patients performed a whole-body bone scan at approximately 3 h after injection of radiolabelled bisphosphonate and a second 1 h after the first. BSI values for each bone scintigraphy were obtained using EXINI boneBSI software. A total of 25 patients were included. Median BSI for the first acquisition was 0·05 (range 0-11·93) and for the second acquisition 0·21 (range 0-13·06). There was a statistically significant increase in BSI at the second image acquisition compared to the first (P<0·001). In seven of 25 patients (28%) and in seven of 13 patients with BSI > 0 (54%), a clinically significant increase (>0·3) was observed. The time between injection and scanning should be fixed when changes in BSI are important, for example when monitoring therapeutic efficacy.

Innovations in imaging modalities for recurrent and metastatic prostate cancer: a systematic review

INTRODUCTION

The last decade has witnessed tremendous changes in the management of advanced and metastatic castration resistant prostate cancer. In the current systematic review, we analyze novel imaging techniques in the setting of recurrent and metastatic prostate cancer (PCa), exploring available data and highlighting future exams which could enter clinical practice in the upcoming years.

EVIDENCE ACQUISITION

The National Library of Medicine Database was searched for relevant articles published between January 2012 and August 2017. A wide search was performed including the combination of following words: "Prostate" AND "Cancer" AND ("Metastatic" OR "Recurrent") AND "imaging" AND ("MRI" OR "PET"). The selection procedure followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) principles and is presented using a PRISMA flow chart.

EVIDENCE SYNTHESIS

Novel imaging techniques, as multiparametric magnetic resonance imaging (MRI), whole-body MRI and Choline and prostate-specific membrane antigen (PSMA) PET imaging techniques are currently revolutioning the treatment planning in patients with advanced and metastatic PCa, allowing a better characterization of the disease. Multiparametric MRI performs well in the detection of local recurrences, with sensitivity rates of 67-98% and overall diagnostic accuracy of 83-93%, depending on the type of magnetic field strength (1.5 vs. 3T). Whole body MRI instead shows a high specificity (>95%) for bone metastases. PET imaging, and in particular PSMA PET/CT, showed promising results in the detection of both local and distant recurrences, even for low PSA values (<0.5 ng/mL). Sensitivity varies from 77-98% depending on PSA value and PSA velocity.

CONCLUSIONS

Whole body-MRI, NaF PET, Choline-PET/CT and PSMA PET/CT are flourishing techniques which find great application in the field of recurrent and metastatic PCa, in the effort to reduce treatment of "PSA only" and rather focus our therapies on clinical tumor entities. Standardization is urgently needed to allow adequate comparison of results and diffusion on a large scale.

Hospice Admission and Survival After 18F-Fluoride PET Performed for Evaluation of Osseous Metastatic Disease in the National Oncologic PET Registry

We have previously reported that PET using ¹⁸F-fluoride (NaF PET) for assessment of osseous metastatic disease was associated with substantial changes in intended management in Medicare beneficiaries participating in the National Oncologic PET Registry (NOPR). Here, we use Medicare administrative data to examine the association between NaF PET results and hospice claims within 180 d and 1-y survival. Methods: We classified NOPR NaF PET results linked to Medicare claims by imaging indication (initial staging [IS]; detection of suspected first osseous metastasis [FOM]; suspected progression of osseous metastasis [POM]; or treatment monitoring [TM]) and type of cancer (prostate, lung, breast, or other). Results were classified as definitely positive scan findings versus probably positive scan findings versus negative scan findings for osseous metastasis for IS and FOM; more extensive disease versus no change or less extensive disease for POM; and worse prognosis versus no change or better prognosis for TM, based on the postscan assessment. Our study included 21,167 scans obtained from 2011 to 2014 of consenting NOPR participants aged 65 y or older. Results: The relative risk of hospice claims within 180 d of a NaF PET scan was 2.0-7.5 times higher for patients with evidence of new or progressing osseous metastasis than for those without, depending on indication and cancer type (all P < 0.008). The percentage difference in hospice claims for those with a finding of new or more advanced osseous disease ranged from 3.9% for IS prostate patients to 28% for FOM lung patients. Six-month survival was also associated with evidence of new or increased osseous disease; risk of death was 1.8-5.1 times as likely (all P ≤ 0.0001), with percentage differences of approximately 30% comparing positive and negative scans in patients with lung cancer imaged for IS or FOM. Conclusion: Our analyses demonstrated that NaF PET scan results are highly associated with subsequent hospice claims and, ultimately, with patient survival. NaF PET provides important information on the presence of osseous metastasis and prognosis to assist patients and their physicians when making decisions on whether to select palliative care and transition to hospice or whether to continue treatment.

Prognostic value of bone scan index as an imaging biomarker in metastatic prostate cancer: a meta-analysis

Background

The prognostic value of the bone scan index (BSI) in metastatic prostate cancer (mPCa) remained controversial. Therefore, we performed a meta-analysis to determine the predictive value of BSI and survival in patients with mPCa.

Materials and Methods

A literature search was performed in PubMed, Embase, Web of Science and Cochrane library databases. Hazard ratios (HRs), concordance indices (C-indices) were extracted to estimate the relationship between BSI and survival in patients with mPCa. Subgroup analyses were conducted on different types of mPCa, ethnics, cut-off values and sample sizes.

Results

14 high quality studies involving 1295 patients with mPCa were included in this meta-analysis. The pooled results indicated that high basline BSI and elevated BSI change on treatment (ΔBSI) were significantly predictive of poor overall survial (HR = 1.29, P < 0.001; HR = 1.27, P < 0.001, respectively). Baseline BSI was also significantly related to cancer specific survival (HR = 1.65, P = 0.019) and prostate specific antigen recurrence survival (HR = 2.26, P < 0.001). Subgroup analysis supported main results. Moreover, BSI could increase the C-indices of predictive models.

Conclusions

Baseline BSI and ΔBSI may be beneficial to mPCa prognosis in clinical monitor and treatment. Further high quality studies with larger sample size are required in the future.

WITHDRAWN: Author Reply to the Editorial Comment on: Zacho HD, Gade M, Mortensen JC, Bertelsen H, Boldsen SK, Petersen LJ. Bone Scan Index Is an Independent Predictor for Time to Castration Resistant Prostate Cancer in Newly Diagnosed Prostate Cancer: A Prospective Study. Urology 2017, In Press

The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.urology.2017.05.060. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.