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

Flare phenomenon visualized by 99mTc-bone scintigraphy has prognostic value for patients with metastatic castration-resistant prostate cancer

OBJECTIVE

This study aimed to determine the prognostic value of the flare phenomenon in patients with metastatic castration-resistant prostate cancer (mCRPC) using the bone scan index (BSI) derived from 99mTc-methylenediphosphonate (MDP) bone scintigraphy images.

METHODS

We categorized 72 patients from the PROSTAT-BSI registry with mCRPC who were followed-up for 2 years after starting docetaxel chemotherapy to groups based on pre-chemotherapy BSI values of < 1, 1-4, and > 4. We assessed the effects of the flare phenomenon (defined as a > 10% increase in the BSI within 3 months of starting chemotherapy, followed by > 10% improvement within the next 3 months) on survival using Kaplan-Meier curves and Cox proportional hazard analyses.

RESULTS

The flare phenomenon was found in 26 (36%) of the 72 patients. Prostate-specific antigen (PSA), alkaline phosphatase (ALP), and hemoglobin (Hb) levels steadily increased, then deteriorated in patients with and without flare, respectively. Elevated BSI and PSA values at 3 months after starting therapy and the absence of abiraterone or/and enzalutamide therapy led to poor 2-year overall survival (OS) in the group without flare. In contrast, no influence was noticeable in the group with flare. The results of multivariable analyses that included only factors associated with PSA and BSI showed that increased baseline BSI (hazard ratio [HR], 1.39; 95% confidence interval [CI], 1.04-1.86; P = 0.023) and PSA (HR, 7.15; 95% CI 2.13-24.04; P = 0.0015) values could be independent risk factors for patients with mCRPC without flare. However, these factors lost significance during flare. The risk for all-cause death was significantly higher among patients with BSI > 4 without, than with flare. The results of univariable analyses indicated that flare positively impacted survival (HR, 0.24; 95% CI 0.06‒0.91; P = 0.035). Multivariable analysis did not identify any factors that could predict outcomes.

CONCLUSION

Favorable prognosis, with fewer disturbances from other factors such as the use of abiraterone or/and enzalutamide, PSA changes, and BSI, was attainable in cases when the mCRPC patient demonstrated flare phenomenon. Follow-up bone scintigraphy at least every 3 months could help to determine the prognosis of patients with bone metastasis of mCRPC.

Bone Metastasis in Prostate Cancer: Bone Scan Versus PET Imaging

Prostate cancer is the second most common cause of malignancy among men, with bone metastasis being a significant source of morbidity and mortality in advanced cases. Detecting and treating bone metastasis at an early stage is crucial to improve the quality of life and survival of prostate cancer patients. This objective strongly relies on imaging studies. While CT and MRI have their specific utilities, they also possess certain drawbacks. Bone scintigraphy, although cost-effective and widely available, presents high false-positive rates. The emergence of PET/CT and PET/MRI, with their ability to overcome the limitations of standard imaging methods, offers promising alternatives for the detection of bone metastasis. Various radiotracers targeting cell division activity or cancer-specific membrane proteins, as well as bone seeking agents, have been developed and tested. The use of positron-emitting isotopes such as fluorine-18 and gallium-68 for labeling allows for a reduced radiation dose and unaffected biological properties. Furthermore, the integration of artificial intelligence (AI) and radiomics techniques in medical imaging has shown significant advancements in reducing interobserver variability, improving accuracy, and saving time. This article provides an overview of the advantages and limitations of bone scan using SPECT and SPECT/CT and PET imaging methods with different radiopharmaceuticals and highlights recent developments in hybrid scanners, AI, and radiomics for the identification of prostate cancer bone metastasis using molecular imaging.

Clinical Significance of the Highest Regional Bone Scan Index in Patients with Metastatic Castration-Resistant Prostate Cancer

Purpose

This study evaluated the clinical utility of the highest bone scan index (BSI), among other BSIs, for each bone metastatic site in patients with bone metastatic castration-resistant prostate cancer (bmCRPC).

Methods

Thirty patients, diagnosed with bmCRPC by bone scintigraphy, were included. Total BSI, the number of hot spots, and regional BSI on each hot spot from bone scintigraphy at diagnosis with bmCRPC were evaluated by VSBONE BSI®. Highest regional BSI was defined as the highest value among regional BSIs on each hot spot in each patient. Related factors to overall survival and skeletal-related events (SREs) were evaluated using the Cox proportional-hazards model.

Results

The median follow-up time from diagnosis with bmCRPC was 29.0 months. During this time, 24 patients died, of which 22 patients died from prostate cancer. On univariate analysis, alkaline phosphatase (ALP) [Hazard ratio (HR): 5.96, 95% confidence interval (CI): 2.05-17.3] and highest regional BSI (HR: 2.01, 95% CI: 1.17-7.05) had significant correlation with overall survival. On multivariate analysis, ALP (HR: 4.79, 95% CI: 1.61-14.2) had significant correlation with overall survival. SREs were found in eight patients. Only the highest regional BSI (HR: 9.99, 95% CI: 2.46-40.6) significantly correlated with SREs on univariate analysis.

Conclusion

Highest regional BSI may provide important information regarding prognosis and SREs in patients with bmCRPC.

The modern therapeutic & imaging landscape of metastatic prostate cancer: a primer for radiologists

Prostate cancer represents one of the leading causes of cancer-related mortality in the United States and the most common cancer among men. Treatment paradigms for the management of advanced stages of prostate cancer have continued to evolve in recent years. These advancements in the therapeutic landscape of metastatic prostate cancer and diagnostic imaging modalities have fundamentally changed the treatment of patients with prostate cancer. In this review article we provide a primer for radiologists highlighting the most recent developments in treatment options and imaging techniques utilized in the modern oncologic management of metastatic prostate cancer. We will examine current therapy options and associated toxicities with an emphasis on relevant imaging findings commonly encountered by radiologists. We also summarize the role of modalities including CT, MRI, PET, bone scintigraphy, and PET in the diagnosis and follow-up of patients with metastatic prostate cancer.

Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer

PET imaging with targeted novel tracers has been commonly used in the clinical management of prostate cancer. The use of artificial intelligence (AI) in PET imaging is a relatively new approach and in this review article, we will review the current trends and categorize the currently available research into the quantification of tumor burden within the organ, evaluation of metastatic disease, and translational/supplemental research which aims to improve other AI research efforts.

An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer

Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient's medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method.

Revealing Prognostic Value of Skeletal-Related Parameters in Metastatic Castration-Resistant Prostate Cancer on Overall Survival: A Systematic Review and Meta-Analysis of Randomized Controlled Trial

BACKGROUND

The skeleton is a preferred site for prostate cancer metastasis, and once metastases occur, the disease becomes incurable. Increasing evidence indicates the prognostic value of skeletal-related parameters, but remains controversial.

OBJECTIVE

To perform a systematic review of the existing literature on assessing the prognostic value of alkaline phosphatase (ALP), bone-specific alkaline phosphatase (BSAP), urinary N-telopeptide (uNTx), bone scan index (BSI), and Brief Pain Inventory Short Form (BPI-SF) score in castration-resistant prostate cancer (CRPC) patients with skeleton metastasis.

EVIDENCE ACQUISITION

PubMed, Web of Science, Cochrane Library, Medline, OVID, and Embase between 2010 and 2019 were reviewed. Key terms included randomized trials, prostate cancer, alkaline phosphatase, bone-specific alkaline phosphatase, urinary N-telopeptide, bone scan index, and Brief Pain Inventory Short Form. Data were collected, checked, and analyzed from December 2019 to March 2020. Hazard ratios (HRs) and overall survival (OS) were extracted to estimate the relationship between the above parameters and OS in patients with metastatic prostate cancer (mPCa).

EVIDENCE SYNTHESIS

A total of 1,055 studies were identified via initial screening, including 1,032 from database research and 23 from other sources. After deduplication, 164 records were further excluded according to titles and abstracts. The remaining 36 potential articles were carefully screened. In the end, 15 eligible studies syntheses, which were published between 2010 and 2019, comprised data for a total of 11,378 patients, whose mean age ranged from 66 to 72 years. The sample size ranged from 82 to 1,901 patients. And the median follow-up time ranged from 24 to 55 months. Based on 15 randomized controlled trials published between 2010 and 2019, higher ALP levels (HR = 1.60, 95% CI: 1.38-1.87 P < 0.001), higher BSAP levels (HR = 1.31, 95% CI: 1.11-1.54 P = 0.001), higher uNTx levels (HR = 1.40, 95% CI: 1.29-1.52 P < 0.001), BSI progression (HR = 1.18, 95% CI: 1.08-1.29 P < 0.001), and higher BPI-SF score (HR = 1.47, 95% CI: 1.35-1.61 P < 0.001) had an association with inferior OS.

CONCLUSIONS

Higher levels of ALP/BSAP and uNTx, a higher BPI-SF score, and progression of BSI predict inferior OS in patients with mCRPC. More randomized control trials are needed to investigate the promising value of these parameters.

Prognostic value of the bone scan index in patients with metastatic castration-resistant prostate cancer: a systematic review and meta-analysis

Background

Many studies have reported the prognostic significance of the bone scan index (BSI) for metastatic castration-resistant prostate cancer (mCRPC); however, these reports are controversial. This study investigated the BSI in mCRPC and its relationship with prognosis.

Methods

The PubMed, Cochrane, and Embase databases were searched systematically for relevant articles published before September 1, 2019. Hazard ratios (HRs) were used to investigate the prognostic value.

Results

This study finally identified 9 eligible studies. The results suggested that high baseline BSI predicted poor OS (HR = 1.331, 95% CI: 1.081–1.640) and that elevated ΔBSI also predicted poor OS (HR = 1.220, 95% CI: 1.015–1.467). The subgroup analysis stratified by ethnicity showed that the baseline BSI and ΔBSI predicted poor OS in the Asian population but not in the Caucasian population. We also performed a subgroup analysis based on the different cut-off values of baseline BSI. The subgroup of ≤1 showed a significant association with OS in mCRPC patients.

Conclusion

Our study demonstrated that high baseline BSI and elevated ΔBSI predicted poor OS in patients with mCRPC. Hence, the BSI can serve as a prognostic indicator for mCRPC patients and may therefore guide clinical treatment in the future.

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Optimum Imaging Strategies for Advanced Prostate Cancer: ASCO Guideline

PURPOSE

Provide evidence- and expert-based recommendations for optimal use of imaging in advanced prostate cancer. Due to increases in research and utilization of novel imaging for advanced prostate cancer, this guideline is intended to outline techniques available and provide recommendations on appropriate use of imaging for specified patient subgroups.

METHODS

An Expert Panel was convened with members from ASCO and the Society of Abdominal Radiology, American College of Radiology, Society of Nuclear Medicine and Molecular Imaging, American Urological Association, American Society for Radiation Oncology, and Society of Urologic Oncology to conduct a systematic review of the literature and develop an evidence-based guideline on the optimal use of imaging for advanced prostate cancer. Representative index cases of various prostate cancer disease states are presented, including suspected high-risk disease, newly diagnosed treatment-naïve metastatic disease, suspected recurrent disease after local treatment, and progressive disease while undergoing systemic treatment. A systematic review of the literature from 2013 to August 2018 identified fully published English-language systematic reviews with or without meta-analyses, reports of rigorously conducted phase III randomized controlled trials that compared ≥ 2 imaging modalities, and noncomparative studies that reported on the efficacy of a single imaging modality.

RESULTS

A total of 35 studies met inclusion criteria and form the evidence base, including 17 systematic reviews with or without meta-analysis and 18 primary research articles.

RECOMMENDATIONS

One or more of these imaging modalities should be used for patients with advanced prostate cancer: conventional imaging (defined as computed tomography [CT], bone scan, and/or prostate magnetic resonance imaging [MRI]) and/or next-generation imaging (NGI), positron emission tomography [PET], PET/CT, PET/MRI, or whole-body MRI) according to the clinical scenario.

PF4V1, an miRNA-875-3p target, suppresses cell proliferation, migration, and invasion in prostate cancer and serves as a potential prognostic biomarker

Background

PF4V1 is a novel protein in inflammation, angiogenesis, and cancer. However, the pathogenesis, underlying mechanisms, and the prognostic value of PF4V1 in prostate cancer (PCa) are still unclear.

Materials and methods

The PF4V1 expression and relation with survival were analyzed based on a large sample size in the Cancer Genome Atlas. In vitro, the overexpression of PF4V1 was conducted in DU145 and LNCaP cells. Cell Counting Kit-8, colony formation, wound healing, and Transwell^® assays were preformed to test biological functions of PF4V1 and miR-875-3p in PCa. Western blotting was used to measure downstream markers in AKT pathways and epithelial–mesenchymal transition (EMT). In vivo experiments were performed to test the therapeutic effect of PF4V1 protein to PCa via a mouse model.

Results

The expression of PF4V1 was significantly lower in 497 PCa samples than in 52 normal controls (P=0.0012). High PF4V1 expression (normalized by TP53) was associated with poor disease-free survival (DFS) and good overall survival (OS) in PCa (P<0.05). PF4V1 was underexpressed in four PCa cell lines than in normal prostate cells. Overexpression of PF4V1 could significantly suppress the proliferation, migration, and invasion of DU145 and LNCaP cells (P<0.05). Moreover, miR-875-3p targeted the 3′-untranslated region of PF4V1 and derepressed the inhibitory function of PF4V1 in PCa (P<0.05). Key proteins such as p-AKT/p-ERK/Snail/Slug/N-cadherin were downregulated, while E-cadherin was upregulated when PF4V1 was overexpressed in PCa cells. Finally, intratumoral injection of PF4V1 protein could significantly inhibit PCa growth in vivo.

Conclusion

PF4V1 can suppress the proliferation, migration, and invasion of PCa cells by regulating AKT/ERK pathways and EMT. Elevated PF4V1/TP53 expression is correlated with poorer DFS and better OS in the patients with PCa. The miR-875-3p-PF4V1 axis may be a new therapeutic target site in PCa.

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.

Prognostic value of serum alkaline phosphatase in the survival of prostate cancer: evidence from a meta-analysis

BACKGROUND

Many studies have evaluated the relationship between alkaline phosphatase (ALP) and the prognosis for prostate cancer (PCa). But they have not reached a widespread consensus yet. Therefore, we completed a meta-analysis to ascertain the significance of ALP and the prognosis for PCa.

METHODS

A literature search was performed in the PubMed, Embase, and Web of Science databases. HRs concerning overall survival (OS), progression-free survival (PFS), and cancer-specific survival (CSS) were extracted to evaluate the impacts of ALP on the prognosis for PCa. Subgroup analyses were conducted on different study types, regions, sample sizes, and cutoff values. Sensitivity analysis was performed by removing one study in sequence.

RESULTS

A total of 63 studies from 54 articles with 16,135 patients were included in this meta-analysis. The pooled results indicated that high baseline ALP was associated with obviously poor OS (HR=1.74, 95% CI: 1.47-2.06) and PFS (HR=1.60, 95% CI: 1.13-2.26) in patients with PCa. The pooled HR for bone-specific ALP and OS was 1.76 (95% CI: 1.45-2.15). However, no association between ALP and CSS (HR=1.002, 95% CI: 0.998-1.005) was found for PCa. The results of subgroup analyses were all in accordance with the main findings. Sensitivity analysis suggested that no single study could affect the stability of the results.

CONCLUSION

High serum ALP is significantly associated with poor OS and PFS except for CSS in PCa. ALP is an efficient and convenient biomarker for PCa prognosis.

Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioinformatics Methods

OBJECTIVE

To identify key microRNAs (miRNAs) and their regulatory networks in prostate cancer.

METHODS

Four miRNA and three gene expression microarray datasets were downloaded for analysis from Gene Expression Omnibus database. The differentially expressed miRNA and genes were accessed by a GEO2R. Functional and pathway enrichment analyses were performed using the DAVID program. Protein-protein interaction (PPI) and miRNA-mRNA regulatory networks were constructed using the STRING and Cytoscape tool. Moreover, the results and clinical significance were validated in TCGA data.

RESULTS

We identified 26 significant DEMs, 633 upregulated DEGs, and 261 downregulated DEGs. Functional enrichment analysis indicated that significant DEGs were related to TGF-beta signaling pathway and TNF signaling pathway in PCa. Key DEGs such as HSPA8, PPP2R1A, CTNNB1, ADCY5, ANXA1, and COL9A2 were found as hub genes in PPI networks. TCGA data supported our results and the miRNAs were correlated with clinical stages and overall survival.

CONCLUSIONS

We identified 26 miRNAs that may take part in key pathways like TGF-beta and TNF pathways in prostate cancer regulatory networks. MicroRNAs like miR-23b, miR-95, miR-143, and miR-183 can be utilized in assisting the diagnosis and prognosis of prostate cancer as biomarkers. Further experimental studies are required to validate our results.

A prospective study to evaluate the intra-individual reproducibility of bone scans for quantitative assessment in patients with metastatic prostate cancer

BACKGROUND

The Bone Scan Index (BSI) is used to quantitatively assess the total tumour burden in bone scans of patients with metastatic prostate cancer. The clinical utility of BSI has recently been validated as a prognostic imaging biomarker. However, the clinical utility of the on-treatment change in BSI is dependent on the reproducibility of bone scans. The objective of this prospective study is to evaluate the intra-patient reproducibility of two bone scan procedures performed at a one-week interval.

METHODS

We prospectively studied prostate cancer patients who were referred for bone scintigraphy at our centres according to clinical routine. All patients underwent two whole-body bone scans: one for clinical routine purposes and a second one as a repeated scan after approximately one week. BSI values were obtained for each bone scintigraph using EXINI bone^BSI software.

RESULTS

A total of 20 patients were enrolled. There was no statistical difference between the BSI values of the first (median = 0.66, range 0-40.77) and second (median = 0.63, range 0-22.98) bone scans (p = 0.41). The median difference in BSI between the clinical routine and repeated scans was - 0.005 (range - 17.79 to 0). The 95% confidence interval for the median value was - 0.1 to 0. A separate analysis was performed for patients with BSI ≤ 10 (n = 17). Differences in BSI were smaller for patients with BSI ≤ 10 compared to the whole cohort (median - 0.1, range - 2.2-0, 95% confidence interval - 0.1 to 0).

CONCLUSIONS

The automated BSI demonstrated high intra-individual reproducibility for BSI ≤ 10 in the two repeated bone scans of patients with prostate cancer. The study supports the use of BSI as a quantitative parameter to evaluate the change in total tumour burden in bone scans.

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.