Predict Bone Metastasis Research Articles

New Approach to Predict Bone Metastasis in Breast Cancer Patients

New Approach to Predict Bone Metastasis in Breast Cancer Patients

CTNND1 to mediate bone metastasis of triple-negative breast cancer via regulating CXCR4.

e13045 Background: Compared to lumial breast cancer, the proporation of triple-negative breast cancer (TNBC) with bone metastases (BMs) is relatively low and few data focusing on the mechanism of the BMs in TNBC are available, Here, we screened that CTNND1 was associated with BMs of TNBC by integrating high-throughput sequencing, and further investigated the role of CTNND1 in BMs of TNBC in vitro. Methods: TNBC tissue samples with only BMs (n = 6) and without any metastasis (n = 10) were tested using high-throughput sequencing and 11 differentially expressed relative genes were identified. We then quantified these 11 genes in normal breast tissue samples (n = 26), TNBC tissue samples with only BMs (n = 10), TNBC tissue samples without any metastasis (n = 88) as well as luminal tissue samples with BMs（n = 10）through qPCR and immunohistochemical staining (IHC). The effects of knocking down CTNND1 on the interaction between TNBC cells and osteoblasts were examined by cell adhesion, transwell migration and matrigel invasion assays. To explorethe role of CTNND1 in mediating bone metastasis in TNBC, we used RNA-sequencing to find out the relative downstream gene CXCR4 and PI3K-AKT-mTOR pathway and verified it in vitro by Western Blotting. Results: Combining our high-throughput sequencing data, qPCR and IHC in clinical tissue samples, we verified that CTNND1 was decreased in TNBC patients with bone metastasis compared to normal tissue and luminal tissue with BMs. Knocking down of CTNND1 in TNBC cells including MDA-MB-231, MDA-MB-468 and BT549 weakened cells adhesion, but facilitated cells migration and invasion. Mechanically, knocking down of CTNND1 upregulated CXCR4 via activating PI3K-AKT-mTOR pathway in TNBC but not luminal and HER2- positive breast cancer cells lines. Conclusions: CTNND1 mediates bone metastasis in triple-negative breast cancer via regulating CXCR4.CTNND1 may serve as a potential predictor of bone metastasis for TNBC patients.

BRMS1L inhibits bone metastasis of breast cancer cells through epigenetic silence of CXCR4.

e13002 Background: We previously demonstrated that BRMS1L (breast cancer metastasis suppressor 1 like) suppresses breast cancer metastasis through HDAC1 recruitment and histone H3K9 deacetylation at the promoter of FZD10, a receptor for Wnt signaling. It is still unclear whether BRMS1L regulates organ-specific metastases, such as bone metastasis, the most prevalent metastatic site of breast cancer. Methods: Examination of the expression of BRMS1L in primary tumors, bone metastatic and other metastatic tissues from breast cancer patients was implemented using qRT-PCR and immunohistochemistry staining. To investigate the mechanism by which BRMS1L drives breast cancer bone metastasis, we tested the mRNA expression by qRT-PCR of a set of potential bone related genes (BRGs) based on PubMed database in MDA-MB-231 cells over expressing BRMS1L and MCF-7 cells knocking-down BRMS1L, and detected the expression of CXCR4 in these established cells by western blot. Transwell assays were performed to assess the migration abilities of breast cancer cells towards osteoblasts. ChIP (Chromatin Immuno-Precipitation) were employed to test the interaction between BRMS1L and CXCR4. Results: At both mRNA and protein levels, the expression of BRMS1L was significantly lower in bone metastatic sites than that in primary cancer tissues and other metastatic sites of breast cancer patients. CXCR4 was screened out in a set of BRGs and negatively correlated with the expression of BRMS1L in breast cancer cell lines. BRMS1L inhibited the migration of breast cancer cells towards osteoblasts through CXCL12/CXCR4 axis. In the presence of TSA treatment, breast cancer cell lines showed an increased expression of CXCR4 in a TSA concentration-dependent manner. In addition, ChIP assays verified that BRMS1L directly bound to the promoter region of CXCR4 and inhibited its transcription through promoter histone deacetylation. Conclusions: BRMS1L mediates the migration abilities of breast cancer cells to bone microenvironment via targeting CXCR4 and contributes to bone metastasis of breast cancer cells. Thus, BRMS1L may be a potential biomarker for predicting bone metastasis in breast cancer.

Predictive value of standard serum markers for bone metastases in prostate cancer

BackgroundThe early detection of bone metastases is very important in prostate cancer follow-up. This study aimed to compare conventional tumor markers, namely free prostate-specific antigen (free PSA), total prostate-specific antigen (total PSA), free PSA/total PSA ratio, alkaline phosphatase (ALP) values, Gleason scores and 99 m Tc-MDP bone scintigraphy findings in the prediction of bone metastases in prostate cancer.MethodsIn total, 175 patients with prostate cancer who underwent whole-body bone scintigraphy were included in the study. All selected scintigraphic studies were reprocessed. Free PSA, total PSA, free PSA/total PSA ratio, alkaline phosphatase (ALP) values and Gleason scores of patients were recorded.ResultsThe results of our study show that the presence of bone metastasis correlates very weakly with free PSA/total PSA ratio (rho = 0.179), weakly with total PSA (rho = 0.318) and Gleason score (rho = 0.382), moderately with ALP (rho = 0.539), free PSA (0.416). Only ALP variable had a diagnostic value and ALP cutoff value was 76.50 IU/L, with 80% sensitivity and 82.1% specificity.ConclusionAccording to the results of our study; the free PSA, total PSA, free PSA/total PSA ratio and Gleason score values were not considered as a reliable parameter in the prostate cancer cases follow-up for bone metastasis development. Only ALP had a diagnostic value and ALP cutoff value was 76.50 IU / L with 80% sensitivity and 82.1% specificity in predicting bone metastases in prostate cancer.

Skeletal system as a rare metastatic site in patients with ovarian carcinoma

ObjectiveTo evaluate the frequency and predictors of bone metastasis in patients with ovarian cancer and to determine prognostic factors associated with this finding.MethodsPatients diagnosed with ovarian cancer between January 2009...

Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

ObjectivesThis study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC).MethodsDemographic and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine‐learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model.ResultsA total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC: 0.917, accuracy: 0.904, recall rate: 0.833, and specificity: 0.905).ConclusionsThe RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision‐making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients.

Establishment and validation of a novel predictive model to quantify the risk of bone metastasis in patients with prostate cancer.

BackgroundPatients with prostate cancer (PCa) commonly suffer from bone metastasis during disease progression. This study aims to construct and validate a nomogram to quantify bone metastasis risk in patients with PCa.MethodsClinicopathological data of patients diagnosed with PCa between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Predictors for bone metastasis were identified by logistic regression analyses to establish a nomogram. The concordance index (c-index) and calibration plots were generated to assess the nomogram’s discrimination, and the area under the receiver operating characteristic curve (AUC) was used to compare the precision of the nomogram with routine staging systems. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA) and clinical impact curves (CIC). Independent prognostic factors were identified by Cox regression analysis.ResultsA total of 168,414 eligible cases were randomly assigned to the training cohort or validation cohort at a ratio of 1:1. The nomogram, which was established based on independent factors, showed good accuracy, with c-indexes of 0.911 in the training set and 0.910 in the validation set. Calibration plots also approached 45 degrees. After other distant metastatic sites were included in the predictive model, the new nomogram displayed superior prediction performance. The AUCs and net benefit of the nomograms were both higher than those of other routine staging systems. Furthermore, bone metastasis prediction points were shown to be a new risk factor for overall survival.ConclusionsNovel validated nomograms can effectively predict the risk of bone metastasis in patients with PCa and help clinicians improve cancer management.

Construction of Nomograms for Predicting Lung and Bone Metastases in Patients with Intrahepatic Cholangiocarcinoma and Identification of Patients Who Can Benefit from Chemotherapy

Objective The purpose of our study is to build nomograms for predicting the possibility of lung metastasis (LM) and bone metastasis (BM) in patients with intrahepatic cholangiocarcinoma (ICC). Methods 1527 patients diagnosed with ICC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable logistic regression analyses were used to recognize the predictors of LM and BM, respectively. Then two nomograms were established. We applied the C-index, calibration plot, receiver-operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the novel nomograms. The maximum values of the Youden indexes from the ROC curves were utilized to select the cutoff points of the nomograms. The Kaplan–Meier survival curves were used to evaluate the effect of chemotherapy in different groups. The bootstrap resampling method was chosen for internal validation. Results Five predictors for LM and three predictors for BM were identified, and two nomograms were constructed. The nomograms had high values of C-indexes, reaching 0.821 (95% CI 0.772–0.871) for LM and 0.759 (95% CI 0.700–0.818) for BM. C-indexes of 0.814 for LM and 0.749 for BM were also observed in internal validation. The calibration plots, ROC curves, and DCAs exhibited favorable performances for predicting LM and BM. The cutoff points of total points in nomograms were 108 for LM and 144 for BM, which could distinguish between high-risk and low-risk groups for LM and BM. Chemotherapy is suggested to undergo for patients in high-risk groups. Conclusions The nomograms could assess the possibility of LM and BM in ICC patients and determine the optimal treatment.

Risk factors, prognostic factors, and nomograms for bone metastasis in patients with newly diagnosed infiltrating duct carcinoma of the breast: a population-based study

BackgroundBreast cancer is the most common malignancy in women, and it is also the leading cause of death in female patients; the most common pathological type of BC is infiltrating duct carcinoma (IDC). Some nomograms have been developed to predict bone metastasis (BM) in patients with breast cancer. However, there are no studies on diagnostic and prognostic nomograms for BM in newly diagnosed IDC patients.MethodsIDC patients with newly diagnosed BM from 2010 to 2016 in the Surveillance, Epidemiology and End Results (SEER) database were reviewed. Multivariate logistic regression analysis was used to identify risk factors for BM in patients with IDC. Univariate and multivariate Cox proportional hazards regression analysis were used to explore the prognostic factors of BM in patients with IDC. We then constructed nomograms to predict the risk and prognosis of BM for patients with IDC. The results were validated using bootstrap resampling and retrospective research on 113 IDC patients with BM from 2015 to 2018 at the Affiliated Hospital of Chengde Medical University.ResultsThis study included 141,959 patients diagnosed with IDC in the SEER database, of whom 2383 cases were IDC patients with BM. The risk factors for BM in patients with IDC included sex, primary site, grade, T stage, N stage, liver metastasis, race, brain metastasis, breast cancer subtype, lung metastasis, insurance status, and marital status. The independent prognostic factors were brain metastases, race, grade, surgery, chemotherapy, age, liver metastases, breast cancer subtype, insurance status, and marital status. Through calibration, receiver operating characteristic curve and decision curve analyses, we found that the nomogram for predicting the prognosis of IDC patients with BM displayed great performance both internally and externally.ConclusionThese nomograms are expected to be a precise and personalized tool for predicting the risk and prognosis for BM in patients with IDC. This will help clinicians develop more rational and effective treatment strategies.

Perineural invasion by prostate adenocarcinoma in needle biopsies predicts bone metastasis: Ten year data from the TROG 03.04 RADAR Trial.

Perineural invasion (PNI) by prostatic adenocarcinoma is debated as a prognostic parameter. This study investigates the prognostic predictive value of PNI in a series of patients with locally advanced prostate cancer treated with radiotherapy and androgen deprivation using 10years outcome data from the TROG 03.04 RADAR trial. Diagnostic prostate biopsies from 976 patients were reviewed and the presence of PNI noted. Patients were followed for 10years according to the trial protocol or until death. The primary endpoint for the study was time to bone metastasis. Secondary endpoints included time to soft tissue metastasis, transition to castration resistance, prostate cancer-specific mortality and all-cause mortality. PNI was detected in 449 cases (46%), with 234 cases (24%) having PNI in more than one core. The presence of PNI was significantly associated with higher ISUP grade, clinical T staging category, National Comprehensive Cancer Network risk group, and percent positive biopsy cores. The cumulative probability of bone metastases according to PNI status was significant over the 10years follow-up interval of the study (log-rank test P<0.0001). PNI was associated with all endpoints on univariable analysis. After adjusting for baseline clinicopathological and treatment factors, bone metastasis was the only endpoint in which PNI retained its prognostic significance (hazard ratio 1.42, 95% confidence interval 1.05-1.92, P=0.021). The association between PNI and the development of bone metastases supports the inclusion of this parameter as a component of the routine histology report. Further this association suggests that evaluation of PNI may assist in selecting those patients who should be monitored more closely during follow-up.

Prostat kanseri tanısında kan nötrofil / lenfosit dağılımının yeri

Objective: Neutrophil to lymphocyte ratio (NLR) is a recently popular diagnostic marker. We sought answers to questions such as whether this marker has a role in the diagnosis of prostate cancer, any cut off value can be calculated, compared to platelet lymphocyte ratio (PLR) and it has a relationship with Gleason score and percentage of tumors detected in biopsy cores. In addition, we aimed to share the findings of patients undergoing transrectal ultrasound (TRUS) guided biopsy. Materials and Methods : The results of 679 patients who underwent TRUS-guided prostate biopsy with prostate specific antigen (PSA) values above 2.5 ng / ml in our clinic between August 2014 and February 2017 were retrospectively analyzed. Patients were between 2.5-3.9 ng / ml (Group 1), 4-9.9 ng / ml (Group 2), 10-19.9 ng / ml (Group 3), 20 ng / ml and above (Group 4) according to PSA values, divided into four groups. The diagnostic value of NLR was evaluated in these groups. Results: NLR was found as a diagnostic marker in groups 2,3 and 4. The cut off value was calculated as 2.5. It had similar diagnostic power as PLR. There was a poor correlation with Gleason score and percentage of tumor in cores. It was not important in predicting bone metastasis. The difference between PSA density and DRE was statistically significant in groups with pathologic prostate cancer (PCa) or benign prostatic hyperplasia (BPH). Conclusion: Although NLR is not very strong in the diagnosis of PCa, it has a diagnostic value. It can be used as a marker to support PSA.

Current and Emerging Biomarkers Predicting Bone Metastasis Development.

Bone is one of the preferential sites of distant metastases from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Patients with bone metastases (BMs) may experience skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with negative effects on the quality of life. In the last decades, a deeper understanding of the molecular mechanisms underlying the BM onset has been gained, leading to the development of bone-targeting agents. So far, most of the research has been focused on the pathophysiology and treatment of BM, with only relatively few studies investigating potential predictors of risk for BM development. The ability to select such “high-risk” patients could allow early identification of those most likely to benefit from interventions to prevent or delay BM. This review summarizes several evidences for the potential use of specific biomarkers able to predict early the BM development.

A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients

PurposeTo establish and validate a radiomics nomogram for predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). MethodOne-hundred and sixteen patients (training cohort: n = 81; validation cohort: n = 35) who underwent prostate MR imaging and confirmed by pathology with newly diagnosed PCa from January 2014 to January 2019 were enrolled. Radiomic features were extracted from diffusion-weighted, axial T2-weighted fat suppression, and dynamic contrast-enhanced T1-weighted MRI of each patient. Dimension reduction, feature selection, and radiomics feature construction were performed using the least absolute shrinkage and selection operator (LASSO) regression. Combined with independent clinical risk factors, a multivariate logistic regression model was used to establish a radiomics nomogram. Nomogram calibration and discrimination were evaluated in training cohort and verified in the validation cohort. Finally, the clinical usefulness of the nomogram was estimated through decision curve analysis (DCA). ResultsRadiomics signature consisting of 12 selected features was significantly correlated with bone status (P < 0.001 for both training and validation sets). The radiomics nomogram combined a radiomics signature from multiparametric MR images with independent clinic risk factors. The model showed good discrimination and calibration in the training cohort (AUC 0.93, 95% CI, 0.86 to 0.99) and the validation cohort (AUC 0.92, 95% CI, 0.84 to 0.99). DCA also demonstrated the clinical use of the radiomics model. ConclusionThe radiomics nomogram, which incorporates the multiparametric MRI-based radiomics signature and clinical risk factors, can be conveniently used to promote individualized prediction of BM in patients with newly diagnosed PCa.

Abstract P3-01-27: Ectonucleotidase ATP hydrolysis facilitates breast cancer bone metastasis

Abstract Bone is the most common site for breast cancer metastasis. Osteocytes are the most abundant bone cell and can signal nearby cells by releasing ATP through connexin 43 hemichannels. We previously found that this signaling pathway hindered breast cancer growth in bone. In this study, we investigated the role of purinergic signaling on breast cancer bone metastasis in patient samples. Surprisingly, no ATP receptors were differentially expressed in bone metastases compared to other sites. However, enzymes that degrade ATP into adenosine (CD39/ENTPD1 and CD73/NT5E) were upregulated, as was the ADORA3 adenosine receptor (Limma, p=0.037, 0.016, 0.0019, respectively). We next investigated whether this expression signature in primary tumors predicts bone metastasis. In primary ER+ breast cancer, which predominately metastasizes to bone, high expression of these genes is correlated with lower distant metastasis-free survival (HR = 1.97, 1.49, 2.03, logrank p=0.0017, 0.0004, 0.0012). Moreover, this expression pattern is correlated with a bone metastasis signature (Spearman R=0.28, p=1.2 × 10−20). In a pharmacologic screen of 750 cancer cell lines, breast cancer lines and especially ER+ ones, are uniquely sensitive to a pan-adenosine receptor inhibitor (Limma, p=2.9 × 10−5) and ADORA3/2B inhibitor (p=5.4 × 10−5). Our findings suggest that an expression program to convert ATP to adenosine facilitates bone metastasis and begins in the primary tumor. ADORA3 inhibition is proposed as a pharmacologic strategy for preventing or treating bone metastasis. Furthermore, bone metastases should be analyzed in anti-CD39 and CD73 clinical trials. Citation Format: Daniel B Shropshire, Jaime Benavides, Jean X Jiang. Ectonucleotidase ATP hydrolysis facilitates breast cancer bone metastasis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-01-27.

Correlation between prostate-specific antigen and bone scan findings in patients with prostate cancer at Grey’s Hospital, KwaZulu-Natal: A retrospective chart review

Many studies have indicated that prostate-specific antigen (PSA) is used for the detection and monitoring of prostate cancer. We analysed PSA levels and bone scan findings to determine whether PSA can accurately predict bone metastases. A study was conducted at Grey’s Hospital, a public hospital in Pietermaritzburg, KwaZulu-Natal, South Africa. A total of 84 patients were included in this study: 61 (72.62%) black patients, 14 (16.67%) Indian and 9 (10.71%) white patients. Our study indicated a high correlation between PSA level and bone scan finding. We arrived at a conclusion that at PSA level > 24 ng/mL, bone scan is highly recommended.

Prediction of Bone Metastasis in Breast Cancer Based on Minimal Driver Gene Set in Gene Dependency Network.

Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and the risk of bone metastasis might be affected by another gene. Then, based on the structure controllability theory, we mined the driver gene set which can control the whole network in the gene dependency network, and the signature genes were selected from them. Survival analysis showed that the signature could distinguish the bone metastasis risks of cancer patients in the test data set and independent data set. Besides, we used the signature genes to construct a centroid classifier. The results showed that our method is effective and performed better than published methods.

Can Neutrophil/Lymphocyte and Platelet/Lymphocyte Rates Predict Bone Metastasis in Prostate Cancer Patients?

Can Neutrophil/Lymphocyte and Platelet/Lymphocyte Rates Predict Bone Metastasis in Prostate Cancer Patients?

Nomogram based on homogeneous and heterogeneous associated factors for predicting bone metastases in patients with different histological types of lung cancer

BackgroundThe purpose of the present study was to characterize the prevalence, associated factors, and to construct a nomogram for predicting bone metastasis (BM) with different histological types of lung cancer.Patients and methodsThis study was a descriptive study that basing on the invasive lung cancer patients diagnosed between 2010 and 2014 in Surveillance, Epidemiology, and End Results program. A total of 125,652 adult patients were retrieved. Logistic regression analysis was conducted to investigate homogeneous and heterogeneous factors for BM occurrence. Nomogram was constructed to predict the risk for developing BM and the performance was evaluated by the receiver operating characteristics curve (ROC) and the calibration curve. The overall survival of the patients with BM was analyzed using the Kaplan–Meier method and the survival differences were tested by the log-rank test.ResultsA total of 25,645 (20.9%) were reported to have BM, and the prevalence in adenocarcinoma, squamous cell carcinoma, small cell lung cancer (SCLC), large cell lung cancer (LCLC), and non-small cell lung cancer/not otherwise specified lung cancer (NSCLC/NOS) were 24.4, 12.5, 24.7, 19.5 and 19.4%, respectively, with significant difference (P < 0.001). Male gender, more metastatic sites and lymphatic metastasis were positively associated with BM in all lung cancer subtypes. Larger tumor size was positively associated with BM in all the lung cancer subtypes except for NSCLC/NOS. Poorly differentiated histology was positively associated with adenocarcinoma, squamous cell carcinoma and NSCLC/NOS. The calibration curve and ROC curve exhibited good performance for predicting BM. The median survival of the bone metastatic lung cancer patients was 4.00 (95%CI: 3.89–4.11) months. With the increased number of the other metastatic sites (brain, lung and liver metastasis), the survival significantly decreased (p < 0.001).ConclusionDifferent lung cancer histological subtypes exhibited distinct prevalence and homogeneity and heterogeneity associated factors for BM. The nomogram has good calibration and discrimination for predicting BM of lung cancer.

Role of Neutrophil to Lymphocyte Ratio or Platelet to Lymphocyte Ratio in Prediction of Bone Metastasis of Prostate Cancer.

Accumulating evidence has revealed that inflammation might play an important role in the genesis and development of cancer. High levels of neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ration (PLR) are parameters of systemic inflammation which have been identified to be associated with poor prognosis in PCa. Bone is one of the most common sites of metastasis from prostate cancer; however, there are few studies concerning the correlation of NLR, PLR, and bone metastases in PCa. The aim of this study was to evaluate the performance of neutrophil to lymphocyte ratio (NLR) or platelet to lymphocyte (PLR) in diagnosis of bone metastasis of prostate cancer (PCa). Data of 74 PCa patients without metastases, 51 PCa patients with bone metastases, and 43 patients with benign prostatic hypertrophy (BPH) were retrospectively reviewed. The difference of patients' clinical and laboratory characteristics of the three groups was comparatively studied. ROC analysis was used to evaluate the benefit of adding NLR or PLR to prostate specific antigen (PSA) in prediction of bone metastases. Depending on this cutoff value, patients were divided into high-NLR or low-NLR group, high-PLR or low-PLR group. There were significant differences in NLR and PLR between groups with bone metastases and without bone metastases (p = 0.044; p = 0.030), while there was no significant difference between NLR and PLR of the patients with localized prostate cancer and BPH (p = 0.462; p = 0.102). NLR and PLR were correlated with PSA level in the patients with prostate cancer (p = 0.006, r = 0.247; p = 0.025, r = 0.200). The distribution of PSA showed significant differences between the high-NLR and low-NLR group, as well as between the high-PLR and low-PLR group. By applying the ROC curve method, the AUC values of PSA with NLR or PLR were 0.725 and 0.838 (0.763 - 0.913), respectively. Although PSA + PLR had the largest area, there was no statistical significance between PSA + PLR and PSA (p = 0.6992). NLR and PLR significantly increase in PCa patients with bone metastases and are valuable in the diagnosis of bone metastases in PCa patients.

P081 - Predicting bone metastasis on pretreatment imaging in men with intermediate or high risk prostate cancer: A nationwide, population-based study

P081 - Predicting bone metastasis on pretreatment imaging in men with intermediate or high risk prostate cancer: A nationwide, population-based study