Risk Factors For Bone Metastasis Research Articles

Early diagnostic value of ECT whole-body bone imaging combined with PINP and β-CTX for bone metastasis of lung cancer.

This research was aimed at investigating the early diagnostic value of emission computed tomograph (ECT) whole-body bone imaging combined with PINP and β-CTX for bone metastasis of lung cancer. Case data of 86 lung cancer patients were categorized into lung cancer with bone metastasis (LCWBM, 46 cases) and lung cancer without bone metastasis (LCWOBM, 40 cases) groups according to the presence or absence of bone metastasis. Patients' general information were collected. ECT whole-body bone imaging was used to detect bone metastases and the grading of the extent of disease (EOD) in both groups, and electrochemiluminescence was utilized to detect the serum levels of PINP and β-CTX. Spearman correlation analysis was employed to evaluate the correlation between EOD grading and PINP and β-CTX levels. Logistic univariate and multivariate regression was implemented to analyze the risk factors of bone metastasis of lung cancer. Receiver operating characteristic (ROC) curve was applied to analyze the diagnostic efficacy of the single test of ECT whole-body bone imaging, PINP, or β-CTX and the combination of the three tests. The differences in pathological type, clinical stage and EOD grading, the number of positive ECT cases, and the expression levels of PINP and β-CTX between the LCWBM and LCWOBM groups were statistically significant. In LCWBM patients with different EOD grading, the trends of the expression of PINP and β-CTX were grade 3 > grade 2 > grade 1 and grade 0. Further correlation analyses revealed that EOD grading showed a significant positive correlation with the PINP and β-CTX expression levels. Univariate logistic regression analysis demonstrated that adenocarcinoma, TNM stage IV, ECT positivity, and high expression of PINP and β-CTX were associated with bone metastasis of lung cancer, and multivariate logistic regression analysis indicated that ECT positivity, high expression of PINP and β-CTX were independent risk factors for bone metastasis of lung cancer. The area under the curve (AUC) of ECT, PINP, and β-CTX alone for the diagnosis of bone metastasis of lung cancer were 0.872, 0.888, and 0.874, respectively, and the AUC for the combined diagnosis of the three was 0.963, which was greater than that of any one of the individual indices, with a sensitivity of 86.96% and a specificity of 97.50% at a Youden index of 0.845. ECT whole-body bone imaging combined with PINP and β-CTX has high diagnostic value for bone metastasis of lung cancer.

Machine Learning Models to Predict Bone Metastasis Risk in Patients With Lung Cancer.

The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone. Patients with either histological or radiological diagnoses of lung cancer were included in this study. The patient cohort comprised 1864 patients diagnosed from 2016 to 2021. A total of 25 variables were considered as potential risk factors. These variables have been identified in previous studies as independent risk factors for bone metastasis. Treatment methods for lung cancer were taken into account during model development. The outcome variable was binary, (presence or absence of bone metastasis) with follow-up until death or 12-month survival, whichever is the sooner. Results showed that American Joint Committee on Cancer staging, the use of EGFR inhibitor, age, T-staging, and lymphovascular invasion were the five input features contributing the most to the model algorithm. High AJCC staging (OR 1.98; p < 0.05), the use of EGFR inhibitor (OR 6.14; p < 0.05), high T-staging (OR 1.47; p < 0.05), and the presence of lymphovascular invasion (OR 4.92; p < 0.05) increase predicted risk of bone metastasis. Conversely, older age reduces predicted bone metastasis risk (OR 0.98; p < 0.05). The machine learning model developed in this study can be easily incorporated into the hospital's Clinical Management System so that input variables can be immediately utilized to give an accurate prediction of bone metastatic risk, therefore informing clinicians on the best treatment strategy for that individual patient.

Identification Of Risk Factors Of Bone Metastasis Among Iraqi Breast Cancer Women

Background: The most common site of metastases in people with breast cancer is bone metastasis. Patients with advanced phases of cancer are associated with an increased prevalence of several types of metastases, one of which is bone metastasis. Object: Assessment of the risk factors for bone metastases in patients with breast cancer. Patients and Methods: This is a retrospective study of 316 patients with breast cancer who either had bone metastases at the time of diagnosis or developed it later on. The study was carried out between January 2013 and April 2018. Three hundred and sixteen patients were assigned into two groups. In the case group, 100 patients with breast cancer had been diagnosed with bone metastasis by CT, MRI, bone scan, and PET scan; in the control group, there were 216 cases in are control group with breast cancer metastasis to sites other than bone. The questionnaire was created to record all factors (with the exception of demographic information), such as age, histopathological reading for invasive lobular carcinoma, invasive ductal carcinoma, or another type of cancer, ER status, PR status, Her2neu status, lymph node status and stage, and menopausal status. Results: The majority were 45 years of age or younger, and 96.5% of them had invasive ductal carcinoma in terms of hormonal status. Positive ER and PR status is found in 67.7% and 67.1% of the cases, respectively; 40.5% of the cases had amplified Her2neu receptors, 78.5% of the cases had positive lymph nodes, and 41.9% of the cases had (N1) stage. The highest proportion of bone metastasis was associated with negative ER and PR receptors (46.2% and 49%, respectively), while no significant statistical association (P= 0.65). Conclusion: negative ER, PR status, and Her2neu receptors not amplified, lymph node involvement, age 45 and less, infiltrating lobular carcinoma (ILC) histological type, and menopausal state are significant risk factors for bone metastasis in breast cancer.

Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study

BackgroundBone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME).Patients and methodsThe study cohort comprised 1,151 individuals diagnosed with right-sided colon cancer, with a subset of 73 patients presenting with bone metastases originating from the colon. We used univariate and multivariate regression analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and surgical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), to develop the predictive model. Additionally, the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the model.ResultsThe XGBoost algorithm performed the best performance among the four prediction models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953–0.994), an accuracy of 0.925 (0.913–0.936), a sensitivity of 0.921 (0.902–0.940), and a specificity of 0.908 (0.894–0.922). In the validation set, the XGBoost algorithm had an AUC value of 0.922 (0.833–0.995), an accuracy of 0.908 (0.889–0.926), a sensitivity of 0.924 (0.873–0.975), and a specificity of 0.883 (0.810–0.956). Furthermore, the AUC value of 0.83 for the external validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline phosphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophil-to-lymphocyte ratio (NLR) levels as significant risk factors for BM from right-sided colon cancer subsequent to CME.ConclusionThe prediction model for BM from right-sided colon cancer developed using the XGBoost machine learning algorithm in this study is both highly precise and clinically valuable.

Clinical characteristics and prognosis analysis of gastric cancer patients with bone metastasis

Objectives: To investigate the clinical characteristics and prognosis of bone metastasis of gastric cancer, analyze the influencing factors of bone metastasis and the effects of different treatment methods, and provide a basis for early detection and treatment optimization of bone metastasis of gastric cancer. Methods: A total of 142 gastric cancer patients with bone metastasis admitted to the First Hospital of Lanzhou University from January 2011 to December 2021 were enrolled, including 60 cases of simple bone metastasis and 82 cases of bone metastasis combined with extraosseous metastasis. 142 patients with stage Ⅲgastric cancer without distant metastasis and 142 gastric cancer patients with visceral metastasis admitted to this hospital during the same period were also enrolled for comparison. Logistic regression analysis was used to determine the influencing factors of bone metastasis, and the Cox proportional hazards regression model was used to evaluate the influencing factors of overall survival (OS) of patients with bone metastasis. Results: Among the 142 patients with bone metastasis, poorly differentiated adenocarcinoma was the main type (123 cases), and 45 patients had simultaneous bone metastasis. Rib metastasis (100 cases), spine metastasis (88 cases), and pelvis metastasis (84 cases) were more common. A total of 110 patients had multiple bone metastasis, and 82 patients had extraosseous metastasis. Results of the stage Ⅲ gastric cancer group, the visceral metastasis group, the bone metastasis group, and the bone metastasis with extraosseous metastasis group were compared. There were significant differences in age, degree of differentiation, Borrmann type, alkaline phosphatase, lactate dehydrogenase, serum calcium, alanine aminotransferase, aspartate aminotransferase, creatine kinase isoenzyme, lymphocyte, hemoglobin, platelet, CEA, CA19-9, and CA724 (all P<0.05). Multivariate logistic regression analysis showed that Borrmann type was an independent protective factor of bone metastasis of gastric cancer (type 3: OR=0.07, 95%CI: 0.01-0.64, P=0.018). Alkaline phosphatase (OR=2.54, 95% CI: 1.07-6.01, P=0.034), serum calcium (OR=2.71, 95% CI: 1.15-6.41, P=0.023), creatine kinase isoenzyme (OR=16.33, 95% CI: 1.83-145.58, P=0.012), platelet (OR=10.08, 95% CI:1.89-53.85, P=0.007), and CA19-9 (OR=2.40, 95% CI: 1.14-5.05, P=0.021) were independent risk factors of bone metastasis of gastric cancer. The median OS of the stage Ⅲ gastric cancer group, the visceral metastasis group, the bone metastasis group, and the bone metastasis with extrabony group were 47, 13, 18, and 6 months, respectively, and the difference was statistically significant (P<0.001). The median OS of patients with bone metastasis only who underwent primary tumor surgery was 33 months, better than 6 months of patients without surgery (P=0.048). Multivariate Cox regression analysis showed that extraosseous metastasis (HR=2.45, 95% CI: 1.56-3.85, P<0.001) and decreased hemoglobin (HR=1.54, 95%CI: 1.02-2.34, P=0.042) were independent risk factors of OS of gastric cancer patients with bone metastasis. Conclusions: The prognosis of gastric cancer patients with bone metastasis alone is significantly better than that of other stage Ⅳ patients. For such patients, surgery on the primary site combined with chemotherapy after full evaluation may prolong the survival time.

Incidence and risk factors for bone metastases at presentation in solid tumors.

Bone metastases are associated with increased morbidity and decreased quality of life in patients with solid tumors. Identifying patients at increased risk of bone metastases at diagnosis could lead to earlier interventions. We sought to retrospectively identify the incidence and predictive factors for bone metastases at initial diagnosis in a large population-based dataset. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients 18 years-old or older diagnosed with solid cancers from 2010 to 2019. Patients with hematologic malignancies and primary tumors of the bone were excluded. We calculated the incidence and predictive factors for bone metastases according to demographic and tumor characteristics. Among 1,132,154 patients identified, 1,075,070 (95.0%) had known bone metastasis status and were eligible for the study. Bone metastases were detected in 55,903 patients (5.2% of those with known bone metastases status). Among patients with bone metastases, the most common primary tumors arose from lung (44.4%), prostate (19.3%), breast (12.3%), kidney (4.0%), and colon (2.2%). Bone metastases at presentation were most common in small cell lung cancer (25.2%), non-small cell lung cancer (18.0%), and esophageal adenocarcinoma (9.4%). In addition to stage classification, predictors for bone metastases included Gleason score (OR 95.7 (95% CI 73.1 - 125.4) for Grade Group 5 vs 1 and OR 42.6 (95% CI 32.3 - 55.9) for Group 4 vs 1) and PSA (OR 14.2 (95% CI 12.6 - 16.0) for PSA > 97 vs 0 - 9.9) for prostate cancer, HER2 and hormonal receptor (HR) status (OR 2.2 (95% CI 1.9 - 2.6) for HR+/HER2+ vs HR-/HER2-) for breast cancer, histology (OR 2.5 (95% CI 2.3 - 2.6) for adenocarcinoma vs squamous) for lung cancer, and rectal primary (OR 1.2 (95% 1.1 - 1.4) vs colon primary) and liver metastases (OR 8.6 (95% CI 7.3 - 10.0) vs no liver metastases) for colorectal tumors. Bone metastases at presentation are commonly seen in solid tumors, particularly lung, prostate, breast, and kidney cancers. Clinical and pathologic factors are associated with a significantly increased risk for bone metastases.

Risk factor analysis and predictive model construction for bone metastasis in newly diagnosed malignant tumor patients.

To identify risk factors for bone metastasis in patients with newly diagnosed malignant tumor and to develop a prediction model. Clinical data from 232 patients with newly diagnosed malignant tumors were analyzed to screen for risk factors associated with bone metastasis. A nomogram prediction model was constructed using R software. The model's performance was evaluated using Receiver Operating Characteristic (ROC) analysis, Bootstrap sampling, and Decision Curve Analysis (DCA). The incidence of bone metastasis in the 232 cases with newly diagnosed malignant tumors was 21.98% (51/232). Multivariate logistic regression analysis revealed that tumor staging III-IV, lymph node metastasis, high Eastern Cancer Collaboration Group Physical Status (ECOG-PS) score, high alkaline phosphatase (ALP) expression, and high SII index were risk factors for bone metastasis at initial diagnosis (all P<0.05). The area under the curve (AUC) of the nomogram model was 0.893. Bootstrap sampling validation showed a small error of 0.017 between predicted and actual probabilities. DCA supported the utility of the model in clinical practice. Bone metastasis in newly diagnosed malignant tumors is associated with advanced tumor staging, lymph node metastasis, high ECOG-PS score, elevated ALP expression, and a high SII index. A nomogram model based on these factors can effectively predict the risk of bone metastasis in these patients.

The clinical association between coagulation indexes, platelet-related parameters, and bone metastasis of newly diagnosed prostate cancer

BackgroundAt present, much evidence shows that many cancers have a high risk of thrombosis. Several studies have shown the prognostic value of platelet-related parameters and coagulation indexes in prostate cancer (PCa). However, the association between platelet-related parameters, coagulation indexes and bone metastasis of Pca is unclear.MethodsA total of 234 pathologically diagnosed patients with Pca were consecutively collected and stratified into the bone metastasis group and non-bone metastasis group according to the results of the bone scan. ROC curve analysis was used to explore the auxiliary predictive value of single and combined parameters for bone metastasis in Pca patients. Univariate and multivariate Logistic regression analyses were used to determine the relationship between platelet-related parameters, coagulation indexes, and bone metastasis of Pca.ResultsPlatelet count (PLT), fibrinogen (Fib), prostate-specific antigen (PSA), and D-dimer (DD) levels of the bone metastasis group were significantly higher than the non-bone metastasis group (P = 0.010, P < 0.001, P < 0.001, and P < 0.001, respectively). This study confirmed that PLT, PSA, DD and Fib have auxiliary predictive value for prostate cancer bone metastasis. After the combination of PLT, PSA, DD and Fib, the area under the curve, sensitivity and specificity increased significantly. The univariate logistic analysis demonstrated that PLT (OR: 1.008, P = 0.011), DD (OR: 2.690, P < 0.001), PSA (OR: 1.073, P < 0.001), Gleason score (OR: 7.060, P < 0.001), and Fib (OR: 2.082, P < 0.001) were significantly positively correlated with bone metastasis of Pca. Multivariate analysis showed that PSA (OR: 1.075, P < 0.001), DD (OR: 2.152, P < 0.001), Gleason score (OR: 2.904, P < 0.001), and Fib (OR: 1.706, P < 0.001) were independent risk factors for bone metastasis of Pca after adjusting for Age, BMI and other confounding factors.ConclusionsHigher platelet, D-dimer, prostate-specific antigen, Gleason score, and fibrinogen levels may predict a worse prognosis in patients with Pca. PLT, DD, and Fib, as readily available and relatively inexpensive indicators, help predict bone metastasis of Pca. It is suggested that PLT, DD and Fib may be helpful in the risk stratification of Pca.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study.

The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM. The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system. From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system. The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.

Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning

This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models. Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (Diagnostic model AUC = 0.98; Prognostic model AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis. Based on the XGBoost algorithm, we could effectively predict the diagnosis and survival of bone metastasis in breast cancer and provide targeted references for the treatment of BCBM patients.

Prediction of bone metastasis risk of early breast cancer based on nomogram of clinicopathological characteristics and hematological parameters.

The purpose of this study was to determine the independent risk factors for bone metastasis in breast cancer and to establish a nomogram to predict the risk of bone metastasis in early stages through clinicopathological characteristics and hematological parameters. We selected 1042 patients with breast cancer from the database of Shandong Cancer Hospital for retrospective analysis, and determined independent risk factors for bone metastatic breast cancer (BMBC). A BMBC nomogram based on clinicopathological characteristics and hematological parameters was constructed using logistic regression analysis. The performance of the nomograph was evaluated using the receiver operating characteristic (ROC) and calibration curves. The clinical effect of risk stratification was tested using Kaplan-Meier analysis. BMBC patients were found to be at risk for eight independent risk factors based on multivariate analysis: age at diagnosis, lymphovascular invasion, pathological stage, pathological node stage, molecular subtype, platelet count/lymphocyte count, platelet count * neutrophil count/lymphocyte count ratio, Systemic Immunological Inflammation Index, and radiotherapy. The prediction accuracy of the BMBC nomogram was good. In the training set, the area under the ROC curve (AUC) was 0.909, and in the validation set, it was 0.926, which proved that our model had good calibration. The risk stratification system can analyze the risk of relapse in individuals into high- and low-risk groups. The proposed nomogram may predict the possibility of breast cancer bone metastasis, which will help clinicians optimize metastatic breast cancer treatment strategies and monitoring plans to provide patients with better treatment.

Osseous Metastases in Thyroid Cancer: Unveiling Risk Factors, Disease Outcomes, and Treatment Impact.

Bone is the second most common site of metastasis in patients with thyroid cancer (TC) and dramatically impacts overall survival and quality of life with no definitive cure, yet there is no extensive study of the demographic and clinical risk factors in the recent literature. Data regarding 120,754 TC patients with bone metastasis were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were used to identify the risk factors of bone metastasis occurring in various histologies of TC. Cox regression was performed to analyze the influence of bone metastasis on overall survival. Hazard ratios were computed to analyze the association between bone metastasis and the primary outcomes. Of the 120,754 records collected from the SEER database from 2000 to 2019, 976 (0.8%) presented with bone metastasis, with occurrence being the greatest in patients of age ≥ 55 years (OR = 5.63, 95%CI = 4.72-6.71), males (OR = 2.60, 95%CI = 2.27-2.97), Blacks (OR = 2.38, 95%CI = 1.95-2.9) and Asian or Pacific Islanders (OR = 1.90, 95%CI = 1.58-2.27), and single marital status. TC patients presenting with bone metastasis (HR = 2.78, 95%CI = 2.34-3.3) or concurrent bone and brain metastases (HR = 1.62, 95%CI = 1.03-2.55) had a higher mortality risk. Older age, gender, race, and single marital status were associated with bone metastasis and poorer prognosis in TC patients at initial diagnosis. Understanding such risk factors can potentially assist clinicians in making early diagnoses and personalized treatment plans, as well as researchers in developing more therapeutic protocols.

Diagnosis of SPECT/CT bone imaging combined with two serum examinations in patients with bone metastases from pulmonary cancer.

To study the clinical diagnostic value of SPECT/CT bone imaging combined with two serum examinations in patients with bone metastases from pulmonary cancer. The clinical data of 120 patients consistent with pulmonary cancer admitted to the First Affiliated Hospitalof Hebei North University from March 2019 to December 2019 were selected for retrospective analysis, and they were divided into the bone metastasis group (n = 58) and non-bone metastasis group (n = 62) according to comprehensive evaluation result of X-ray, CT, MRI and clinical follow-up. The CT values of patients were obtained by SPECT/CT bone imaging to compare serum levels of ALP (alkaline phosphatase belongs to phosphoric monoester hydrolases, as a specific phosphatase, mainly in body tissues and body fluid) and BAP (bone alkaline phosphatase is formed by different modification and processing of alkaline phosphatase, and is mainly released by osteoblasts) and CT values of patients in both groups, using receiver operating characteristic (ROC) curve to evaluate the diagnostic efficacy of single detection and combined detection. SPECT/CT bone imaging in patients with bone metastasis from pulmonary cancer showed abnormal radioactive accumulation in spine, pelvis and bilateral ribs. Serum ALP, BAP and CT values in bone metastasis group were overtly higher than the non-bone metastasis group (P < 0.001). Logistic regression analysis showed that serum ALP, BAP and CT value were independent risk factors for bone metastasis from pulmonary cancer. The AUC value and Youden index of combined diagnosis were higher than those of single diagnosis. SPECT/CT bone imaging combined with serum detection of ALP and BAP in patients with pulmonary cancer is helpful for early diagnosis of bone metastasis, which provides more basis for the formulation and selection of clinical treatment options.

The incidence and risk factors predictive of bone metastases at initial diagnosis of malignancy.

e18816 Background: Bone metastases are associated with increased morbidity and decreased quality of life in patients with solid tumors. We report here the incidence and predictive factors for bone metastases at initial diagnosis in a large population-based dataset. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients 18 years-old or older and with solid cancers of all stages diagnosed from 2010 to 2019. We calculated the incidence and predictive factors for bone metastases according to demographic and tumor characteristics. Results: Among the 1,132,154 patients identified, 1,075,069 (94.96%) had known bone metastasis status and were eligible for the study. Bone metastases were detected in 55,903 (4.9%) patients. Among patients with bone metastases, the most common primary tumors were lung (44.4%), prostate (19.3%), breast (12.4%), kidney (4.0%), colorectal (2.24%) and pancreas (2.23%). The percentage of bone metastases in patients with non-small cell lung cancer (NSCLC), prostate adenocarcinoma, infiltrating ductal carcinoma of the breast (IDC), and colorectal adenocarcinoma (CRC) was 18.04% (18,123 out of 100,447), 5.92% (9,071 out of 153,325), 2.9% (4,490 out of 154,626), and 1.17% (1,043 out of 89,510) respectively. In addition to T and N stage, independent risk factors by multivariable analysis for bone metastases included female sex and non-squamous histology in NSCLC, black race, hormonal and HER2 status in breast cancer, Gleason score and PSA in prostate cancer, male sex, black race, rectal location, liver metastases and lung metastases in CRC. In univariate analysis, the strongest predictors for bone metastases were N2/3 (28.3%) and T3/4 (24.9%) in NSCLC, T3/4 (15.8%), N2/3 (10.6%), N1 (5.9%) and HER2 positive (4.9%) in IDC, N1 (38.1%), Gleason score 9/10 (22.8%), PSA 98 or higher (19.5%) and PSA 20-97 (16.8%) in prostate cancer, and rectal or rectosigmoid location (1.43% vs 1.05% in colon cancer, p &lt; 0.001) in CRC. Conclusions: Approximately 5% of patients with solid tumors have bone metastases at presentation. In addition to T and N stage, there are several risk factors for bone metastases according to the primary tumor site.

Prognostic value of alkaline phosphatase and bone-specific alkaline phosphatase in breast cancer: A systematic review and meta-analysis.

Numerous studies have reported the clinical value of alkaline phosphatase (ALP) and its bone-specific isoforms (bone-specific alkaline phosphatase (BAP)) in breast cancer. The purpose of this meta-analysis was to summarize the prognostic value of serum ALP and BAP in breast cancer, especially focused on bone metastasis and survival. PRISMA guidelines were followed to conduct this review. Observational studies were searched in PubMed, Cochcrane Library and EMBASE to January 1, 2022. Data were extracted to explore the prognostic value of ALP and BAP. The quality of the included studies was assessed and the outcome effects were evaluated. Subgroup and sensitivity analyses were performed to explore the potential sources of heterogeneity. Publication bias was assessed. There was a total of 53 studies with 22,436 patients included. For the primary outcome of survival, high levels of both ALP and BAP were associated with short survival time. The hazard ratio of high ALP level on overall survival was 1.72 (95% CI 1.37, 2.16, P < 0.001). For the secondary outcomes, a high ALP level (not BAP) was detected in breast cancer compared with healthy controls, and high levels of both ALP and BAP were risk factors for bone metastasis, while ALP (not BAP) was a risk factor for non-bone metastasis. This study showed that high levels of both serum ALP and BAP were associated with metastasis (BAP was associated with bone metastasis) and survival in breast cancer. The biomarkers could provide useful information for the early diagnostic assessment and monitoring in the follow-up of breast cancer patients.

A population-based investigation: How to identify high-risk T1-2N0 esophageal cancer patients?

Newly diagnosed T1-2N0 esophageal cancer (EC) is generally deemed as early local disease, with distant metastases (DM) easily overlooked. This retrospective study aimed to describe the metastatic patterns, identify risk factors and established a risk prediction model for DM in T1-2N0 EC patients. A total of 4623 T1-2N0 EC patients were identified in the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2018. Multivariable logistic regression was used to identify risk factors for DM. A nomogram was developed for presentation of the final model. Of 4623 T1-2N0 patients, 4062 (87.9%) had M0 disease and 561 (12.1%) had M1 disease. The most common metastatic site was liver (n = 156, 47.3%), followed by lung (n = 89, 27.0%), bone (n = 70, 21.2%) and brain (n = 15, 4.5%). Variables independently associated with DM included age at diagnosis, gender, tumor grade, primary site, tumor size and T stage. A nomogram based on the variables had a good predictive accuracy (area under the curve: 0.750). Independent risk factors for bone metastases (BoM), brain metastases (BrM), liver metastases (LiM) and lung metastases (LuM) were identified, respectively. We identified independent predictive factors for DM, as well as for BoM, BrM, LiM and LuM. Above all, a practical and convenient nomogram with a great accuracy to predict DM probability for T1-2N0 EC patients was established.

Influencing Factors of Bone Metastasis in Human Epidermal Growth Factor Receptor 2 Negative Breast Cancer and Efficacy of Capecitabine Plus Docetaxel

The clinical data characteristics of human epidermal growth factor receptor 2 negative breast cancer patients were analyzed to investigate the influencing factors of bone metastasis. A total of 754 patients with human epidermal growth factor receptor 2 negative breast cancer admitted to the First People's Hospital of Lianyungang from January 2017 to September 2022, including 273 patients in the bone metastasis group and 481 patients in the non-bone metastasis group, were retrospectively and systematically evaluated for their clinic pathological characteristics, treatment modalities and their influencing factors. Chi-square test and Mann-Whitney U test were used to calculate the differences in clinical data between patients with human epidermal growth factor receptor 2 negative bone metastases and those without bone metastases. Binary logistic regression was performed to analyses the risk factors for the development of bone metastases in human epidermal growth factor receptor 2 negative breast cancer. A significant difference was found between the patients in the bone metastasis group and the non-bone metastasis group in terms of T-stage, N-stage, estrogen receptor, progesterone receptor, tumour molecular classification, axillary lymph node metastasis and quality of life scores (functional assessment of cancer therapy-breast) after combined therapy with capecitabine plus docetaxel (Z=-2.706, Z=-2.864, χ2=75.954, χ2=55.618, χ2=114.854, χ2=305.211, χ2=66.945 and p<0.05), while statistical significance was absent in terms of age and location of the primary site (χ2=2.888, χ2=1.903 and p>0.05). Logistic regression analysis revealed that high T-stage, luminal A molecular staging and the occurrence of axillary lymph node metastases were all risk factors for bone metastases in human epidermal growth factor receptor 2 negative breast cancer patients (OR=4.352, 95 % CI=2.147 to 8.823, p<0.001; OR=0.281, 95 % CI=0.179 to 0.441, p<0.001 and OR=12.766, 95 % CI=6.712 to 24.283, p<0.001). The occurrence of bone metastases in human epidermal growth factor receptor 2 negative breast cancers was statistically correlated with T-stage, N-stage, estrogen receptor, progesterone receptor, molecular tumour staging, axillary lymph node metastasis and patient quality of life scores after combined therapy with capecitabine plus docetaxel. High T-stage, luminal A and axillary lymph node metastasis were all risk factors. Combined therapy of capecitabine and docetaxel provides significant efficacy in patients with human epidermal growth factor receptor 2 negative breast cancer bone metastases.

Risk Factors, Patterns, and Distribution of Bone Metastases and Skeletal-Related Events in High-Risk Breast Cancer Patients

More than a quarter of breast cancer patients are at risk to develop recurrent metastases to the bone. This study was designed to identify risk factors and predilections of bone metastasis and skeletal-related events (SRE) in a population of breast cancer survivors initially diagnosed in advanced stages and with high-risks of relapse. Associated risk factors, distribution, and attainable treatment of bone metastasis and SRE were analyzed in a cohort of 1,329 breast cancer patients. The association with dependent variables was subsequently analyzed using multivariable logistic regression. Sociodemographic and adverse clinical characteristics were included as covariates of progression into bone metastasis and SREs. Of 1329 breast cancer patients, 246 patients (18.5%) were diagnosed as metastatic breast cancer in which 232 of them (94.3%) had bone metastases. Spines were the most common sites of bone metastases (25.6%). In multivariable analysis, advanced stage at diagnosis (OR=1.840, 95%CI:1.198-2.826, P=0.005), luminal subtype (OR=1.788, 95%CI:1.206-2.652, P=0.045), lobular histology (OR=1.795, 95%CI:1.012-3/184, P=0.046), positive axillary lymph node (OR=1.771, 95%CI:1.087-2.886, P=0.022), multiple metabolic comorbidities (OR=2.193, 95%CI:1.371-3.508, P=0.001), early menopause (OR=2.136, 95%CI:1.116-4.464, P=0.046) were significantly associated with risk of recurrent bone metastases. SREs occurred in 89 (68.5%) patients. Several risk factors for SREs were early menopausal age (OR=2.342, P=0.024), advanced stages (OR=1.404, P=0.039), lobular histology (OR=2.279, P=0.007), and having multiple metabolic comorbidities (OR=1.728, P=0.039). Bone metastases and SREs are relatively high in breast cancer patients diagnosed in advanced stages. Luminal subtypes, having multiple metabolic comorbidities, and lobular histology are associated with higher risks of recurrent bone metastases. Living in rural areas and advanced stage at diagnosis as a risk factors for bone metastases might represent a social gradient of care delivery.

KIF11: A potential prognostic biomarker for predicting bone metastasis-free survival of prostate cancer.

Most prostate cancer (PCa) cases remain indolent with a relatively good prognosis. However, bone metastasis of PCa can quickly worsen prognoses and lead to mortality. Metastasis-free survival (MFS), a strong surrogate for overall survival, is widely used in PCa prognosis research. The present study identified molecules that affect bone MFS in PCa, with clinical validation. Three datasets (GSE32269, GSE74367 and GSE77930) were downloaded from the Gene Expression Omnibus database. Hub genes most relevant to clinical traits (bone metastasis-associated morbidity) were identified by weighted gene co-expression network analysis (WGCNA) and subjected to logistic regression analysis. Patient samples were obtained between January 2014 and December 2016, with a clinically annotated follow-up in December 2021. Clinical data and follow-up information for 60 patients with PCa were used in MFS analysis. Tumor samples were retrieved, and immunohistochemistry was performed to detect vascular endothelial growth factor (VEGF). The prognostic potential of the two molecules was assessed using Cox proportional hazards regression analysis. A total of 16 gene modules were obtained via WGCNA, and the tan module, containing 147 genes, was most closely linked to bone metastasis. In total, 877 differentially expressed genes (DEGs) were detected. The DEG-tan module intersection yielded seven hub genes [BUB1, kinesin family member (KIF)2C, RACGAP1, CENPE, KIF11, TTK and KIF20A]. Using univariate and multivariate logistic regression analyses for independent risk factors of bone metastasis, KIF11 and VEGF were found to be significantly associated with a higher T stage, prostate-specific antigen level and Gleason score. In addition, KIF11 and VEGF expression levels were positively correlated (P<0.001). Using univariate Cox analysis, KIF11 and VEGF were found to exhibit a significant association with poor MFS (P<0.05). However, only KIF11 was significantly associated with MFS upon multivariate analysis (P=0.007; hazard ratio, 2.776; 95% confidence interval, 1.315-5.859). Markers of bone metastasis in PCa were identified. Overall, KIF11 is an independent indicator that can predict bone metastasis for patients with PCa, which could be used to guide clinical practice.

Influence of alcohol sensitivity on bone metastases and skeletal-related events in primary operable breast cancer: A retrospective cohort study.

BackgroundBone metastases in breast cancer patients are a common concern for medical doctors and dentists. Bone-modifying agents, which are necessary to prevent skeletal-related events (SREs), are associated with osteonecrosis of the jaw as an adverse side effect. Hypersensitivity to alcohol is an unfavorable response caused by deficiency of aldehyde dehydrogenase-2 (ALDH2) activity. Inactive ALDH2 is associated with osteoporosis, but its influence on bone metastases is unclear. The aim of our study was to evaluate the effects of alcohol sensitivity on bone metastases and SREs in primary operable breast cancer patients.MethodsWe retrospectively analyzed patients who were administered docetaxel, an anti-tumor agent, for histologically diagnosed breast cancer between April 2004 and September 2015. Alcohol sensitivity was assessed based on medical records of hypersensitivity to alcohol. The primary endpoint was time to bone metastases and the secondary endpoint was time to first SRE from the initial docetaxel administration. Data were stratified by alcohol sensitivity and tumor stages, and differences were estimated by the Kaplan-Meier method. Prognostic risk factors were analyzed by the multivariate Cox proportional hazards model.ResultsThe median follow-up period of patients with high sensitivity to alcohol (n = 45) was 54 months and that for those with low sensitivity (n = 287) was 64 months. Stratification by alcohol sensitivity revealed that tumor stage exhibited significant correlations with the cumulative incidence of bone metastases in low-sensitivity patients; however, no differences were found in high-sensitivity patients. In multivariate analysis, alcohol sensitivity was a significant prognostic risk factor for bone metastases (HR 2.721, 95% CI 1.268–5.841, P = 0.010).ConclusionAlcohol sensitivity may be a prognostic risk factor for bone metastases. More detailed genetic investigations and metabolic analyses are needed.