Establishing a prediction model for prostate cancer bone metastasis

Association between 99mTc-PSMA SPECT/CT imaging and prostate-specific antigen (PSA) and alkaline phosphatase (ALP) levels post-endocrine therapy in patients with prostate cancer and bone metastases

AIM

To investigate the association between positive lesions detected by 99mTc-PSMA SPECT/CT and blood levels of prostate-specific antigen (PSA) and alkaline phosphatase (ALP) in patients with prostate cancer (PCa) and bone metastasis undergoing endocrine therapy.

METHODS

A retrospective analysis was performed on 43 patients diagnosed with PCa bone metastasis who underwent endocrine therapy. PSA, ALP, whole body bone imaging and 99mTc-PSMA SPECT/CT imaging were collected from all patients (Among them, 17 cases were re-examined 99mTc-PSMA SPECT/CT imaging). According to the results of the first 99mTc-PSMA SPECT/CT imaging for detecting bone metastasis, all cases were divided into two groups: positive group and negative group. The relationship between 99mTc-PSMA imaging and PSA and ALP was analyzed by ROC curve. Fisher exact probability method was used to examine the changes in imaging radioactivity uptake, PSA, and ALP levels in 17 patients after treatment, and P < 0.05 was statistically significant.

RESULTS

All 43 patients had different degrees of radioactive concentrations on whole-body bone imaging. The first 99mTc-PSMA SPECT/CT imaging showed positive bone metastases in 31 cases and negative bone metastases in 12 cases. ROC curve analysis of PSA and ALP, AUC were 0.778 and 0.770, respectively. When PSA > 1.13 ng/mL, 99mTc-PSMA SPECT/CT imaging diagnostic sensitivity was 93.55%, and specificity was 66.67%. When ALP was >86U/L, the diagnostic sensitivity of 99mTc-PSMA SPECT/CT imaging was 64.52%, and the specificity was 83.33%. In 17 cases, the PSA level decreased in 7 and increased in 10. There were 10 cases of increased ALP and 7 cases of decreased ALP levels. In the second 99mTc-PSMA imaging lesion, there were 9 cases with decreased or no uptake, and 8 cases with increased uptake or number of lesions. The changes in 99mTc-PSMA uptake by Fisher's exact probability method were statistically significant (P < 0.05, P = 0.006, and P = 0.006, respectively), and ALP level was not statistically significant (P = 0.563).

CONCLUSION

99mTc-PSMA SPECT/CT imaging can detect PCa bone metastases, which are related to PSA levels. When PSA > 1.13 ng/mL, the sensitivity of diagnosis and detection of positive bone metastases is higher, and when ALP is >86U/L, 99mTc-PSMA imaging has higher specificity.

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study

OBJECTIVE

To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.

METHODS

Three hundred sixty-two inpatients diagnosed with PCa at the First Affiliated Hospital were randomly assigned to training and testing sets in a 3:7 ratio, while 136 PCa patients from People's Hospital formed the external validation set. Imaging and clinicopathologic information were collected. Optimal features distinguishing advanced prostate cancer (APC) and metastatic PCa (mPCa) were identified through logistic regression (LR). ML algorithms were employed to build and compare ML models. The best-performing algorithm established models for PCa progression stage. Models performance was evaluated using metrics, ROC curves, calibration, and decision curve analysis (DCA) in training, testing, and external validation sets.

RESULTS

Following LR analyses, PSA (P = .001), maximum tumor diameter (P = .026), Gleason score (P <.001), and RNF41 (P <.001) were optimal features for predicting APC, while ALP (P <.001), PSA (P <.001), and GS score (P = .024) were for mPCa. Among ML models, the LR models exhibited superior performance. Consequently, the LR algorithm was used for the APC-risk-nomogram and mPCa-risk-nomogram construction, with AUC values of 0.848, 0.814, 0.810, and 0.940, 0.913, 0.910, in the training, testing, and external validation sets, respectively. Calibration and DCA curves affirmed nomograms' consistency and net benefits for clinical decision-making.

CONCLUSION

In summary, ML-based APC-risk-nomogram and mPCa-risk-nomogram exhibit outstanding predictive performance for PCa progression stages. These nomograms can assist clinicians in finely categorizing newly diagnosed PCa patients, facilitating personalized treatment plans and prognosis assessment.

Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance

Objective

To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance.

Methods

Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

Results

The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5-28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901-0.991] and 0.927[95% CI: 0.864-0.990] in the training and validation cohort, respectively.

Conclusion

After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.

A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients

Objective

To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.

Methods

A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor's region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).

Results

The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction's internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.

Conclusion

The ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.

Measurements of peri-prostatic adipose tissue by MRI predict bone metastasis in patients with newly diagnosed prostate cancer

Objectives

To investigate the role of MRI measurements of peri-prostatic adipose tissue (PPAT) in predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa).

Methods

We performed a retrospective study on 156 patients newly diagnosed with PCa by prostate biopsy between October 2010 and November 2022. Clinicopathologic characteristics were collected. Measurements including PPAT volume and prostate volume were calculated by MRI, and the normalized PPAT (PPAT volume/prostate volume) was computed. Independent predictors of BM were determined by univariate and multivariate logistic regression analysis, and a new nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves were used to estimate predictive performance.

Results

PPAT and normalized PPAT were associated with BM (P<0.001). Normalized PPAT positively correlated with clinical T stage(cT), clinical N stage(cN), and Grading Groups(P<0.05). The results of ROC curves indicated that PPAT and normalized PPAT had promising predictive value for BM with the AUC of 0.684 and 0.775 respectively. Univariate and multivariate analysis revealed that high normalized PPAT, cN, and alkaline phosphatase(ALP) were independently predictors of BM. The nomogram was developed and the concordance index(C-index) was 0.856.

Conclusions

Normalized PPAT is an independent predictor for BM among with cN, and ALP. Normalized PPAT may help predict BM in patients with newly diagnosed prostate cancer, thus providing adjunctive information for BM risk stratification and bone scan selection.

Development and external validation of a novel prognostic nomogram for overall survival in prostate cancer patients with bone metastatic: a retrospective study of the SEER-based and a single Chinese center

BACKGROUND

Prostate cancer (PCa) patients with bone metastases (BM) often face a poor prognosis, a leading contributor to mortality within this group. This study aims to develop a novel prognostic nomogram to predict overall survival for them.

METHODS

We retrospectively analyzed PCa patients with BM from Surveillance, Epidemiology, and End Results (SEER) database and our hospital. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses for the creation of a nomogram. Calibration curves and receiver operating characteristic (ROC) curves, along with the concordance index (C-index) and decision curve analysis (DCA), were employed to evaluate the performance of the constructed nomogram.

RESULTS

A total of 12,344 PCa patients with BM, derived from 2010 to 2019 SEER database, were randomly allocated into a training cohort (n = 8640) and an internal validation cohort (n = 3704). Additionally, an external validation cohort (n = 126) from our hospital. The novel nomogram integrates multiple factors: age, race, histopathology, organ metastasis, chemotherapy, Gleason score, and prostate-specific antigen (PSA). C-index for the training, internal validation, and external validation cohorts were 0.770 (0.766-0.774), 0.756 (0.749-0.763), and 0.751 (0.745-0.757) respectively. Similarly, the area under the curve (AUC) for each cohort exhibited comparable results (training cohort-3-year: 0.682, 6-year: 0.775, 9-year: 0.824; internal validation cohort-3-year: 0.681, 6-year: 0.750, 9-year: 0.806; external validation cohort-2-year: 0.667, 3-year: 0.744, 4-year: 0.800), indicating that the nomogram possesses robust discriminative ability. Calibration curve and DCA curve further proved the reliability and accuracy of the prognostic nomogram.

CONCLUSION

This study determined the independent risk factors for prostate cancer (PCa) patients with bone metastasis (BM) and subsequently developed a robust prognostic nomogram to predict overall survival (OS). This tool can serve to guide precise clinical treatment strategies for these patients.

Distinguishable Colorimetric Biosensor for Diagnosis of Prostate Cancer Bone Metastases

Castration-resistant prostate cancer (PCa) causes severe bone metastasis (BM), which significantly increases mortality in men with PCa. Imaging tests and radiometric scanning require long analysis times, expensive equipment, specialized personnel, and a slow turnaround. New visualization technologies are expected to solve the above problems. Nonetheless, existing visualization techniques barely meet the urgency for precise diagnosis because the human eyes cannot recognize and capture even slight variations in visual information. By using dye differentiated superposition enhancement colorimetric biosensors, an effective method to diagnose prostate cancer bone metastases (PCa-BM) with excellent accuracy for naked-eye quantitative detection of alkaline phosphatase (ALP) is developed. The biomarker ALP specific hydrolytic product ascorbic acid can be detected by rhodamine derivatives (Rd) as gold nanobipyramids (Au NBPs) are deposited and grown. Color-recombining enhancement effects between Rd and Au NBPs significantly improved abundance. The 150 U L-1 threshold between normal and abnormal can be identified by color. And with color enhancement effect and double signal response, the ALP index is visually measured to diagnose PCa-BM and provide handy treatment recommendations. Additionally, the proposed colorimetric sensing strategy can be used to diagnose other diseases.

Prognostic Factors of Bone Metastases From Colorectal Cancer in the Era of Targeted Therapy

BACKGROUND

Various prognostic factors have been reported for bone metastases from different primary tumor sites. However, bone metastases from colorectal cancer are very rare, and the prognostic factors have not been investigated in detail.

OBJECTIVE

This study aimed to identify prognostic factors of bone metastases from colorectal cancer.

DESIGN

This is a retrospective cohort study using data from a prospectively collected database.

SETTINGS

This study was conducted at a single tertiary care cancer center in Japan.

PATIENTS

Patients who developed bone metastases from colorectal cancer during the study period among all patients who received initial treatment for colorectal cancer at our hospital between 2005 and 2016 (n = 4538) were included.

MAIN OUTCOME MEASURES

Overall survival after diagnosis of bone metastases from colorectal cancer was the main outcome measure.

RESULTS

Ninety-four patients developed bone metastases during the study period. The 5-year overall survival rate was 11.0%. Multivariable analysis identified the following independent risk factors associated with poor prognosis: ≥70 years of age at diagnosis of bone metastases (HR, 2.48; 95% CI, 1.24-4.95; p < 0.01), curative surgery not performed as initial treatment (HR, 2.54; 95% CI, 1.24-5.19; p = 0.01), multiple bone metastases (HR, 2.44; 95% CI, 1.30-4.57; p < 0.01), albumin level <3.7 g/dL (HR, 3.80; 95% CI, 1.95-7.39; p < 0.01), CEA ≥30 ng/mL (HR, 1.94; 95% CI, 1.09-3.46; p = 0.02), and less than 3 chemotherapy options remaining at diagnosis of bone metastases (HR, 2.83; 95% CI, 1.51-5.30; p < 0.01). The median survival times for patients with 0-2, 3, and 4-6 risk factors were 25.0, 8.8, and 4.3 months, respectively.

LIMITATIONS

The main limitation is the single-center, retrospective design of this study.

CONCLUSIONS

Our results may facilitate multidisciplinary decision-making in patients with bone metastases from colorectal cancer. See Video Abstract at http://links.lww.com/DCR/B930 .

FACTORES PRONSTICOS DE LAS METSTASIS SEAS DEL CNCER COLORRECTAL EN LA ERA DE LA TERAPIA DIRIGIDA

ANTECEDENTES:Se han reportado varios factores pronósticos para las metástasis óseas de diferentes sitios de tumores primarios. Sin embargo, las metástasis óseas del cáncer colorrectal son muy raras y los factores pronósticos no se han investigado en detalle.OBJETIVO:Identificar los factores pronósticos de las metástasis óseas del cáncer colorrectal.DISEÑO:Estudio de cohorte retrospectivo utilizando datos de una base de datos recolectada prospectivamente.ENTORNO CLINICO:Un solo centro oncológico de atención terciaria en Japón.PACIENTES:Se seleccionaron pacientes que desarrollaron metástasis óseas de cáncer colorrectal durante el período de estudio entre todos los pacientes que recibieron tratamiento inicial para el cáncer colorrectal en nuestro hospital entre 2005 y 2016 (n = 4538).MEDIDA DE RESULTADO PRINCIPAL:Supervivencia general después del diagnóstico de metástasis óseas por cáncer colorrectal.RESULTADOS:Noventa y cuatro pacientes desarrollaron metástasis óseas, lo que representa el 2,0% de todos los pacientes con cáncer colorrectal que comenzaron el tratamiento durante el período de estudio. La tasa de supervivencia global a 5 años fue del 11,0 %. El análisis multivariable identificó los siguientes factores de riesgo independientes asociados con mal pronóstico: edad ≥70 años al momento del diagnóstico de metástasis óseas (hazard ratio 2,48, CI del 95 % 1,24-4,95, p < 0,01), cirugía curativa no realizada como tratamiento inicial (hazard ratio 2,54, CI 95 % 1,24-5,19, p = 0,01), metástasis óseas múltiples (hazard ratio 2,44, CI del 95 % 1,30-4,57, p < 0,01), nivel de albúmina <3,7 g/dL (hazard ratio 3,80, CI del 95 % 1,95 -7,39, p < 0,01), antígeno carcinoembrionario ≥30 ng/mL (hazard ratio 1,94, CI del 95 % 1,09-3,46, p = 0,02) y menos de 3 opciones de quimioterapia restantes al momento del diagnóstico de metástasis óseas (hazard ratio 2,83, 95 % CI 1,51-5,30, p < 0,01). La mediana de los tiempos de supervivencia para los pacientes con 0-2, 3 y 4-6 factores de riesgo fue de 25,0, 8,8 y 4,3 meses, respectivamente.LIMITACIONES:Diseño retrospectivo de un solo centro.CONCLUSIÓN:Nuestros resultados pueden facilitar la toma de decisiones multidisciplinares en pacientes con metástasis óseas de cáncer colorrectal. Consulte Video Resumen en http://links.lww.com/DCR/B930 . (Traducción- Dr. Francisco M. Abarca-Rendon ).

Three-dimensional amide proton transfer-weighted and intravoxel incoherent motion imaging for predicting bone metastasis in patients with prostate cancer: A pilot study

PURPOSE

To explore the value of 3-dimensional amide proton transfer-weighted (APTw) and intravoxel incoherent motion (IVIM) imaging in predicting bone metastasis (BM) of prostate cancer (PCa) in addition to routine diffusion-weighted imaging (DWI).

METHODS

The clinical and imaging data of 39 PCa patients who were pathologically confirmed in our hospital from March 2019 to February 2022 were retrospectively analyzed, and they were divided into BM-negative (27 patients) and BM-positive (12 patients) groups. MR examination included APTw, DWI and IVIM imaging. The IVIM data was fitted by single-exponential IVIM model (IVIM_mono) and double-exponential IVIM model (IVIM_bi), respectively. The APTw, ADC, IVIM_mono (D_mono, D*_mono, and f_mono), and IVIM_bi (D_bi, D*_bi, and f_bi) parameters were independently measured by two radiologists. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group. Mann-Whitney U test or Student's t-test was used to compare above values between the BM-negative and BM-positive groups. The diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis of each parameter and their combination. The Delong test was used for ROC curve comparison.The relationship between APTw and IVIM was explored through Spearman's rank correlation analysis.

RESULTS

The APTw and D*_mono values were higher, and the ADC, f_mono, and f_bi values were lower in the BM-positive group than in the BM-negative group (all P < 0.05). Among the individual parameters, the AUC of f_mono was the highest (AUC = 0.865), and AUC (f_mono) was significantly higher than AUC (f_bi), AUC (D*_mono), and AUC (ADC) (all P < 0.05). The AUC (IVIM_mono) was higher than the AUC (IVIM_bi) (P = 0.0068). The combination of APTw and IVIM_mono further improved diagnostic capability, and the AUC of APTw+IVIM_mono was significantly higher than those of APTw and DWI (all P < 0.05). No correlation was found between IVIM-derived parameters and APTw value.

CONCLUSION

Both 3D APTw and IVIM imaging could predict BM of PCa. IVIM showed better performance than APTw and DWI, and the single-exponential IVIM model was superior to the double-exponential IVIM model. The combination of APTw and IVIM could further improve diagnostic performance.

Construction and validation of a prognostic model based on 11 lymph node metastasis-related genes for overall survival in endometrial cancer

BACKGROUND

Endometrial cancer (EC) is one of the most common malignant tumors in female reproductive system. The incidence of lymph node metastasis (LNM) is only about 10% in clinically suspected early-stage EC patients. Discovering prognostic models and effective biomarkers for early diagnosis is important to reduce the mortality rate.

METHODS

A least absolute shrinkage and selection operator (LASSO) regression was conducted to identify the characteristic dimension decrease and distinguish porgnostic LNM related genes signature. Subsequently, a novel prognosis-related nomogram was constructed to predict overall survival (OS). Survival analysis was carried out to explore the individual prognostic significance of the risk model and key gene was validated in vitro.

RESULTS

In total, 89 lymph node related genes (LRGs) were identified. Based on the LASSO Cox regression, 11 genes were selected for the development of a risk evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better OS (p = 3.583e-08). The area under the ROC curve (AUC) of this model was 0.718 at 5 years of OS. Then, we developed an OS-associated nomogram that included the risk score and clinicopathological features. The concordance index of the nomogram was 0.769. The survival verification performed in three subgroups from the nomogram demonstrated the validity of the model. The AUC of the nomogram was 0.787 at 5 years OS. Proliferation and metastasis of HMGB3 were explored in EC cell line. External validation with 30 patients in our hospital showed that patients with low-risk scores had a longer OS (p-value = 0.03). Finally, we revealed that the most frequently mutated genes in the low-risk and high-risk groups are PTEN and TP53, respectively.

CONCLUSIONS

Our results suggest that LNM plays an important role in the prognosis, and HMGB3 was potential as a biomarker for EC patients.

Survival Analysis and a Novel Nomogram Model for Progression-Free Survival in Patients with Prostate Cancer

Background

This study sought to perform a survival analysis and construct a prognostic nomogram model based on the Gleason grade, total prostate-specific antigen (tPSA), alkaline phosphate (ALP), and TNM stage in patients with prostate cancer (PCa).

Methods

The progression-free survival (PFS) of 255 PCa patients was analyzed in this study. The prognostic value of tPSA and ALP was evaluated using the Kaplan-Meier survival curves and Cox regression analysis, and a nomogram model based on the Gleason grade, tPSA, ALP, and TNM stage was further established for PFS prediction in PCa patients.

Results

PCa patients with different Gleason grades, tPSA and ALP levels, and TNM stages presented distinct PFS. The Gleason grade, tPSA, ALP, and TNM stage were four independent prognostic indicators. The C-index of the established nomogram was 0.705 for PFS in the test cohort and 0.687 for the validation cohort, and the calibration curves indicated a good consistency between predicted and actual PFS in PCa patients.

Conclusion

The data of this study demonstrated that the Gleason grade, tPSA, ALP, and TNM stage of PCa patients are independently correlated with PFS, and a nomogram model based on these indicators may be valuable for the PFS prediction in PCa patient.

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Objective

This study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients.

Methods

Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model.

Results

A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients.

Conclusion

This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.

A new nomogram for the prediction of bone metastasis in patients with prostate cancer

Objective

This study aimed to establish a new prognostic nomogram for bone metastasis in patients with prostate cancer (PCa).

Methods

This study retrospectively analyzed clinical data from 332 patients diagnosed with PCa from 2014 to 2019, and patients were randomly divided into a training set (n = 184) and a validation set (n = 148). Multivariate logistic regression analysis was used to establish a prediction model based on the training set, and a nomogram was constructed for visual presentation. The calibration, discrimination and clinical usefulness of the model were evaluated using the validation set.

Results

Total prostate-specific antigen, clinical tumor stage, Gleason score, prostate volume, red cell distribution width and serum alkaline phosphatase were selected as predictors to develop a prediction model of bone metastasis. After evaluation, the model developed in our study exhibited good discrimination (area under the curve: 0.958; 95% confidence interval: 0.93–0.98), calibration (U = 0.01) and clinical usefulness.

Conclusions

The new proposed model showed high accuracy for bone metastasis prediction in patients with PCa and good clinical usefulness.

A Novel Nomogram for Survival Prediction of Patients with Spinal Metastasis From Prostate Cancer

STUDY DESIGN

A retrospective study of 84 patients with spinal metastasis from prostate cancer (SMPCa) was performed.

OBJECTIVE

The aim of this study was to predict the survival of patients with SMPCa by establishing an effective prognostic nomogram model, associating with the affecting factors and compare its efficacy with the existing scoring models.

SUMMARY OF BACKGROUND DATA

Prostate cancer (PCa) is the second most frequently malignant cancer causing death in men, and the spine is the most common site of bone metastatic burden. The aim of this study was to establish a prognostic nomogram for survival prediction of patients with SMPCa, explore associated factors, and compare the effectiveness of the new nomogram prediction model with the existing scoring systems.

METHODS

Included in this study were 84 SMPCa patients who were admitted in our spinal tumor center between 2006 and 2018. Their clinical data were retrospectively analyzed by univariate and multivariate analyses to identify independent variables that enabled to predict prognosis. A nomogram, named Changzheng Nomogram for Survival Prediction (CNSP), was established on the basis of preoperative independent variables, and then subjected to bootstrap re-samples for internal validation. The predictive accuracy and discriminative ability were measured by concordance index (C-index). Receiver-operating characteristic (ROC) analysis with the corresponding area under the ROC was used to estimate the prediction efficacy of CNSP and compare it with the four existing prognostic models Tomita, Tokuhashi, Bauer, and Crnalic.

RESULTS

A total of seven independent variables including Gleason score (P = 0.001), hormone refractory (P < 0.001), visceral metastasis (P < 0.001), lymphocyte to monocyte ratio (P = 0.009), prostate-specific antigen (P = 0.018), fPSA/tPSA (P = 0.029), Karnofsky Performance Status (P = 0.039) were identified after accurate analysis, and then entered the nomogram with the C-index of 0.87 (95% confidence interval, 0.84-0.90). The calibration curves for probability of 12-, 24-, and 36-month overall survival (OS) showed good consistency between the predictive risk and the actual risk. Compared with the previous prognostic models, the CNSP model was significantly more effective than the four existing prognostic models in predicting OS of the SMPCa patients (p < 0.05).

CONCLUSION

The overall performance of the CNSP model was satisfactory and could be used to estimate the survival outcome of individual patients more precisely and thus help clinicians design more specific and individualized therapeutic regimens.Level of Evidence: 4.

Increased Serum Calcium Level Promotes the Risk of Lymph Node Metastasis in Endometrial Cancer

Purpose

The early predictive values of diagnostic markers for lymph node metastasis (LNM) in endometrial cancer (EC) are still unclear at present. The purpose of this study is to explore the relationship between serum calcium and LNM in EC.

Methods

We identified all patients with EC who underwent surgery between January 2012 and December 2016. Patient characteristics and various preoperative clinicopathologic data were obtained from medical records and were reviewed retrospectively. These patients were divided into two groups according to the pathology of their lymph node. Logistic regression models analyzed the relationship between the ionized calcium and LNM of EC patients, while adjusting for the potential confounders.

Results

A total of 448 patients were assessed. Univariate analysis showed that ionized calcium, CA125 level, tumor grade, peritoneal cytology, FIGO stage, histological type, LVSI, and myometrial invasion were positively correlated with LNM (all P<0.05). The risk of LNM increased with the promotion of serum ionized calcium (P for trend <0.01). Ionized calcium level was significant before and after the adjustment of cofounders (unadjusted: OR=11.9, 95% CI: 4.8-29.6, P< 0.01; model I: OR=11.3, 95% CI: 4.5-28.8, P< 0.01; model II: OR=5.2, 95% CI: 1.6-17.2, P< 0.05). Additionally, the risk of ionized calcium was especially evident in patients whose age was older than 60, BMI<28 kg/m², grade 3, negative peritoneal cytology and endometrioid endometrial adenocarcinoma.

Conclusion

Ionized calcium level was highly associated with LNM in EC and acted as a potential biomarker in predicting the risk of LNM in EC.

Assessment of Serum Tumor Markers for Predicting Ocular Metastasis in Lung Adenocarcinoma: A Retrospective Study

The purpose of this study was to detect clinical variations between lung adenocarcinoma patients with and without ocular metastasis (OM) to identify risk factors for OM and assess the diagnostic values. We included 1153 patients with lung adenocarcinoma in this study. Independent t-tests and chi-square tests were used to compare patients' clinical characteristics. Statistically significant parameters were analyzed by binary logistic regression to detect risk factors of OM. The results showed that the OM group had increased alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), cytokeratin fragment 19 (CYFRA 21-1), carbohydrate antigen- (CA-) 125, CA-153, and total prostate-specific antigen (TPSA) compared with the NOM group. CYFRA21-1 is the most useful biomarker for detecting OM in this population.

The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study

Background: To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off points. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer and the nomogram prediction model may help early diagnose the high risk prostate cancer.

An elevated preoperative serum calcium level is a significant predictor for positive peritoneal cytology in endometrial carcinoma

Objective

To evaluate preoperative serum calcium concentration and investigate the association between calcium level and positive peritoneal cytology in endometrial carcinoma (EC).

Methods

A total of 510 patients who were diagnosed with EC and had surgery were initially enrolled in this study at Peking University People’s Hospital between January 2012 and December 2016. Clinical characteristics and preoperative serum calcium, albumin, carbohydrate antigen (CA)125, CA19-9, carcinoembryonic antigen (CEA) were extracted from patient records and evaluated according to postoperative peritoneal cytology. Predictive factors were assessed with Cox univariate and multivariate analyses. Factors selected from multivariate analysis results were used to build a predictive model.

Results

A total of 510 patients are identified in our database and 444 patients who fulfilled inclusion and exclusion criteria are included in this study. Univariate analysis revealed that ionized calcium concentration was closely related to positive peritoneal cytology, tumor grade and lymph-vascular space invasion (LVSI). Moreover, peritoneal cytology was significantly associated with hypertension, tubal ligation, serum CA125, CA19-9, CEA and ionized calcium level. Multivariate analysis revealed that albumin-adjusted calcium level, CA125 and tubal ligation were independent predictive factors of positive peritoneal cytology (P<0.05). A combination of ionized calcium level with the other two indexes yielded significantly great area under the curve (AUC=0.824).

Conclusions

This study enhanced the value of preoperative ionized calcium level. We also identified several potential biomarkers to predict positive peritoneal cytology in EC patients before surgery.