Development of a new nomogram to predict insignificant prostate cancer in patients undergoing radical prostatectomy

Prostate cancer epidemiology and prognostic factors in the United States

Objective

Using the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.

Methods

Patients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.

Results

There was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.

Conclusions

Although there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Use of increasing the number of biopsy cores in proportion to prostate size on prostate cancer diagnosis

Objectives:

This study aimed to determine the value of changing the number of biopsy cores in proportion to the size of the prostate in patients who had initial transperineal prostate biopsies.

Materials and methods:

This study included 470 patients who underwent an initial transperineal prostate biopsy. The number of biopsy cores was changed according to the value of the product of the vertical and horizontal diameters of the largest horizontal section of the prostate on transrectal ultrasonography (TRUS). Biopsies were classified into five groups: 12 cores, 14 cores, 18 cores, 20 cores, and 24 cores. Predictive factors for positive biopsy were studied with logistic regression analyses.

Results:

Variables that were significantly associated with positive biopsy were age, prostate-specific antigen density (PSAD), prostate volume (Pvol), and number of biopsy cores in univariate analysis. Age, PSAD, and Pvol were independent predictors in multivariate analysis. There was no significant difference in the number of biopsy cores, and it was not an independent predictor.

Conclusions:

Changing the number of biopsy cores according to the area of the largest horizontal section of the prostate on TRUS had no significant impact in detecting prostate cancer. However, further research is required to confirm this conclusion.

Level of evidence:

Level 2b.

The potential value of microRNA-145 for predicting prognosis in patients with ovarian cancer: A protocol for systematic review and meta-analysis

BACKGROUND

As an anticancer gene, microRNA-145 (miRNA-145) inhibits the growth, migration, and invasion of cancer cells, and inhibits tumorigenesis by targeting various genes that are abnormally expressed in tumors. However, whether miRNA-145 can be applied as a biomarker for potential prognosis of ovarian cancer still remains controversial. Therefore, this study further explored the prognostic value and mechanism of miRNA-145 in ovarian cancer through meta-analysis and bioinformatics analysis.

METHODS

Eligible studies were identified by searching the China National Knowledge Infrastructure, Chinese Biomedical literature Database, Chinese Scientific and Journal Database, Wan Fang database, PubMed, EMBASE, and Web of Science up to July 2021. Pooled hazard ratios with 95% confidence intervals for patient survival were calculated to investigate the effects of miRNA-145 on the prognosis of ovarian cancer. Survival curves of differential expression of miRNA-145 were analyzed by Oncomir. The target genes of miRNA-145 were predicted by miRTARbase and Diana-Tarbase V7.0 database. Enrichr database was applied to analyze the target genes by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Protein-protein interaction network of target genes was constructed from STRING database. Cytoscape software was used to screen the hub genes to meet the requirements. The Gene Expression Profiling Interactive Analysis database was applied to analyze the survival outcomes of hub genes.

RESULTS

The results of this meta-analysis would be submitted to peer-reviewed journals for publication.

CONCLUSION

This study provides high-quality evidence to support the relationship between miRNA-145 expression and ovarian cancer prognosis. Through bioinformatics analysis, we further explored the mechanism of miRNA-145 in ovarian cancer and related pathways.

Combined multiple clinical characteristics for prediction of discordance in grade and stage in prostate cancer patients undergoing systematic biopsy and radical prostatectomy

BACKGROUND

The present study aimed to develop three nomograms by incorporating multiple clinical characteristics to identify those prostate cancer (PCa) patients with high probability of incorrect biopsy Gleason grade group (GG) before making treatment decisions.

METHODS

We retrospectively collected data from PCa patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. Univariable and multivariable logistic regression analyses were preformed to identify independent risk factors associated with upgrading, upstaging and downgrading. By incorporating selected clinical parameters with high predictive value, we constructed three nomograms to predict the probability of upgrading, upstaging and downgrading. Discrimination of nomograms was evaluated by receiver operating characteristic (ROC) analysis with corresponding area under the curve (AUC). Decision curve analysis (DCA) and calibration curves were performed to evaluate calibration and the clinical usefulness of nomograms. Performance of the three nomograms was validated in the testing dataset.

RESULTS

There were 585 PCa patients in total enrolled in this study who met the inclusion criteria. Of the 585 patients, the disease of 262 (44.8 %) was upgraded and 68 (11.6 %) was downgraded, and the disease of 67 (11.5 %) was upstaged. With regard to findings of multivariable analyses, patients' age and biopsy GG (GG 2, GG 3, GG 4 versus GG 1) were significantly associated with upgrading. Moreover, maximum diameter of the index lesion (D-max), clinical T stage (cT3a, cT3b versus cT1-2), number of positive cores and total tumor length were significantly associated with upstaging. Furthermore, d-max, %fPSA (> 0.16 versus ≤ 0.16) and biopsy GG (GG 3, GG 4, GG 5 versus GG 2) were independent predictors of downgrading. The three nomograms displayed good calibration in respective calibration plots. ROC analyses showed good discrimination with satisfactory AUC values and DCA plots demonstrated that the upgrading-risk nomogram, upstaging-risk nomogram and downgrading-risk nomogram were all clinically useful.

CONCLUSIONS

The upgrading-risk nomogram, upstaging-risk nomogram, and downgrading-risk nomogram were developed and correctly predicted the probability of incorrect Gleason grade group assigned to patients undergoing systematic biopsy.

Development and validation of a preoperative nomogram for predicting survival of patients with locally advanced prostate cancer after radical prostatectomy

Background

For selected locally advanced prostate cancer (PCa) patients, radical prostatectomy (RP) is one of the first-line treatments. We aimed to develop a preoperative nomogram to identify what kinds of patients can get the most survival benefits after RP.

Methods

We conducted analyses with data from the Surveillance, Epidemiology, and End Results (SEER) database. Covariates used for analyses included age at diagnosis, marital status, race, American Joint Committee on Cancer (AJCC) 7th TNM stage, Prostate specific antigen, Gleason biopsy score (GS), percent of positive cores. We estimated the cumulative incidence function for cause-specific death. The Fine and Gray’s proportional subdistribution hazard approach was used to perform multivariable competing risk analyses and reveal prognostic factors. A nomogram was built by these factors (including GS, percent of positive cores and N stage) and validated by concordance index and calibration curves. Risk stratification was established based on the nomogram.

Results

We studied 14,185 patients. N stage, GS, and percent of positive cores were the independent prognostic factors used to construct the nomogram. For validating, in the training cohort, the C-index was 0.779 (95% CI 0.736–0.822), and in the validation cohort, the C-index was 0.773 (95% CI 0.710–0.836). Calibration curves showed that the predicted survival and actual survival were very close. The nomogram performed better over the AJCC staging system (C-index 0.779 versus 0.764 for training cohort, and 0.773 versus 0.744 for validation cohort). The new stratification of risk groups based on the nomogram also showed better discrimination than the AJCC staging system.

Conclusions

The preoperative nomogram can provide favorable prognosis stratification ability to help clinicians identify patients who are suitable for surgery.

How to Pick Out the "Unreal" Gleason 3 + 3 Patients: A Nomogram for More Precise Active Surveillance Protocol in Low-Risk Prostate Cancer in a Chinese Population

OBJECTIVE

To develop a nomogram for selecting the "unreal" Gleason score (GS) 3 + 3 patients in biopsy GS 3 + 3 prostate cancer (PCa) patients.

METHODS

Patients who were newly diagnosed with PCa by biopsy and underwent radical prostatectomy in the First Affiliated Hospital of Nanjing Medical University from January 2009 to October 2018 were enrolled. Comparisons were made between GS 3 + 3 and higher grade PCa patients. Logistic regression analysis was performed to determine the risk factors for the "unreal" GS 3 + 3 PCa in biopsy GS 3 + 3 patients. Then, a nomogram was developed to predict the probability of "unreal" GS 3 + 3 PCa according to the results of multivariate analysis. Finally, receiver operating characteristic and decision curve analysis (DCA) curves were structured to identify the efficiency of the predictive model.

RESULTS

Compared to higher GS grade, biopsy GS 3 + 3 had greater upgrade risk (P < 0.05) while a lower proportion of positive surgical margins, seminal vesicle invasion, extra-prostatic extension, lymph node invasion, and nerve invasion (all P < 0.05). Multivariate analysis showed that age, PSAD, prostate imaging reporting and data system (PI-RADS) score and biopsy positive cores were significant risk factors for "unreal" GS 3 + 3. A nomogram was developed utilizing these factors with high prediction performance (area under curve = 0.924). Furthermore, DCA curve suggested that this predictive model was effective.

CONCLUSIONS

The nomogram identified the probability of "unreal" GS 3 + 3 PCa in biopsy GS 3 + 3 PCa patients, which was of great value for clinical guidance in low risk PCa therapy.

Development and validation of prognostic nomograms for patients with metastatic prostate cancer

OBJECTIVE

This study aimed to develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with prostate cancer.

METHODS

Clinical data of patients with mPCa between 2010 and 2014 were retrieved retrospectively, and randomized into training (2/3) and validation sets (1/3). Nomograms were built with potential risk factors based on COX regression analysis. Accuracy was validated using the discrimination and calibration curve for the training and validation groups, respectively.

RESULTS

6659 mPCa patients were collected and enrolled, including 4440 in the training set and 2219 in the validation set. Multivariate analysis showed that age, marital status, PSA, biopsy Gleason score, T stage, and bone metastasis were independent risk factors for both OS and CSS. The concordance index (C-index) of OS was 0.735 (95% CI 0.722-0.748) for the internal validation and 0.735 (95% CI 0.717-0.753) for the external validation. For CSS, it was 0.734 (95% CI 0.721-0.747) and 0.742 (95% CI 0.723-0.761), respectively. The nomograms for predicting OS and CSS displayed better discrimination power in both training and validation sets. Moreover, a favorable consistency between the predicted and actual survival probabilities was demonstrated using calibration curves.

CONCLUSIONS

The nomograms showed good performances for predicting OS and CSS in patients with prostate cancer. It might be a convenient individualized predictive tool for prognosis in clinical practice.

Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes' Theorem to reduce costs and improve outcomes

Prostate cancer is the second leading cause of death from cancer, behind lung cancer, for men in the U. S, with nearly 30,000 deaths per year. A key problem is the difficulty in distinguishing, after biopsy, between significant cancers that should be treated immediately and clinically insignificant tumors that should be monitored by active surveillance. Prostate cancer has been over-treated; a recent European randomized screening trial shows overtreatment rates of 40%. Overtreatment of insignificant tumors reduces quality of life, while delayed treatment of significant cancers increases the incidence of metastatic disease and death. We develop a decision analysis approach based on simulation and probability modeling. For a given prostate volume and number of biopsy needles, our rule is to treat if total length of cancer in needle cores exceeds c, the cutoff value, with active surveillance otherwise, provided pathology is favorable. We determine the optimal cutoff value, c*. There are two misclassification costs: treating a minimal tumor and not treating a small or medium tumor (large tumors were never misclassified in our simulations). Bayes' Theorem is used to predict the probabilities of minimal, small, medium, and large cancers given the total length of cancer found in biopsy cores. A 20 needle biopsy in conjunction with our new decision analysis approach significantly reduces the expected loss associated with a patient in our target population about to undergo a biopsy. Longer needles reduce expected loss. Increasing the number of biopsy cores from the current norm of 10-12 to about 20, in conjunction with our new decision model, should substantially improve the ability to distinguish minimal from significant prostate cancer by minimizing the expected loss from over-treating minimal tumors and delaying treatment of significant cancers.

Prediction models for prostate cancer outcomes: what is the state of the art in 2017?

PURPOSE OF REVIEW

Prostate cancer (PCa) remains a significant public health burden, with multiple points for decision-making at all stages of the disease. Given the amount and variety of data that may influence disease management, prediction models have been published to assist clinicians and patients in making decisions about the best next course of action at many disease states. We sought to review the most important studies related to PCa prediction models since 2016 and evaluate their impact upon the evolving field of risk modeling in PCa.

RECENT FINDINGS

There has been a significant amount of work published in the past year concerning risk modeling in PCa at all stages of disease, ranging from screening to predicting survival with metastatic disease. The majority of recent publications focus upon the addition of a new biomarker to prediction models or upon validating previously published prediction models. In particular, MRI has been the topic of a number of more recent studies.

SUMMARY

Prediction modeling in PCa currently compares the area under the receiver operating curve between models with and without the biomarker of interest to predict the outcome of interest in multiple disease states, ranging from diagnosis to prediction of survival with metastatic disease. Future work should provide additional information regarding clinical impact and measures of prediction confidence.

Whom to Treat: Postdiagnostic Risk Assessment with Gleason Score, Risk Models, and Genomic Classifier

Management of prostate cancer presents unique challenges because of the disease's variable natural history. Accurate risk stratification at the time of diagnosis in clinically localized disease is crucial in providing optimal counseling about management options. To accurately distinguish pathologically indolent tumors from aggressive disease, risk groups are no longer sufficient. Rather, multivariable prognostic models reflecting the complete information known at time of diagnosis offer improved accuracy and interpretability. After diagnosis, further testing with genomic assays or other biomarkers improves risk classification. These postdiagnostic risk assessment tools should not supplant shared decision making, but rather facilitate risk classification and enable more individualized care.

Metabolic characterization and pathway analysis of berberine protects against prostate cancer

Recent explosion of biological data brings a great challenge for the traditional methods. With increasing scale of large data sets, much advanced tools are required for the depth interpretation problems. As a rapid-developing technology, metabolomics can provide a useful method to discover the pathogenesis of diseases. This study was explored the dynamic changes of metabolic profiling in cells model and Balb/C nude-mouse model of prostate cancer, to clarify the therapeutic mechanism of berberine, as a case study. Here, we report the findings of comprehensive metabolomic investigation of berberine on prostate cancer by high-throughput ultra performance liquid chromatography-mass spectrometry coupled with pattern recognition methods and network pathway analysis. A total of 30 metabolite biomarkers in blood and 14 metabolites in prostate cancer cell were found from large-scale biological data sets (serum and cell metabolome), respectively. We have constructed a comprehensive metabolic characterization network of berberine to protect against prostate cancer. Furthermore, the results showed that berberine could provide satisfactory effects on prostate cancer via regulating the perturbed pathway. Overall, these findings illustrated the power of the ultra performance liquid chromatography-mass spectrometry with the pattern recognition analysis for large-scale biological data sets may be promising to yield a valuable tool that insight into the drug action mechanisms and drug discovery as well as help guide testable predictions.