Predicting the outcome of prostate biopsy in a racially diverse population: a prospective study

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Histopathology, pharmacotherapy, and predictors of prostatic malignancy in elderly male patients with raised prostate-specific antigen levels - A prospective study

Background

Prostate cancer is the second most common cancer among adult men in the world, and the diagnosis requires biopsy. Prostate-specific antigen (PSA) test along with digital rectal examination (DRE) increases the detection rate of prostate cancer than DRE alone. The objective of this study was to correlate serum PSA level with histopathological diagnosis, identify the predictors of malignancy, and describe the pharmacotherapy of patients with serum PSA levels >4 ng/ml.

Materials and Methods

This was a hospital-based observational study done among patients who presented with lower urinary tract symptoms and PSA levels >4 ng/ml who were planned to undergo prostatic biopsy. DRE followed by transrectal ultrasound (TRUS) assessment and guided sextant (6-core) prostatic biopsy was performed.

Results

One hundred and four patients were screened and 87 were included. Nineteen patients were diagnosed with malignancy, and among them, eight had bone metastasis. Spearman's correlation coefficient between PSA and malignancy was 0.449 (P ≤ 0.001). Multivariate analysis suggested that the factors (adjusted odds ratio; 95% confidence interval; P value) such as increasing age (1.127; 1.013, 1.253; 0.027), nodular prostate (22.668; 4.655, 110.377; P < 0.001), and PSA (1.034; 1.004, 1.064; 0.024) were significant predictors of prostate cancer. All patients with benign prostatic hyperplasia were advised a combination therapy with 5-alpha reductase inhibitor and selective alpha-1 receptor antagonist while those with malignancy were prescribed androgen deprivation therapy with antiosteoporosis therapy.

Conclusion

In elderly patients with raised PSA levels or suspicious DRE findings, TRUS-guided prostate is recommended to rule out malignancy and plan appropriate management.

Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging

PURPOSE

To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer.

MATERIALS AND METHODS

The Institutional Review Board approved this HIPAA-compliant study and waived informed consent. Eighteen men with prostate cancer (median age, 55 years; range, 36-71 years) who underwent endorectal MRI/MRSI before radical prostatectomy were included in this study. These patients had at least one cancer area on whole-mount histopathological map and at least one matching MRSI voxel suspicious for cancer detected. Two ANN models for automatic classification of MRSI voxels in the prostate were implemented and compared: model 1, which used only spectra as input, and model 2, which used the spectra plus information from anatomical segmentation. The models were trained, tested and validated using spectra from voxels that the spectroscopist had designated as cancer and that were verified on histopathological maps.

RESULTS

At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949).

CONCLUSION

Automatic analysis of prostate MRSI to detect cancer using ANN model is feasible. Application of anatomical segmentation from MRI as an additional input to ANN improves the accuracy of detecting cancerous voxels from MRSI.

3.4 kb mitochondrial genome deletion serves as a surrogate predictive biomarker for prostate cancer in histopathologically benign biopsy cores

Recently, we described a 3.4-kb mitochondrial genome deletion having significance for identifying malignant and benign prostate tissues (p < 0.001). This biomarker was also present in normal appearing tissue, in close proximity to a tumour indicating a "field effect." In the present study, we report 4 cases (3 malignant, 1 benign) which suggest that this field effect may occur before tumourigenesis; this effect may also identify the presence of a small tumour focus/foci, which are difficult to detect with single or multiple biopsy procedures.

Critical review of prostate cancer predictive tools

Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.

Operator is an independent predictor of detecting prostate cancer at transrectal ultrasound guided prostate biopsy

PURPOSE

We investigated whether interoperator differences exist in the setting of prostate cancer detection by transrectal ultrasound guided prostate biopsy. Our secondary aim was to investigate whether a learning curve exists for prostate cancer detection.

MATERIALS AND METHODS

A prospective database from 2000 to 2008 including 9,072 transrectal ultrasound guided prostate biopsies at our institution was limited to 4,724 done at initial presentation. Biopsies were performed by 4 uroradiologists. The OR for detecting cancer on transrectal ultrasound guided prostate biopsy was calculated for likely independent prognostic variables, including operator. We also examined the rate of biopsy positivity in increments, comparing the first and last cohorts. The senior radiologist (AT) with the most biopsies (75%) was considered the referent for prostate cancer detection. Univariate and multivariate logistic regression modeling was used to determine significant covariates with p <0.05 deemed relevant.

RESULTS

Prostate cancer was detected in 2,331 men (49.3%). Operators performed a median of 514 transrectal ultrasound guided prostate biopsies (range 187 to 3,509) with a prostate cancer detection rate of 43.8% to 52.4% (p = 0.001). Other significant covariates were prostate specific antigen, suspicious lesions on ultrasound, nodule on digital rectal examination, smaller prostate volume and increasing patient age. Operator was a significant multivariate predictor of cancer detection (OR 0.67 to 0.89, p = 0.003). No learning curve was detected and biopsy rates were consistent throughout the series.

CONCLUSIONS

Significant differences in prostate cancer detection exist among operators who perform transrectal ultrasound guided prostate biopsy even in the same setting. The volume of previously performed transrectal ultrasound guided prostate biopsies does not appear to influence the positive prostate cancer detection rate, nor could a learning curve be identified. Differences in prostate cancer detection among operators are likely related to unknown differences in expertise or technique. Further research is needed.

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

Inventory of prostate cancer predictive tools

PURPOSE OF REVIEW

We created an inventory of current predictive tools available for prostate cancer. This review may serve as an initial step toward a comprehensive reference guide for physicians to locate published nomograms that apply to the clinical decision in question. Using MEDLINE a literature search was performed on prostate cancer predictive tools from January 1966 to November 2007. We describe the patient populations to which they apply and the outcomes predicted, and record their individual characteristics.

RECENT FINDINGS

The literature search generated 111 published prediction tools that may be applied to patients in various clinical stages of disease. Of the 111 prediction tools, only 69 had undergone validation. We present an inventory of models with input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed.

SUMMARY

Decision rules, such as nomograms, provide evidence-based and at the same time individualized predictions of the outcome of interest. Such predictions have been repeatedly shown to be more accurate than those of clinicians, regardless of their level of expertise. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous high-risk patient groups for whom new cancer therapeutics will be investigated.

Computer technology in detection and staging of prostate carcinoma: A review

After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists. Radiology 231, 7-9]. This is particularly the case in the analysis of mammographic images [Helvie, M.A., Hadjiiski, L., Makariou, E., Chan, H.P., Petrick, N., Sahiner, B., Lo, S.C., Freedman, M., Adler, D., Bailey, J., Blane, C., Hoff, D., Hunt, K., Joynt, L., Klein, K., Paramagul, C., Patterson, S.K., Roubidoux, M.A., 2004. Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology 231, 208-214] and in the detection of pulmonary nodules [Reeves, A.P., Kostis, W.J., 2000. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am. 38, 497-509]. However, similar approaches can be applied more widely with the promise of increasing clinical utility in other areas. We review how computer-aided approaches may be applied in the diagnosis and staging of prostatic cancer. The current status of computer technology is reviewed, covering artificial neural networks for detection and staging, computerised biopsy simulation and computer-assisted analysis of ultrasound and magnetic resonance images.

H-NMR-based metabolic signatures of mild and severe ischemia/reperfusion injury in rat kidney transplants

BACKGROUND

Severe ischemia/reperfusion (IR) injury is a risk factor for delayed graft function. Delayed graft function remains difficult to predict, and it currently relies primarily on serum creatinine (SCr), urine output, and occasionally on graft biopsy. (1)H-NMR (nuclear magnetic resonance spectroscopy) based metabolomics was used to establish IR-specific metabolic markers in both blood and kidney tissue. These markers were compared to SCr and graft histology.

METHODS

Male Lewis rats were used for kidney transplantation. Two cold ischemia (CI) groups (24- and 42-hour) and two transplantation groups [after 24 (TX24) and after 42 hours (TX42) of CI] were compared to a control group. Whole blood and kidney tissue were collected for further analysis.

RESULTS

SCr levels taken 24 hours after transplantation were 1.6 +/- 0.12 mg/dL (TX24) and 2.1 +/- 0.5 mg/dL (TX42), (P= n.s.). Histology samples revealed mild injury in the TX24 group and severe injury in the TX42 group. A significantly decreased level of polyunsaturated fatty acids (PUFA) and elevated levels of allantoin, a marker of oxidative stress, was found in the renal tissue. In the blood, both trimethylamine-N-oxide (TMAO), a marker of renal medullary injury, and allantoin were significantly increased. Allantoin levels were low in both the control and CI groups. Levels were significantly increased after reperfusion (control 0.02 +/- 0.03 micromol/mL, TX24 1.13 +/- 0.22, and TX42 1.89 +/- 0.38, P < 0.001), and correlated with cold ischemia time (r= 0.96) and TMAO (r= 0.94).

CONCLUSION

The (1)H-NMR metabolic profiles of both the mild and severe IR groups revealed significant changes consistent with graft histology, while the SCr did not.

Model to predict prostate biopsy outcome in large screening population with independent validation in referral setting

OBJECTIVES

To develop a model capable of predicting prostate biopsy outcomes in a large screening population, with independent validation in the referral setting.

METHODS

Data from 3814 men participating in the Tyrol screening project were used to develop the model. Prospectively collected data from two independent sites in the United States (Virginia Mason Clinic, Seattle, Wash and Stanford University, Stanford, Calif) were used to validate the model independently. The Tyrol data was split randomly into three cross-validation sets, and a feed-forward, back error-propagation artificial neural network (ANN) was alternately trained on a combination of two of these data sets and validated on the remaining data set. Similarly, three logistic regression (LR) models were produced and validated using identical cross-validation data sets. The Tyrol model with the median area under receiver operating characteristic curve (AUROC) was then validated against the Virginia Mason (n = 491) and Stanford University (n = 483) data sets.

RESULTS

The AUROCs for the three cross-validations were 0.74, 0.76, and 0.75 for the ANN and 0.75, 0.76, and 0.75 for the LR models. The mean AUROC for both ANN and LR was 0.75 with a standard deviation of 0.009 for ANN and 0.006 for LR. The AUROCs for the Virginia Mason and Stanford University data were 0.74 (both ANN and LR) and 0.73 (ANN) and 0.72 (LR), respectively.

CONCLUSIONS

This model, designed to predict the prostate biopsy outcome, performed accurately and consistently when validated with data from two independent referral centers in the United States, suggesting that it generalizes well and may be of clinical utility to a broad range of patients.

Preoperative neural network using combined magnetic resonance imaging variables, prostate specific antigen, and Gleason score to predict prostate cancer recurrence after radical prostatectomy

OBJECTIVE

An artificial neural network analysis (ANNA) was developed to predict the biochemical recurrence more effectively than regression models based on the combined use of pelvic coil magnetic resonance imaging (pMRI), prostate specific antigen (PSA) and biopsy Gleason score in patients with clinically organ-confined prostate cancer after radical prostatectomy (RP).

METHODS

Two-hundred-and-ten patients undergoing retropubic RP with pelvic lymphadenectomy were evaluated. Predictive study variables included clinical TNM classification, preoperative serum PSA, biopsy Gleason score, transrectal ultrasound (TRUS) findings, and pMRI findings. The predicted result was a biochemical failure (PSA >or=0.1 ng/ml). Using a five-way cross-validation method, the predicted ability of ANNA for a validation set of 200 randomly selected patients was compared with those of Cox regression analysis and "Kattan nomogram" by area under the receiver operating characteristic curve (AUC) analysis.

RESULTS

Seventy-three patients (35%) failed at median follow-up of 61 (mean: 60, range: 2-94) months. Using similar input variables, the AUC of ANNA (0.765, 95% Confidence Interval [CI]: 0.704-0.825) was comparable (p > 0.05) to those for Cox regression (0.738, 95%CI: 0.691-0.819) and Kattan nomogram (0.728, 95%CI: 0.644-0.819). Contrarily, adding the pMRI findings, the ANNA is significantly (p < 0.05) superior to any other predictive model (0.897, 95%CI: 0.841-0.977). The Gleason score represented the most influential predictor (relative weight: 2.4) of PSA recurrence, followed by pMRI (2.2), and PSA (2.0).

CONCLUSION

ANNA is superior to regression models to predict accurately biochemical recurrence. The relative importance of pMRI and the utility of ANNA to predict the PSA failure in patients referred for RP must be confirmed in further trials.

Biochemical recurrence and survival prediction models for the management of clinically localized prostate cancer

A number of new predictive modeling techniques have emerged in the past several years. These methods, which have been developed in fields such as artificial intelligence research, engineering, and meteorology, are now being applied to problems in medicine with promising results. This review outlines our recent work with use of selected advanced techniques such as artificial neural networks, genetic algorithms, and propensity scoring to develop useful models for estimating the risk of biochemical recurrence and long-term survival in men with clinically localized prostate cancer. In addition, we include a description of our efforts to develop a comprehensive prostate cancer database that, along with these novel modeling techniques, provides a powerful research tool that allows for the stratification of risk for treatment failure and survival by such factors as age, race, and comorbidities. Clinical and pathologic data from 1400 patients were used to develop the biochemical recurrence model. The area under the receiver operating characteristic curve for this model was 0.83, with a sensitivity of 85% and specificity of 74%. For the survival model, data from 6149 men were used. Our analysis indicated that age, income, and comorbidities had a statistically significant impact on survival. The effect of race did not reach statistical significance in this regard. The C index value for the model was 0.69 for overall survival. We conclude that these methods, along with a comprehensive database, allow for the development of models that provide estimates of treatment failure risk and survival probability that are more meaningful and clinically useful than those previously developed.

Impact of training level of urology residents on the detection of prostate cancer on TRUS biopsy

The objective of this study is to evaluate the performance of urology residents at each training level in detecting prostate cancer with transrectal ultrasound-guided (TRUS) biopsy. The inclusion criteria were: (1) prostate-specific antigen (PSA) 4-10 ng/ml; and (2) 10-12 cores per biopsy session. Data from repeat biopsy sessions were excluded. Overall prostate cancer detection rate for 170 patients was 39.4%. PSA, digital rectal examination (DRE), and prostate volume were predictors of cancer detection. There were no significant differences in overall cancer detection rates, PSA, DRE, or prostate volume between resident levels. In conclusion, urology residents at all levels of training perform equally well at detecting cancer using TRUS prostate biopsy technology.