A direct comparison of the diagnostic accuracy of three prostate cancer nomograms designed to predict the likelihood of a positive initial transrectal biopsy

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.

A Novel Scoring System for Prediction of Prostate Cancer Based on Shear Wave Elastography and Clinical Parameters

OBJECTIVE

To develop a novel scoring system for the prediction of prostate cancer (PCa).

METHODS

We assessed 127 patients who underwent a prostate biopsy. Prior to biopsy, we performed shear wave elastography (SWE), transrectal ultrasound, digital rectal exam, total prostatic specific antigen, PSA density (PSAD), and free PSA/total PSA ratio (F/T). We developed an 11-point scoring system based on SWE and these clinical parameters.

RESULTS

PCa was diagnosed in 51 (40.2%) of 127 patients and 192 (25.2%) of 762 sextants on initial biopsy. ROC curve analyses showed that the cutoff value (COV) for SWE was 40.8 kpa at the sextant level. The AUC of score system based on the SWE and clinical parameters (0.911) was significantly different from scoring systems based on SWE alone (0.842) or clinical parameters alone (0.868). For this 11-point scoring system, the optimal COV, Youden index, sensitivity, specificity, PPV, NPV, and AUC were 3 points, 0.66, 76.5% 89.5%, 82.98%, 85.00%, and 0.911, respectively. There were 68 negative biopsy results in patients with 0-3 points, and the detection rate of PCa was 100% in patients with scores exceeding 6 points.

CONCLUSION

This 11-point scoring system based on SWE and clinical parameters has the good diagnostic performance for predicting PCa. It may be useful in selecting patients for biopsy, substantially reducing the number of unnecessary biopsies while ensuring that few cancers are missed.

A comparison of 3 on-line nomograms with the detection of primary circulating prostate cells to predict prostate cancer at initial biopsy

INTRODUCTION

The use of nomograms which include the PSA may improve the predictive power of obtaining a prostate biopsy (PB) positive for cancer. We compare the use of three on-line nomagrams with the detection of primary malignant circulating prostate cells (CPCs) to predict the results of an initial PB in men with suspicion of prostate cancer.

METHODS AND PATIENTS

Consecutive men with suspicion of prostate cancer underwent a 12 core TRUS prostate biopsy; age, total serum PSA, percent free PSA, family history, ethnic origin and prostate ultrasound results were used for risk assessment using the online nomograms. Mononuclear cells were obtained by differential gel centrifugation from 8ml of blood and CPCs were identified using double immunomarcation with anti-PSA and anti-P504S. A CPC was defined as a cell expressing PSA and P504S and defined as negative/positive. Biopsies were classified as cancer/no-cancer. Areas under the curve (AUC) for each parameter were calculated and compared and diagnostic yields were calculated.

RESULTS

1,223 men aged>55 years participated, 467 (38.2%) had a biopsy positive for cancer of whom 114/467 (24.4%) complied with the criteria for active observation. Area under the curve analysis showed CPC detection to be superior (p<0.001), avoiding 57% of potential biopsies while missing 4% of clinically significant prostate cancers.

CONCLUSIONS

The CPC detection was superior to the nomograms in predicting the presence of prostate cancer at initial biopsy; its high negative predictive value potentially reduces the number of biopsies while missing few significant cancers, being superior to the nomograms in this aspect. Being a positive/negative test the detection of CPCs avoids defining a cutoff value which may differ between populations.

What risk of prostate cancer led urologist to recommend prostate biopsies?

OBJECTIVE

The aim of this study was to estimate the risk of prostate cancer that led urologists to perform prostate biopsies.

PATIENTS AND METHODS

Eight hundred and eight patients had prostate biopsies in 5 tertiary centres in 2010. Following data were collected: age, PSA, DRE, prostate volume, negative prior prostate biopsy and estimated life expectancy (> or <10 years). The risk of prostate cancer was calculated by validated nomogram of PCPT-CRC and SWOP-PRI and correlated with pathological biopsy results.

RESULTS

In final analysis, 625 patients were included, 568 (90.9%) had a life expectancy greater than 10 years. Prostate cancer was found in 291 (46.6%) cases. These patients were older (66.7 ± 6.8 vs 64.3 ± 5.6 years, P < 0.001), had higher PSA values (10 ± 7.9 vs 7.7 ± 4.3 ng/mL, P < 0.0001) and the prostate volume decreased (43.8 ± 19.8 vs 51.3 ± 20.7 mL, P < 0.0001) compared with healthy subjects. Digital Rectal Examination was more frequently suspicious in the group of patients with prostate cancer (43.6% vs 18.9%, P < 0.0001). Risk of prostate cancer estimated was 50.6 ± 14% for PCPT-CRC without ATCD, 56.2 ± 12.8% with PCPT-CRC ATCD and 31.2 ± 17.3% for SWOP-PRI. The likelihood of high-risk prostate cancer was 22.4 ± 16.9% with the PCPT-CRC, and 14.8 ± 18.2% with SWOP-PRI.

CONCLUSION

This study showed that urologists performed prostate biopsies when the risk of cancer was high.

Prostate cancer nomograms: a review of their use in cancer detection and treatment

As prostate cancer treatment discussions have grown more complex, increasing numbers of nomograms to guide decision-making have been found in the literature. Such nomograms can influence every step in the prostate cancer therapeutic process, from determining the need for biopsy to the need for adjuvant therapy. With a properly counseled patient who is aware of the limitations of nomograms, such tools assist in the shared decision-making that characterizes modern informed consent.

Defining the radiobiology of prostate cancer progression: An important question in translational prostate cancer research

Prostate cancer is one of the most common malignancies affecting men worldwide. High mortality rates from advanced and metastatic prostate cancer in the United States are contrasted by a relatively indolent course in the majority of cases. This gives hope for finding methods that could direct personalized diagnostic, preventative, and treatment approaches to patients with prostate cancer. Recent advances in multiparametric magnetic resonance imaging (MP-MRI) offer a noninvasive diagnostic intervention which allows correlation of prostate tumor image characteristics with underlying biologic evidence of tumor progression. The power of MP-MRI includes examination of both local invasion and nodal disease and might overcome the challenges of analyzing the multifocal nature of prostate cancer. Future directions include a careful analysis of the genomic signature of individual prostatic lesions utilizing image-guided biopsies. This review examines the diagnostic potential of MRI in prostate cancer.

Chinese nomogram to predict probability of positive initial prostate biopsy: a study in Taiwan region

Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower in Chinese men, nomograms based on other populations cannot be directly applied to Chinese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Chinese male population. Data were collected from 893 Chinese male referrals, 697 in the derivation set and 196 in the external validation set, who underwent initial prostate biopsies as individual screening. We analyzed age, prostate volume, total prostate-specific antigen (PSA), PSA density (PSAD), digital rectal examinations (DRE) and transrectal ultrasound (TRUS) echogenicity. Logistic regression analysis estimated odds ratio, 95% confidence intervals and P values. Independent predictors of a positive biopsy result included advanced age, small prostate volume, elevated total PSA, abnormal digital rectal examination, and hyperechoic or hypoechoic TRUS echogenicity. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver-operating characteristic curve for the model was 88.8%, which was greater than that of the prediction based on total PSA alone (area under the receiver-operating characteristic curve 74.7%). If externally validated, the predictive probability was 0.827 and the accuracy rate was 78.1%, respectively. Incorporating clinical and laboratory data into a prebiopsy nomogram improved the prediction of prostate cancer compared with predictions based solely on the individual factors.

Old versus new FIGO staging systems in predicting overall survival in patients with uterine leiomyosarcoma: a study of 86 cases

OBJECTIVES

Uterine leiomyosarcoma (uLMS) was staged using the FIGO system for endometrial cancers. The new FIGO system takes into consideration tumor size disregarding myometrial and cervical involvement. We aimed to compare the two systems and see which more accurately predicts overall survival (OS).

METHODS

86 patients with uLMS (1984-2010) were retrospectively staged using both FIGO systems. Mean OS rates were estimated using the Kaplan-Meier method.

RESULTS

More patients had stage-I disease by the new FIGO system (42 versus 33). Five versus 18 and 27 versus 5 had old and new stage-II and III diseases respectively. Five and 4 patients with old stage II and III uLMS respectively were downstaged to stage I while 18 with old stage III were downstaged to stage II. Median follow-up was 23.5 months with a median OS of 114 (95% CI, 61-166) months. Although patients with stage I tumors had a higher mean OS rate compared to those with higher stage disease by either system, patients with old stage II-IV disease showed similar mean OS rates, with stage III-IV patients having a slightly better mean OS and a similar trend was observed with the new system. Patients with new FIGO stage III had a higher mean OS rate than those with stage II or IV disease (37.6 versus 28.1 and 34.3 months). Nonetheless, no statistical significant differences were seen in OS according to stage using either system (p=0.786 and p=0.400 respectively).

CONCLUSION

Neither FIGO staging system is ideal in classifying patients into four clinically significant stages.