A New Preoperative Nomogram to Predict Minimal Prostate Cancer: Accuracy and Error Rates Compared to Other Tools to Select Patients for Active Surveillance

Diagnostic Accuracy of Contemporary Selection Criteria in Prostate Cancer Patients Eligible for Active Surveillance: A Bayesian Network Meta-Analysis

Background

Several active surveillance (AS) criteria have been established to screen insignificant prostate cancer (insigPCa, defined as organ confined, low grade and small volume tumors confirmed by postoperative pathology). However, their comparative diagnostic performance varies. The aim of this study was to compare the diagnostic accuracy of contemporary AS criteria and validate the absolute diagnostic odds ratio (DOR) of optimal AS criteria.

Methods

First, we searched Pubmed and performed a Bayesian network meta-analysis (NMA) to compare the diagnostic accuracy of contemporary AS criteria and obtained a relative ranking. Then, we searched Pubmed again to perform another meta-analysis to validate the absolute DOR of the top-ranked AS criteria derived from the NMA with two endpoints: insigPCa and favorable disease (defined as organ confined, low grade tumors). Subgroup and meta-regression analyses were conducted to identify any potential heterogeneity in the results. Publication bias was evaluated.

Results

Seven eligible retrospective studies with 3,336 participants were identified for the NMA. The diagnostic accuracy of AS criteria ranked from best to worst, was as follows: Epstein Criteria (EC), Yonsei criteria, Prostate Cancer Research International: Active Surveillance (PRIAS), University of Miami (UM), University of California-San Francisco (UCSF), Memorial Sloan-Kettering Cancer Center (MSKCC), and University of Toronto (UT). I² = 50.5%, and sensitivity analysis with different insigPCa definitions supported the robustness of the results. In the subsequent meta-analysis of DOR of EC, insigPCa and favorable disease were identified as endpoints in ten and twenty-two studies, respectively. The pooled DOR for insigPCa and favorable disease were 0.44 (95%CI, 0.31–0.58) and 0.66 (95%CI, 0.61–0.71), respectively. According to a subgroup analysis, the DOR for favorable disease was significantly higher in US institutions than that in other regions. No significant heterogeneity or evidence of publication bias was identified.

Conclusions

Among the seven AS criteria evaluated in this study, EC was optimal for positively identifying insigPCa patients. The pooled diagnostic accuracy of EC was 0.44 for insigPCa and 0.66 when a more liberal endpoint, favorable disease, was used.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/], PROSPERO [CRD42020157048].

Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer

IMPORTANCE

Existing prognostic cancer tools include biological and laboratory variables. However, patients often do not know this information, preventing them from using the tools and understanding their prognosis.

OBJECTIVE

To develop and validate a prognostic survival model for all cancer types that incorporates information on symptoms and performance status over time.

DESIGN, SETTING, AND PARTICIPANTS

This is a retrospective, population-based, prognostic study of data from patients diagnosed with cancer from January 1, 2008, to December 31, 2015, in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). The derivation cohort was used to develop a multivariable Cox proportional hazards regression model with baseline characteristics under a backward stepwise variable selection process to predict the risk of mortality as a function of time. Covariates included demographic characteristics, clinical information, symptoms and performance status, and health care use. Model performance was assessed on the validation cohort by C statistics and calibration plots. Data analysis was performed from February 6, 2018, to November 6, 2019.

MAIN OUTCOMES AND MEASURES

Time to death from diagnosis (year 0) recalculated at each of 4 annual survivor marks after diagnosis (up to year 4).

RESULTS

A total of 255 494 patients diagnosed with cancer were identified (135 699 [53.1%] female; median age, 65 years [interquartile range, 55-73 years]). The cohort decreased to 217 055, 184 822, 143 649, and 109 569 patients for each of the 4 years after diagnosis. In the derivation cohort year 0, and the most common cancers were breast (30 855 [20.1%]), lung (19 111 [12.5%]), and prostate (18 404 [12.0%]). A total of 47 614 (31.1%) had stage III or IV disease. The mean (SD) time to death in year 0 was 567 (715) days. After backward stepwise selection in year 0, the following factors were associated with increased risk of death by more than 10%: being hospitalized; having congestive heart failure, chronic obstructive pulmonary disease, or dementia; having moderate to high pain; having worse well-being; having functional status in the transitional or end-of-life phase; having any problems with appetite; receiving end-of-life home care; and living in a nursing home. Model discrimination was high for all models (C statistic: 0.902 [year 0], 0.912 [year 1], 0.912 [year 2], 0.909 [year 3], and 0.908 [year 4]).

CONCLUSIONS AND RELEVANCE

The model accurately predicted changing cancer survival risk over time using clinical, symptom, and performance status data and appears to have the potential to be a useful prognostic tool that can be completed by patients. This knowledge may support earlier integration of palliative care.

Fifteen-year analysis of prostate biopsies in Western Australia including recent impact of multiparametric magnetic resonance imaging

BACKGROUND

The number of men undergoing prostate biopsy and subsequent cancer detection rates has changed significantly over the past 15 years. We aim to evaluate changes in the diagnostic pathway of prostate cancer between 2003 and 2018.

METHODS

A total of 13 844 Western Australian biopsy-naive men were assessed to determine trends in age, prostate-specific antigen levels, number of core samples, positive cores and tumour grade (Gleason) between 2003 and 2018. Further, in 2018, the impact of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) was also assessed.

RESULTS

Between 2003 and 2012, the number of men undergoing biopsy increased from 1445 to 3100. During this time, the prostate cancer detection rate (%) remained unchanged. However, in 2018, 2042 men underwent prostate biopsy (reduction of 34.1%) and the detection rate increased to 72.6%. The incidence of low-grade cancer (Gleason score <7) increased from 28.1% in 2003 to 36.2% in 2012, but it decreased significantly to 15.1% by 2018. High-grade cancer (Gleason score >7) declined from 21.3% in 2003 to 15.2% in 2012 but then increased to 35.7% in 2018. The use of mpMRI in 2018 improved the detection rate of high-grade cancer. However, its specificity remains low (29.7%) and a considerable proportion of low Prostate Imaging Reporting and Data System score lesions was later diagnosed with cancer unsuitable for active surveillance.

CONCLUSION

In recent years, there has been a significant increase in the diagnosis high-grade cancer and a reduction in cancer suitable for active surveillance. mpMRI identifies high-grade tumours but is not a reliable alternative to prostate biopsy.

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.

Periprostatic fat tissue transcriptome reveals a signature diagnostic for high-risk prostate cancer

Evidence suggests that altered adipose tissue homeostasis may be an important contributor to the development and/or progression of prostate cancer. In this study, we investigated the adipose transcriptional profiles of low- and high-risk disease to determine both prognostic potential and possible biological drivers of aggressive disease. RNA was extracted from periprostatic adipose tissue from patients categorised as having prostate cancer with either a low or high risk of progression based on tumour characteristics at prostatectomy and profiled by RNA sequencing. The expression of selected genes was then quantified by qRT-PCR in a cross-validation cohort. In the first phase, a total of 677 differentially transcribed genes were identified, from which a subset of 14 genes was shortlisted. In the second phase, a 3 gene (IGHA1, OLFM4, RERGL) signature was refined and evaluated using recursive feature selection and cross-validation, obtaining a promising discriminatory utility (area under curve 0.72) at predicting the presence of high-risk disease. Genes implicated in immune and/or inflammatory responses predominated. Periprostatic adipose tissue from patients with high-risk prostate cancer has a distinct transcriptional signature that may be useful for detecting its occult presence. Differential expression appears to be driven by a local immune/inflammatory reaction to more advanced tumours, than any specific adipose tissue-specific tumour-promoting mechanism. This signature is transferable into a clinically usable PCR-based assay, which in a cross-validation cohort shows diagnostic potential.

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

OBJECTIVE

The aim of this study was to develop a nomogram to predict the probability of insignificant prostate cancer.

MATERIALS AND METHODS

A retrospective analysis was conducted of patients who underwent radical prostatectomy at a Korean hospital between January 2005 and December 2014. Patients with pathologically insignificant prostate cancer were defined as having organ-confined disease with tumor volume less than 0.5 cm³ without Gleason scores of 4 or 5. Multivariable logistic regression analysis with a stepwise selection was used to model the relationship between preoperative characteristics and insignificant prostate cancer, and a nomogram to predict the probability of insignificant prostate cancer was created. Receiver operating characteristics (ROC) analysis was performed to assess the predictive value of the model.

RESULTS

The final study population consisted of 1343 patients. Among these patients, insignificant prostate cancer was confirmed in 188 men (14.0%) at the time of prostatectomy. Six independent predictors of insignificant prostate cancer were identified: number of positive cores (p < 0.001), maximal single core tumor involvement (p < 0.001), biopsy Gleason score (p < 0.001), prostate volume (p = 0.024), patient age (p < 0.001) and prostate-specific antigen density (p < 0.001) in the multivariable model. A nomogram to predict insignificant prostate cancer was developed using these six preoperative characteristics. The area under the ROC curve for nomogram predictions was 0.87.

CONCLUSION

The nomogram developed in this paper identifies the probability of insignificant prostate cancer and gives providers more information to guide their clinical decisions.

Evaluation of models predicting insignificant prostate cancer to select men for active surveillance of prostate cancer

BACKGROUND

In an era of personalized medicine, individualized risk assessment using easily available tools on the internet and the literature are appealing. However, uninformed use by clinicians and the public raises potential problems. Herein, we assess the performance of published models to predict insignificant prostate cancer (PCa), using a multi-national low-risk population that may be considered for active surveillance (AS) based on contemporary practice.

METHODS

Data on men suitable for AS but undergoing upfront radical prostatectomy were pooled from three international academic institutions in Cambridge (UK), Toronto (Canada) and Melbourne (Australia). Four predictive models identified from literature review were assessed for their ability to predict the presence of four definitions of insignificant PCa. Evaluation was performed using area under the curve (AUC) of receiver operating characteristic curves and Brier scores for discrimination, calibration curves and decision curve analysis.

RESULTS

A cohort of 460 men meeting the inclusion criteria of all four nomograms was identified. The highest AUCs calculated for any of the four models ranged from 0.618 to 0.664, suggesting weak positive discrimination at best. Models had best discriminative ability for a definition of insignificant disease characterized by organ-confined Gleason score ⩽6 with a total volume ⩽0.5 ml or 1.3 ml. Calibration plots showed moderate range of predictive ability for the Kattan model though this model did not perform well at decision curve analysis.

CONCLUSIONS

External assessment of models predicting insignificant PCa showed moderate performance at best. Uninformed interpretation may cause undue anxiety or false reassurance and they should be used with caution.

Clinical implications of a multiparametric magnetic resonance imaging based nomogram applied to prostate cancer active surveillance

PURPOSE

Multiparametric magnetic resonance imaging may be beneficial in the search for rational ways to decrease prostate cancer intervention in patients on active surveillance. We applied a previously generated nomogram based on multiparametric magnetic resonance imaging to predict active surveillance eligibility based on repeat biopsy outcomes.

MATERIALS AND METHODS

We reviewed the records of 85 patients who met active surveillance criteria at study entry based on initial biopsy and who then underwent 3.0 Tesla multiparametric magnetic resonance imaging with subsequent magnetic resonance imaging/ultrasound fusion guided prostate biopsy between 2007 and 2012. We assessed the accuracy of a previously published nomogram in patients on active surveillance before confirmatory biopsy. For each cutoff we determined the number of biopsies avoided (ie reliance on magnetic resonance imaging alone without rebiopsy) over the full range of nomogram cutoffs.

RESULTS

We assessed the performance of the multiparametric magnetic resonance imaging active surveillance nomogram based on a decision to perform biopsy at various nomogram generated probabilities. Based on cutoff probabilities of 19% to 32% on the nomogram the number of patients who could be spared repeat biopsy was 27% to 68% of the active surveillance cohort. The sensitivity of the test in this interval was 97% to 71% and negative predictive value was 91% to 81%.

CONCLUSIONS

Multiparametric magnetic resonance imaging based nomograms may reasonably decrease the number of repeat biopsies in patients on active surveillance by as much as 68%. Analysis over the full range of nomogram generated probabilities allows patient and caregiver preference based decision making on the risk assumed for the benefit of fewer repeat biopsies.

Management of prostate cancer in older patients: updated recommendations of a working group of the International Society of Geriatric Oncology

In 2010, the International Society of Geriatric Oncology (SIOG) developed treatment guidelines for men with prostate cancer who are older than 70 years old. In 2013, a new multidisciplinary SIOG working group was formed to update these recommendations. The consensus of the task force is that older men with prostate cancer should be managed according to their individual health status, not according to age. On the basis of a validated rapid health status screening instrument and simple assessment, the task force recommends that patients are classed into three groups for treatment: healthy or fit patients who should have the same treatment options as younger patients; vulnerable patients with reversible impairment who should receive standard treatment after medical intervention; and frail patients with non-reversible impairment who should receive adapted treatment.

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.

A three-protein biomarker panel assessed in diagnostic tissue predicts death from prostate cancer for men with localized disease

Only a minority of prostate cancers lead to death. Because no tissue biomarkers of aggressiveness other than Gleason score are available at diagnosis, many nonlethal cancers are treated aggressively. We evaluated whether a panel of biomarkers, associated with a range of disease outcomes in previous studies, could predict death from prostate cancer for men with localized disease. Using a case-only design, subjects were identified from three Australian epidemiological studies. Men who had died of their disease, “cases” (N = 83), were matched to “referents” (N = 232), those who had not died of prostate cancer, using incidence density sampling. Diagnostic tissue was retrieved to assess expression of AZGP1, MUC1, NKX3.1, p53, and PTEN by semiquantitative immunohistochemistry (IHC). Poisson regression was used to estimate mortality rate ratios (MRRs) adjusted for age, Gleason score, and stage and to estimate survival probabilities. Expression of MUC1 and p53 was associated with increased mortality (MRR 2.51, 95% CI 1.14–5.54, P = 0.02 and 3.08, 95% CI 1.41–6.95, P = 0.005, respectively), whereas AZGP1 expression was associated with decreased mortality (MRR 0.44, 95% CI 0.20–0.96, P = 0.04). Analyzing all markers under a combined model indicated that the three markers were independent predictors of prostate cancer death and survival. For men with localized disease at diagnosis, assessment of AZGP1, MUC1, and p53 expression in diagnostic tissue by IHC could potentially improve estimates of risk of dying from prostate cancer based only on Gleason score and clinical stage.

The presence of prostate cancer at biopsy is predicted by a number of genetic variants

Several single nucleotide polymorphisms (SNPs) have been associated with an elevated risk of prostate cancer risk. It is not established if they are useful in predicting the presence of prostate cancer at biopsy or if they can be used to define a low-risk group of men. In this study, 4,548 men underwent a prostate biopsy because of an elevated prostate specific antigen (PSA; ≥4 ng/mL) or an abnormal digital rectal examination (DRE). All men were genotyped for 11 selected SNPs. The effect of each SNP, alone and in combination, on prostate cancer prevalence was studied. Of 4,548 men: 1,834 (40.3%) were found to have cancer. A positive association with prostate cancer was seen for 5 of 11 SNPs studied (rs1800629, rs1859962, rs1447295, rs4430796, rs11228565). The cancer detection rate rose with the number of SNP risk alleles from 29% for men with no variant to 63% for men who carried seven or more risk alleles (OR = 4.2; p = 0.002). The SNP data did not improve the predictive power of clinical factors (age, PSA and DRE) for detecting prostate cancer (AUC: 0.726 vs. 0.735; p = 0.4). We were unable to define a group of men with a sufficiently low prevalence of prostate cancer that a biopsy might have been avoided. In conclusion, our data do not support the routine use of SNP polymorphisms as an adjunct test to be used on the context of prostate biopsy for Polish men with an abnormal screening test.

International multicentre study examining selection criteria for active surveillance in men undergoing radical prostatectomy

Background:

The controversies concerning possible overtreatment of prostate cancer, highlighted by debate over PSA screening, have highlighted active surveillance (AS) as an alternative management option for appropriate men. Regional differences in the underlying prevalence of PSA testing may alter the pre-test probability for high-risk disease, which can potentially interfere with the performance of selection criteria for AS. In a multicentre study from three different countries, we examine men who were initially suitable for AS according to the Toronto and Prostate Cancer Research International: Active Surveillance (PRIAS) criteria, that underwent radical prostatectomy (RP) in regards to:1.the proportion of pathological reclassification(Gleason score ⩾7, ⩾pT3 disease),2.predictors of high-risk disease,3.create a predictive model to assist with selection of men suitable for AS.

Methods:

From three centres in the United Kingdom, Canada and Australia, data on men who underwent RP were retrospectively reviewed (n=2329). Multivariable logistic regression was performed to identify predictors of high-risk disease. A nomogram was generated by logistic regression analysis, and performance characterised by receiver operating characteristic curves.

Results:

For men suitable for AS according to the Toronto (n=800) and PRIAS (410) criteria, the rates for upgrading were 50.6, 42.7%, and upstaging 17.6, 12.4%, respectively. Significant predictors of high-risk disease were:•Toronto criteria: increasing age, cT2 disease, centre of diagnosis and number of positive cores.•PRIAS criteria: increasing PSA and cT2 disease.Cambridge had a high pT3a rate (26 vs 12%). To assist selection of men in the United Kingdom for AS, from the Cambridge data, we generated a nomogram predicting high-risk features in patients who meet the Toronto criteria (AUC of 0.72).

Conclusion:

The proportion of pathological reclassification in our cohort was higher than previously reported. Care must be used when applying the AS criteria generated from one population to another. With more stringent selection criteria, there is less reclassification but also fewer men who may benefit from AS.