Imaging tumor lactate is feasible for identifying intermediate-risk prostate cancer patients with postsurgical biochemical recurrence.

While radical prostatectomy remains the mainstay of prostate cancer (PCa) treatment, 20 to 40% of patients develop postsurgical biochemical recurrence (BCR). A particularly challenging clinical cohort includes patients with intermediate-risk disease whose risk stratification would benefit from advanced approaches that complement standard-of-care diagnostic tools. Here, we show that imaging tumor lactate using hyperpolarized 13C MRI and spatial metabolomics identifies BCR-positive patients in two prospective intermediate-risk surgical cohorts. Supported by spatially resolved tissue analysis of established glycolytic biomarkers, this study provides the rationale for multicenter trials of tumor metabolic imaging as an auxiliary tool to support PCa treatment decision-making.

Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance.

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Urinary symptoms and prostate cancer-the misconception that may be preventing earlier presentation and better survival outcomes.

BACKGROUND: Prostate cancer is an epidemic of the modern age, and despite efforts to improve awareness, it remains the case that mortality has hardly altered over the decades, driven largely by late presentation. There is a strong public perception that male urinary symptoms is one of the key indicators of prostate cancer, and this continues to be part of messaging from national guidelines and media health campaigns. This narrative, however, is not based on evidence and may be seriously hampering efforts to encourage early presentation. DISCUSSION: Anatomically, prostate cancer most often arises in the peripheral zone, while urinary symptoms result from compression of the urethra by prostatic enlargement more centrally. Biopsy studies show that mean prostate volume is actually lower in men found to have (early) prostate cancer compared to those with benign biopsies. This inverse relationship between prostate size and the probability of cancer is so strong that PSA density (PSA corrected for prostate volume) is known to be significantly more accurate in predicting a positive biopsy than PSA alone. Thus, this disconnect between scientific evidence and the current perception is very striking. There is also evidence that using symptoms for investigating possible cancer may lead to higher proportions of men presenting with locally advanced or metastatic disease compared to PSA testing or screening programmes. Concerns about overwhelming health care services if men are encouraged to get tested without symptoms may also be overstated, with recent newer approaches to reduce over-investigation and treatment. In this article, we explore the link between urinary symptoms and prostate cancer and propose that public and professional messaging needs to change. CONCLUSION: If rates of earlier diagnosis are to improve, we call for strong clear messaging that prostate cancer is a silent disease especially in the curable stages and men should come forward for testing regardless of whether or not they have symptoms. This should be done in parallel with other ongoing efforts to raise awareness including targeting men at highest risk due to racial ancestry or family history. While the current resurgence in interest and debate about prostate cancer screening is timely, change of this message by guideline bodies, charities and the media can be a first simple step to improving earlier presentation and hence cures rates.

Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population.

INTRODUCTION: Pre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population. PATIENT AND METHODS: Data from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm. RESULTS: Data from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years' respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years' time frame). CONCLUSION: MRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.

OBJECTIVES: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Discovery of PTN as a serum-based biomarker of pro-metastatic prostate cancer.

Distinguishing clinically significant from indolent prostate cancer (PC) is a major clinical challenge. We utilised targeted protein biomarker discovery approach to identify biomarkers specific for pro-metastatic PC. Serum samples from the cancer-free group; Cambridge Prognostic Group 1 (CPG1, low risk); CPG5 (high risk) and metastatic disease were analysed using Olink Proteomics panels. Tissue validation was performed by immunohistochemistry in a radical prostatectomy cohort (n = 234). We discovered that nine proteins (pleiotrophin (PTN), MK, PVRL4, EPHA2, TFPI-2, hK11, SYND1, ANGPT2, and hK14) were elevated in metastatic PC patients when compared to other groups. PTN levels were increased in serum from men with CPG5 compared to benign and CPG1. High tissue PTN level was an independent predictor of biochemical recurrence and metastatic progression in low- and intermediate-grade disease. These findings suggest that PTN may represent a novel biomarker for the presence of poor prognosis local disease with the potential to metastasise warranting further investigation.

MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance.

OBJECTIVES: To assess the predictive value and correlation to pathological progression of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system in the follow-up of prostate cancer (PCa) patients on active surveillance (AS). METHODS: A total of 295 men enrolled on an AS programme between 2011 and 2018 were included. Baseline multiparametric magnetic resonance imaging (mpMRI) was performed at AS entry to guide biopsy. The follow-up mpMRI studies were prospectively reported by two sub-specialist uroradiologists with 10 years and 13 years of experience. PRECISE scores were dichotomized at the cut-off value of 4, and the sensitivity, specificity, positive predictive value and negative predictive value were calculated. Diagnostic performance was further quantified by using area under the receiver operating curve (AUC) which was based on the results of targeted MRI-US fusion biopsy. Univariate analysis using Cox regression was performed to assess which baseline clinical and mpMRI parameters were related to disease progression on AS. RESULTS: Progression rate of the cohort was 13.9% (41/295) over a median follow-up of 52 months. With a cut-off value of category ≥ 4, the PRECISE scoring system showed sensitivity, specificity, PPV and NPV for predicting progression on AS of 0.76, 0.89, 0.52 and 0.96, respectively. The AUC was 0.82 (95% CI = 0.74-0.90). Prostate-specific antigen density (PSA-D), Likert lesion score and index lesion size were the only significant baseline predictors of progression (each p < 0.05). CONCLUSION: The PRECISE scoring system showed good overall performance, and the high NPV may help limit the number of follow-up biopsies required in patients on AS. KEY POINTS: • PRECISE scores 1-3 have high NPV which could reduce the need for re-biopsy during active surveillance. • PRECISE scores 4-5 have moderate PPV and should trigger either close monitoring or re-biopsy. • Three baseline predictors (PSA density, lesion size and Likert score) have a significant impact on the progression-free survival (PFS) time.

Rationalising the use of investigation for urinary tract infections: Analysis of 700 patients and proposal for a diagnostic algorithm.

AIMS: To evaluate the diagnostic yield of investigations performed on patients with a history of urinary tract infections (UTI). METHODS: A retrospective review was conducted on patients who underwent cystoscopy and imaging for a history of UTI between 2014 and 2019 in a single UK teaching hospital. Data were collected on demographics, cystoscopy and radiological findings requiring further management. The cohort was stratified by age, gender and a confirmed history of recurrent UTI (rUTI). The subsequent algorithm was re-tested in a second cohort to validate its use. RESULTS: Seven hundred patients were included in the primary analysis-427 female and 273 males. Three hundred and thirty-one met the criteria of rUTI. The median age was 64 years (18-97). Imaging abnormalities were equally frequent in men 6.3% (15/241) and women 8% (30/380) and the majority noted in patients aged ≥55 years, 30/45 (66.7%). Amongst those who did not meet the definition of rUTI, abnormal imaging was identified in 5%-7% regardless of age group and gender. Cystoscopy abnormalities (n = 24) were twice more likely in males, 5.5%(15/273) than females, 2%(9/427). About 88%(21/24) were identified in patients ≥55 years. There were no positive findings in women <55 years. Applying baseline imaging but confining cystoscopy to those aged ≥55 years and men with a confirmed history of rUTI would have saved 44% of procedures, missed no abnormalities with an overall diagnosis detection rate of 9.8% (69/700). This algorithm was validated in a separate cohort of 63 patients; applying it would have saved 46% (29/63) of cystoscopies with a positive diagnostic rate of 9.5% and no missed findings. CONCLUSION: To our knowledge this is one of the largest studies reporting the outcomes of investigations for UTI and rUTI. Our result suggests that imaging is a useful baseline assessment, but cystoscopy should be limited to specific subgroups. We propose and validate a simple decision algorithm to manage investigations for referrals for UTI in secondary care.

Clinical utility and cost modelling of the phi test to triage referrals into image-based diagnostic services for suspected prostate cancer: the PRIM (Phi to RefIne Mri) study.

BACKGROUND: The clinical pathway to detect and diagnose prostate cancer has been revolutionised by the use of multiparametric MRI (mpMRI pre-biopsy). mpMRI however remains a resource-intensive test and is highly operator dependent with variable effectiveness with regard to its negative predictive value. Here we tested the use of the phi assay in standard clinical practice to pre-select men at the highest risk of harbouring significant cancer and hence refine the use of mpMRI and biopsies. METHODS: A prospective five-centre study recruited men being investigated through an mpMRI-based prostate cancer diagnostic pathway. Test statistics for PSA, PSA density (PSAd) and phi were assessed for detecting significant cancers using 2 definitions: ≥ Grade Group (GG2) and ≥ Cambridge Prognostic Groups (CPG) 3. Cost modelling and decision curve analysis (DCA) was simultaneously performed. RESULTS: A total of 545 men were recruited and studied with a median age, PSA and phi of 66 years, 8.0 ng/ml and 44 respectively. Overall, ≥ GG2 and ≥ CPG3 cancer detection rates were 64% (349/545), 47% (256/545) and 32% (174/545) respectively. There was no difference across centres for patient demographics or cancer detection rates. The overall area under the curve (AUC) for predicting ≥ GG2 cancers was 0.70 for PSA and 0.82 for phi. AUCs for ≥ CPG3 cancers were 0.81 and 0.87 for PSA and phi respectively. AUC values for phi did not differ between centres suggesting reliability of the test in different diagnostic settings. Pre-referral phi cut-offs between 20 and 30 had NPVs of 0.85-0.90 for ≥ GG2 cancers and 0.94-1.0 for ≥ CPG3 cancers. A strategy of mpMRI in all and biopsy only positive lesions reduced unnecessary biopsies by 35% but missed 9% of ≥ GG2 and 5% of ≥ CPG3 cancers. Using PH ≥ 30 to rule out referrals missed 8% and 5% of ≥ GG2 and ≥ CPG3 cancers (and reduced unnecessary biopsies by 40%). This was achieved however with 25% fewer mpMRI. Pathways incorporating PSAd missed fewer cancers but necessitated more unnecessary biopsies. The phi strategy had the lowest mean costs with DCA demonstrating net clinical benefit over a range of thresholds. CONCLUSION: phi as a triaging test may be an effective way to reduce mpMRI and biopsies without compromising detection of significant prostate cancers.

Removing rician bias in diffusional kurtosis of the prostate using real-data reconstruction.

PURPOSE: To compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase-corrected real data with those generated using magnitude data with and without noise compensation (NC). METHODS: Diffusion-weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b-values (0-1500 s/mm2 ), each acquired with 6 signal averages along 3 diffusion directions, with noise-only images acquired to allow NC. In addition to conventional magnitude averaging, phase-corrected real data were averaged in an attempt to reduce rician noise-bias, with a range of phase-correction low-pass filter (LPF) sizes (8-128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase-corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ). RESULTS: Simulations indicated LPF size can strongly affect K metrics, where 64-pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64-LPF real-data K were lower (P < 0.0001) by 4/10/7%, respectively. CONCLUSION: Compared with magnitude data with NC, phase-corrected real data can produce similar K, although the choice of phase-correction LPF should be chosen carefully.

Impact of hospital nephrectomy volume on intermediate- to long-term survival in renal cell carcinoma.

OBJECTIVE: To evaluate the relationship between hospital volume and intermediate- and long-term patient survival for patients undergoing nephrectomy for renal cell carcinoma (RCC). PATIENTS AND METHODS: Adult patients with RCC treated with nephrectomy between 2000 and 2010 were identified from the English Hospital Episode Statistics database and National Cancer Data Repository. Patients with nodal or metastatic disease were excluded. Hospitals were categorised into low- (LV; <20 cases/year), medium- (20-39 cases/year) and high-volume (HV; ≥40 cases/year), based on annual cases of RCC nephrectomy. Multivariable Cox regression analyses were used to calculate hazard ratios (HRs) for all-cause mortality by hospital volume, adjusting for patient, tumour and surgical characteristics. We assessed conditional survival over three follow-up periods: short (30 days to 1 year), intermediate (1-3 years) and long (3-5 years). We additionally explored whether associations between volume and outcomes varied by tumour stage. RESULTS: A total of 12 912 patients were included. Patients in HV hospitals had a 34% reduction in mortality risks up to 1 year compared to those in LV hospitals (HR 0.66, 95% confidence interval 0.53-0.83; P < 0.01). Assuming causality, treatment in HV hospitals was associated with one fewer death in every 71 patients treated. Benefit of nephrectomy centralisation did not change with higher T stage (P = 0.17). No significant association between hospital volume and survival was observed beyond the first year. CONCLUSIONS: Nephrectomy for RCC in HV hospitals was associated with improved survival for up to 1 year after treatment. Our results contribute new insights regarding the value of nephrectomy centralisation.

Diagnostic accuracy of biparametric versus multiparametric prostate MRI: assessment of contrast benefit in clinical practice.

PURPOSE: To assess the added value of dynamic contrast-enhanced (DCE) in prostate MR in clinical practice. METHODS: Two hundred sixty-four patients underwent prostate MRI, with T2 and DWI sequences initially interpreted, prior to full multiparametric magnetic resonance imaging (mpMRI) interpretation using a Likert 1-5 scale. A prospective opinion was given on likely benefit of contrast prior to review of the DCE sequence, and retrospectively following full mpMRI review. The final histology result following targeted and/or systematic biopsy of the prostate was used for outcome purposes. RESULTS: Biparametric magnetic resonance imaging (bpMRI) and mpMRI were assigned the same score in 86% of cases; when dichotomising to a negative or positive MRI (Likert score ≥ 3), concordance increased to 92.8%. At Likert score ≥ 3 bpMRI detected 89.9% of all cancers and 93.5% clinically significant prostate cancers (csPCa) and mpMRI 90.7% and 94.6%, respectively. mpMRI had fewer false positives than bpMRI (11.4% vs 18.9%) and a lower Likert 3 rate (8.3% vs 17%), conferring higher specificity (74% vs 67%), but similar sensitivity (95% versus 94%) and ROC-AUC (90% vs 89%). At a positive MRI threshold of Likert ≥ 4, mpMRI had a higher sensitivity than bpMRI (89% versus 80%) and detected more csPCa (89.2% versus 79.6%). DCE was prospectively considered of potential benefit in 27.3%, but readers would only recall 11% of patients for DCE sequences, mainly to assess score 3 peripheral zone lesions. Following full mpMRI review, DCE was considered helpful in 28.4% of cases; in 23/75 (30.6%) of these cases this only became apparent after reviewing the sequence, reasons included increased confidence, presence of "safety-net" lesions or inflammatory lesions. CONCLUSION: BpMRI has equivalent cancer detection rates to mpMRI; however, mpMRI had fewer Likert 3 call rates and increased specificity and was subjectively considered of benefit by readers in 28.4% of cases. KEY POINTS: • bpMRI has similar cancer detection rates to the full mpMRI protocol at a positive MRI threshold of Likert 3. • mpMRI had fewer intermediate category 3 calls (8.3%) than bpMRI (17%) and fewer false positives than bpMRI (11.4% vs 18.9%), conferring higher specificity (74% vs 67%). • Readers considered DCE beneficial in 28.4% of cases, but in a relatively high number (30.6%) this only became apparent after reviewing the sequence.

Appraising the relevance of DNA copy number loss and gain in prostate cancer using whole genome DNA sequence data.

A variety of models have been proposed to explain regions of recurrent somatic copy number alteration (SCNA) in human cancer. Our study employs Whole Genome DNA Sequence (WGS) data from tumor samples (n = 103) to comprehensively assess the role of the Knudson two hit genetic model in SCNA generation in prostate cancer. 64 recurrent regions of loss and gain were detected, of which 28 were novel, including regions of loss with more than 15% frequency at Chr4p15.2-p15.1 (15.53%), Chr6q27 (16.50%) and Chr18q12.3 (17.48%). Comprehensive mutation screens of genes, lincRNA encoding sequences, control regions and conserved domains within SCNAs demonstrated that a two-hit genetic model was supported in only a minor proportion of recurrent SCNA losses examined (15/40). We found that recurrent breakpoints and regions of inversion often occur within Knudson model SCNAs, leading to the identification of ZNF292 as a target gene for the deletion at 6q14.3-q15 and NKX3.1 as a two-hit target at 8p21.3-p21.2. The importance of alterations of lincRNA sequences was illustrated by the identification of a novel mutational hotspot at the KCCAT42, FENDRR, CAT1886 and STCAT2 loci at the 16q23.1-q24.3 loss. Our data confirm that the burden of SCNAs is predictive of biochemical recurrence, define nine individual regions that are associated with relapse, and highlight the possible importance of ion channel and G-protein coupled-receptor (GPCR) pathways in cancer development. We concluded that a two-hit genetic model accounts for about one third of SCNA indicating that mechanisms, such haploinsufficiency and epigenetic inactivation, account for the remaining SCNA losses.

Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model.

BACKGROUND: Prognostic stratification is the cornerstone of management in nonmetastatic prostate cancer (PCa). However, existing prognostic models are inadequate-often using treatment outcomes rather than survival, stratifying by broad heterogeneous groups and using heavily treated cohorts. To address this unmet need, we developed an individualised prognostic model that contextualises PCa-specific mortality (PCSM) against other cause mortality, and estimates the impact of treatment on survival. METHODS AND FINDINGS: Using records from the United Kingdom National Cancer Registration and Analysis Service (NCRAS), data were collated for 10,089 men diagnosed with nonmetastatic PCa between 2000 and 2010 in Eastern England. Median follow-up was 9.8 years with 3,829 deaths (1,202 PCa specific). Totals of 19.8%, 14.1%, 34.6%, and 31.5% of men underwent conservative management, prostatectomy, radiotherapy (RT), and androgen deprivation monotherapy, respectively. A total of 2,546 men diagnosed in Singapore over a similar time period represented an external validation cohort. Data were randomly split 70:30 into model development and validation cohorts. Fifteen-year PCSM and non-PCa mortality (NPCM) were explored using separate multivariable Cox models within a competing risks framework. Fractional polynomials (FPs) were utilised to fit continuous variables and baseline hazards. Model accuracy was assessed by discrimination and calibration using the Harrell C-index and chi-squared goodness of fit, respectively, within both validation cohorts. A multivariable model estimating individualised 10- and 15-year survival outcomes was constructed combining age, prostate-specific antigen (PSA), histological grade, biopsy core involvement, stage, and primary treatment, which were each independent prognostic factors for PCSM, and age and comorbidity, which were prognostic for NPCM. The model demonstrated good discrimination, with a C-index of 0.84 (95% CI: 0.82-0.86) and 0.84 (95% CI: 0.80-0.87) for 15-year PCSM in the UK and Singapore validation cohorts, respectively, comparing favourably to international risk-stratification criteria. Discrimination was maintained for overall mortality, with C-index 0.77 (95% CI: 0.75-0.78) and 0.76 (95% CI: 0.73-0.78). The model was well calibrated with no significant difference between predicted and observed PCa-specific (p = 0.19) or overall deaths (p = 0.43) in the UK cohort. Key study limitations were a relatively small external validation cohort, an inability to account for delayed changes to treatment beyond 12 months, and an absence of tumour-stage subclassifications. CONCLUSIONS: 'PREDICT Prostate' is an individualised multivariable PCa prognostic model built from baseline diagnostic information and the first to our knowledge that models potential treatment benefits on overall survival. Prognostic power is high despite using only routinely collected clinicopathological information.

Understanding of prognosis in non-metastatic prostate cancer: a randomised comparative study of clinician estimates measured against the PREDICT prostate prognostic model.

PREDICT Prostate is an individualised prognostic model that provides long-term survival estimates for men diagnosed with non-metastatic prostate cancer ( www.prostate.predict.nhs.uk ). In this study clinician estimates of survival were compared against model predictions and its potential value as a clinical tool was assessed. Prostate cancer (PCa) specialists were invited to participate in the study. 190 clinicians (63% urologists, 17% oncologists, 20% other) were randomised into two groups and shown 12 clinical vignettes through an online portal. Each group viewed opposing vignettes with clinical information alone, or alongside PREDICT Prostate estimates. 15-year clinician survival estimates were compared against model predictions and reported treatment recommendations with and without seeing PREDICT estimates were compared. 155 respondents (81.6%) reported counselling new PCa patients at least weekly. Clinician estimates of PCa-specific mortality exceeded PREDICT estimates in 10/12 vignettes. Their estimates for treatment survival benefit at 15 years were over-optimistic in every vignette, with mean clinician estimates more than 5-fold higher than PREDICT Prostate estimates. Concomitantly seeing PREDICT Prostate estimates led to significantly lower reported likelihoods of recommending radical treatment in 7/12 (58%) vignettes, particularly in older patients. These data suggest clinicians overestimate cancer-related mortality and radical treatment benefit. Using an individualised prognostic tool may help reduce overtreatment.

Using prognosis to guide inclusion criteria, define standardised endpoints and stratify follow-up in active surveillance for prostate cancer.

OBJECTIVES: To test whether using disease prognosis can inform a rational approach to active surveillance (AS) for early prostate cancer. PATIENTS AND METHODS: We previously developed the Cambridge Prognostics Groups (CPG) classification, a five-tiered model that uses prostate-specific antigen (PSA), Grade Group and Stage to predict cancer survival outcomes. We applied the CPG model to a UK and a Swedish prostate cancer cohort to test differences in prostate cancer mortality (PCM) in men managed conservatively or by upfront treatment in CPG2 and 3 (which subdivides the intermediate-risk classification) vs CPG1 (low-risk). We then applied the CPG model to a contemporary UK AS cohort, which was optimally characterised at baseline for disease burden, to identify predictors of true prognostic progression. Results were re-tested in an external AS cohort from Spain. RESULTS: In a UK cohort (n = 3659) the 10-year PCM was 2.3% in CPG1, 1.5%/3.5% in treated/untreated CPG2, and 1.9%/8.6% in treated/untreated CPG3. In the Swedish cohort (n = 27 942) the10-year PCM was 1.0% in CPG1, 2.2%/2.7% in treated/untreated CPG2, and 6.1%/12.5% in treated/untreated CPG3. We then tested using progression to CPG3 as a hard endpoint in a modern AS cohort (n = 133). During follow-up (median 3.5 years) only 6% (eight of 133) progressed to CPG3. Predictors of progression were a PSA density ≥0.15 ng/mL/mL and CPG2 at diagnosis. Progression occurred in 1%, 8% and 21% of men with neither factor, only one, or both, respectively. In an independent Spanish AS cohort (n = 143) the corresponding rates were 3%, 10% and 14%, respectively. CONCLUSION: Using disease prognosis allows a rational approach to inclusion criteria, discontinuation triggers and risk-stratified management in AS.

Time trends in service provision and survival outcomes for patients with renal cancer treated by nephrectomy in England 2000-2010.

OBJECTIVE: To describe the temporal trends in nephrectomy practice and outcomes for English patients with renal cell carcinoma (RCC). PATIENTS AND METHODS: Adult RCC nephrectomy patients treated between 2000 and 2010 were identified in the National Cancer Data Repository and Hospital Episode Statistics, and followed-up until date of death or 31 December 2015 (n = 30 763). We estimated the annual frequency for each nephrectomy type, the hospital and surgeon numbers and their case volumes. We analysed short-term surgical outcomes, as well as 1- and 5-year relative survivals. RESULTS: Annual RCC nephrectomy number increased by 66% during the study period. Hospital number decreased by 24%, whilst the median annual hospital volume increased from 10 to 23 (P < 0.01). Surgeon number increased by 27% (P < 0.01), doubling the median consultant number per hospital. The proportion of minimally invasive surgery (MIS) nephrectomies rose from 1% to 46%, whilst the proportion of nephron-sparing surgeries (NSS) increased from 5% to 16%, with 29% of all T1 disease treated with partial nephrectomy in 2010 (P < 0.01). The 30-day mortality rate halved from 2.4% to 1.1% and 90-day mortality decreased from 4.9% to 2.6% (P < 0.01). The 1-year relative survival rate increased from 86.9% to 93.4%, whilst the 5-year relative survival rate rose from 68.2% to 81.2% (P < 0.01). Improvements were most notable in patients aged ≥65 years and those with T3 and T4 disease. CONCLUSIONS: Surgical RCC management has changed considerably with nephrectomy centralisation and increased NSS and MIS. In parallel, we observed significant improvements in short- and long-term survival particularly for elderly patients and those with locally advanced disease.

Progression and treatment rates using an active surveillance protocol incorporating image-guided baseline biopsies and multiparametric magnetic resonance imaging monitoring for men with favourable-risk prostate cancer.

OBJECTIVE: To assess early outcomes since the introduction of an active surveillance (AS) protocol incorporating multiparametric magnetic resonance imaging (mpMRI)-guided baseline biopsies and image-based surveillance. PATIENTS AND METHODS: A new AS protocol mandating image-guided baseline biopsies, annual mpMRI and 3-monthly prostate-specific antigen (PSA) testing, but which retained protocol re-biopsies, was tested. Pathological progression, treatment conversion and triggers for non-protocol biopsy were recorded prospectively. RESULTS: Data from 157 men enrolled in the AS protocol (median age 64 years, PSA 6.8 ng/mL, follow-up 39 months) were interrogated. A total of 12 men (7.6%) left the AS programme by choice. Of the 145 men who remained, 104 had re-biopsies either triggered by a rise in PSA level, change in mpMRI findings or by protocol. Overall, 23 men (15.9%) experienced disease progression; pathological changes were observed in 20 men and changes in imaging results were observed in three men. Of these 23 men, 17 switched to treatment, giving a conversion rate of 11.7% (<4% per year). Of the 20 men with pathological progression, this was detected in four of them after a PSA increase triggered a re-biopsy, while in 10 men progression was detected after an mpMRI change. Progression was detected in six men, however, solely after a protocol re-biopsy without prior PSA or mpMRI changes. Using PSA and mpMRI changes alone to detect progression was found to have a sensitivity and specificity of 70.0% and 81.7%, respectively. CONCLUSION: Our AS protocol, with thorough baseline assessment and imaging-based surveillance, showed low rates of progression and treatment conversion. Changes in mpMRI findings were the principle trigger for detecting progression by imaging alone or pathologically; however, per protocol re-biopsy still detected a significant number of pathological progressions without mpMRI or PSA changes.

Diagnostic evaluation of magnetization transfer and diffusion kurtosis imaging for prostate cancer detection in a re-biopsy population.

OBJECTIVE: To evaluate diffusion kurtosis imaging (DKI) and magnetisation transfer imaging (MTI) compared to standard MRI for prostate cancer assessment in a re-biopsy population. METHODS: Thirty-patients were imaged at 3 T including DKI (Kapp and Dapp) with b-values 150/450/800/1150/1500 s/mm2 and MTI performed with and without MT saturation. Patients underwent transperineal biopsy based on prospectively defined MRI targets. Receiver-operating characteristic (ROC) analyses assessed the parameters and Wilcoxon-signed ranked test assessed relationships between metrics. RESULTS: Twenty patients had ≥ 1 core positive for cancer in a total of 26 MRI targets (Gleason 3+3 in 8, 3+4 in 12, ≥ 4+3 in 6): 13 peripheral (PZ) and 13 transition zone (TZ). The apparent diffusion coefficient (ADC) and Dapp were significantly lower and the Kapp and MT ratio (MTR) significantly higher in tumour versus benign tissue (all p ≤ 0.005); ROC values 0.767-1.000. Normal TZ had: lower ADC and Dapp and higher Kapp and MTR compared to normal PZ. MTR showed a moderate correlation to Kapp (r = 0.570) and Dapp (r = -0.537) in normal tissue but a poor correlation in tumours. No parameter separated low-grade (Gleason 3+3) from high-grade (≥ 3+4) disease for either PZ (p = 0.414-0.825) or TZ (p = 0.148-0.825). CONCLUSION: ADC, Dapp, Kapp and MTR all distinguished benign tissue from tumour, but none reliably differentiated low- from high-grade disease. KEY POINTS: • MTR was significantly higher in PZ and TZ tumours versus normal tissue • K app was significantly lower and D app higher for PZ and TZ tumours • There was no incremental value for DKI/MTI over mono-exponential ADC parameters • No parameter could consistently differentiate low-grade (Gleason 3+3) from high-grade (≥ 3+4) disease • Divergent MTR/DKI values in TZ tumours suggests they offer different functional information.