Genetic risk and likelihood of prostate cancer detection on first biopsy by ancestry

Despite differences in prostate cancer risk across ancestry groups, relative performance of prostate cancer genetic risks scores (GRS) for positive biopsy prediction in different ancestry groups is unknown. This cross-sectional retrospective analysis examines the association between a polygenic hazard score (PHS290) and risk of prostate cancer diagnosis upon first biopsy in male veterans using 2-sided tests. Our analysis included 36 717 veterans (10 297 of African ancestry). Unadjusted rates of positive first prostate biopsy increased with higher genetic risk (low risk: 34%, high risk: 58%; P < .001). Among men of African ancestry, higher genetic risk was associated with increased prostate cancer detection on first biopsy (odds ratio = 2.18, 95% confidence interval = 1.93 to 2.47), but the effect was stronger among men of European descent (odds ratio = 3.89, 95% confidence interval = 3.62 to 4.18). These findings suggest that incorporating genetic risk into prediction models could better personalize biopsy decisions, although further study is needed to achieve equitable genetic risk stratification among ancestry groups.

Prostate-Specific Antigen Screening and 15-Year Prostate Cancer Mortality: A Secondary Analysis of the CAP Randomized Clinical Trial

Importance

The Cluster Randomized Trial of PSA Testing for Prostate Cancer (CAP) reported no effect of prostate-specific antigen (PSA) screening on prostate cancer mortality at a median 10-year follow-up (primary outcome), but the long-term effects of PSA screening on prostate cancer mortality remain unclear.

Objective

To evaluate the effect of a single invitation for PSA screening on prostate cancer-specific mortality at a median 15-year follow-up compared with no invitation for screening.

Design, Setting, and Participants

This secondary analysis of the CAP randomized clinical trial included men aged 50 to 69 years identified at 573 primary care practices in England and Wales. Primary care practices were randomized between September 25, 2001, and August 24, 2007, and men were enrolled between January 8, 2002, and January 20, 2009. Follow-up was completed on March 31, 2021.

Intervention

Men received a single invitation for a PSA screening test with subsequent diagnostic tests if the PSA level was 3.0 ng/mL or higher. The control group received standard practice (no invitation).

Main Outcomes and Measures

The primary outcome was reported previously. Of 8 prespecified secondary outcomes, results of 4 were reported previously. The 4 remaining prespecified secondary outcomes at 15-year follow-up were prostate cancer-specific mortality, all-cause mortality, and prostate cancer stage and Gleason grade at diagnosis.

Results

Of 415 357 eligible men (mean [SD] age, 59.0 [5.6] years), 98% were included in these analyses. Overall, 12 013 and 12 958 men with a prostate cancer diagnosis were in the intervention and control groups, respectively (15-year cumulative risk, 7.08% [95% CI, 6.95%-7.21%] and 6.94% [95% CI, 6.82%-7.06%], respectively). At a median 15-year follow-up, 1199 men in the intervention group (0.69% [95% CI, 0.65%-0.73%]) and 1451 men in the control group (0.78% [95% CI, 0.73%-0.82%]) died of prostate cancer (rate ratio [RR], 0.92 [95% CI, 0.85-0.99]; P = .03). Compared with the control, the PSA screening intervention increased detection of low-grade (Gleason score [GS] ≤6: 2.2% vs 1.6%; P < .001) and localized (T1/T2: 3.6% vs 3.1%; P < .001) disease but not intermediate (GS of 7), high-grade (GS ≥8), locally advanced (T3), or distally advanced (T4/N1/M1) tumors. There were 45 084 all-cause deaths in the intervention group (23.2% [95% CI, 23.0%-23.4%]) and 50 336 deaths in the control group (23.3% [95% CI, 23.1%-23.5%]) (RR, 0.97 [95% CI, 0.94-1.01]; P = .11). Eight of the prostate cancer deaths in the intervention group (0.7%) and 7 deaths in the control group (0.5%) were related to a diagnostic biopsy or prostate cancer treatment.

Conclusions and Relevance

In this secondary analysis of a randomized clinical trial, a single invitation for PSA screening compared with standard practice without routine screening reduced prostate cancer deaths at a median follow-up of 15 years. However, the absolute reduction in deaths was small.

Trial Registration

isrctn.org Identifier: ISRCTN92187251.

Prostate MRI Was Negative-What's Next?

The primary benefit of prostate MRI in the modern diagnostic pathway for prostate cancer is that many men with elevated serum PSA can safely avoid an immediate biopsy if the MRI is nonsuspicious. It is less clear, though, how these patients should be followed thereafter. Are they to be followed the same as the general population, or do they warrant more attention because of the risk of a cancer missed on MRI? In this issue, Pylväläinen and colleagues report on incidence of clinically significant prostate cancer (csPCa) and clinically insignificant PCa (ciPCa) among patients who were referred for prostate MRI for clinical suspicion of csPCa in Helsinki but had a nonsuspicious MRI (nMRI). Compared with the general population in Finland, patients who had nMRI were approximately 3.4 times more likely to be diagnosed with csPCa and 8.2 times more likely to be diagnosed with ciPCa. Balancing the competing risks of a missed csPCa versus overdiagnosis in patients after nMRI requires integration of MRI and other risk factors, especially age and PSA density. This integration may be facilitated by multivariable models and quantitative pathology and imaging. See related article by Pylväläinen et al., p. 749.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

Adrenal-Permissive Germline HSD3B1 Allele and Prostate Cancer Outcomes

Importance

The adrenal androgen-metabolizing 3β-hydroxysteroid dehydrogenase-1 enzyme, encoded by the HSD3B1 gene, catalyzes the rate-limiting step necessary for synthesizing nontesticular testosterone and dihydrotestosterone production. The common adrenal-permissive HSD3B1(1245C) allele is responsible for encoding the 3β-HSD1 protein with decreased susceptibility to degradation resulting in higher extragonadal androgen synthesis. Retrospective studies have suggested an association of the HSD3B1 adrenal-permissive homozygous genotype with androgen deprivation therapy resistance in prostate cancer.

Objective

To evaluate differences in mortality outcomes by HSD3B1 genetic status among men with prostate cancer.

Design, Setting, and Participants

This cohort study of patients with prostate cancer who were enrolled in the Million Veteran Program within the Veterans Health Administration (VHA) system between 2011 and 2023 collected genotyping and phenotyping information.

Exposure

HSD3B1 genotype status was categorized as AA (homozygous adrenal-restrictive), AC (heterozygous adrenal-restrictive), or CC (homozygous adrenal-permissive).

Main Outcomes and Measures

The primary outcome of this study was prostate cancer-specific mortality (PCSM), defined as the time from diagnosis to death from prostate cancer, censored at the date of last VHA follow-up. Secondary outcomes included incidence of metastases and PCSM in predefined subgroups.

Results

Of the 5287 participants (median [IQR] age, 69 [64-74] years), 402 (7.6%) had the CC genotype, 1970 (37.3%) had the AC genotype, and 2915 (55.1%) had the AA genotype. Overall, the primary cause of death for 91 patients (1.7%) was prostate cancer. Cumulative incidence of PCSM at 5 years after prostate cancer diagnosis was higher among men with the CC genotype (4.0%; 95% CI, 1.7%-6.2%) compared with the AC genotype (2.1%; 95% CI, 1.3%-2.8%) and AA genotype (1.9%; 95% CI, 1.3%-2.4%) (P = .02). In the 619 patients who developed metastatic disease at any time, the cumulative incidence of PCSM at 5 years was higher among patients with the CC genotype (36.0%; 95% CI, 16.7%-50.8%) compared with the AC genotype (17.9%; 95% CI, 10.5%-24.7%) and AA genotype (18.5%; 95% CI, 12.0%-24.6%) (P = .01).

Conclusions and Relevance

In this cohort study of US veterans undergoing treatment for prostate cancer at the VHA, the HSD3B1 CC genotype was associated with inferior outcomes. The HSD3B1 biomarker may help identify patients who may benefit from therapeutic targeting of 3β-hydroxysteroid dehydrogenase-1 and the androgen-signaling axis.

Focal radiotherapy boost to MR-visible tumor for prostate cancer: a systematic review

PURPOSE

The FLAME trial provides strong evidence that MR-guided external beam radiation therapy (EBRT) focal boost for localized prostate cancer increases biochemical disease-free survival (bDFS) without increasing toxicity. Yet, there are many barriers to implementation of focal boost. Our objectives are to systemically review clinical outcomes for MR-guided EBRT focal boost and to consider approaches to increase implementation of this technique.

METHODS

We conducted literature searches in four databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. We included prospective phase II/III trials of patients with localized prostate cancer underdoing definitive EBRT with MR-guided focal boost. The outcomes of interest were bDFS and acute/late gastrointestinal and genitourinary toxicity.

RESULTS

Seven studies were included. All studies had a median follow-up of greater than 4 years. There were heterogeneities in fractionation, treatment planning, and delivery. Studies demonstrated effectiveness, feasibility, and tolerability of focal boost. Based on the Phoenix criteria for biochemical recurrence, the reported 5-year biochemical recurrence-free survival rates ranged 69.7-100% across included studies. All studies reported good safety profiles. The reported ranges of acute/late grade 3 + gastrointestinal toxicities were 0%/1-10%. The reported ranges of acute/late grade 3 + genitourinary toxicities were 0-13%/0-5.6%.

CONCLUSIONS

There is strong evidence that it is possible to improve oncologic outcomes without substantially increasing toxicity through MR-guided focal boost, at least in the setting of a 35-fraction radiotherapy regimen. Barriers to clinical practice implementation are addressable through additional investigation and new technologies.

Patient experiences with tissue-based genomic testing during active surveillance for prostate cancer

Background

Tissue-based gene expression (genomic) tests provide estimates of prostate cancer aggressiveness and are increasingly used for patients considering or engaged in active surveillance. However, little is known about patient experiences with genomic testing and its role in their decision-making.

Methods

We performed a qualitative study consisting of in-depth, semi-structured interviews of patients with low- or favourable-intermediate-risk prostate cancer managed with active surveillance. We purposively sampled to include patients who received biopsy-based genomic testing as part of clinical care. The interview guide focused on experiences with genomic testing during patients' decision-making for prostate cancer management and understanding of genomic test results. We continued interviews until thematic saturation was reached, iteratively created a code key and used conventional content analysis to analyse data.

Results

Participants' (n = 20) mean age was 68 years (range 51-79). At initial biopsy, 17 (85%) had a Gleason grade group 1, and 3 (15%) had a grade group 2 prostate cancer. The decision to undergo genomic testing was driven by both participants and physicians' recommendations; however, some participants were unaware that testing had occurred. Overall, participants understood the role of genomic testing in estimating their prostate cancer risk, and the test results increased their confidence in the decision for active surveillance. Participants had some misconceptions about the difference between tissue-based gene expression tests and germline genetic tests and commonly believed that tissue-based tests measured hereditary cancer risk. While some participants expressed satisfaction with their physicians' explanations, others felt that communication was limited and lacked sufficient detail.

Conclusion

Patients interact with and are influenced by the results of biopsy-based genomic testing during active surveillance for prostate cancer, despite gaps in understanding about test results. Our findings indicate areas for improvement in patient counselling in order to increase patient knowledge and comfort with genomic testing.

ReIGNITE Radiation Therapy Boost: A Prospective, International Study of Radiation Oncologists' Accuracy in Contouring Prostate Tumors for Focal Radiation Therapy Boost on Conventional Magnetic Resonance Imaging Alone or With Assistance of Restriction Spectrum Imaging

PURPOSE

In a phase III randomized trial, adding a radiation boost to tumor(s) visible on MRI improved prostate cancer (PCa) disease-free and metastasis-free survival without additional toxicity. Radiation oncologists' ability to identify prostate tumors is critical to widely adopting intraprostatic tumor radiotherapy boost for patients. A diffusion MRI biomarker, called the Restriction Spectrum Imaging restriction score (RSIrs), has been shown to improve radiologists' identification of clinically significant PCa. We hypothesized that (1) radiation oncologists would find accurately delineating PCa tumors on conventional MRI challenging and (2) using RSIrs maps would improve radiation oncologists' accuracy for PCa tumor delineation.

METHODS AND MATERIALS

In this multi-institutional, international, prospective study, 44 radiation oncologists (participants) and 2 expert radiologists (experts) contoured prostate tumors on 39 total patient cases using conventional MRI with or without RSIrs maps. Participant volumes were compared to the consensus expert volumes. Contouring accuracy metrics included percent overlap with expert volume, Dice coefficient, conformal number, and maximum distance beyond expert volume.

RESULTS

1604 participant volumes were produced. 40 of 44 participants (91%) completely missed ≥1 expert-defined target lesion without RSIrs, compared to 13 of 44 (30%) with RSIrs maps. On conventional MRI alone, 134 of 762 contour attempts (18%) completely missed the target, compared to 18 of 842 (2%) with RSIrs maps. Use of RSIrs maps improved all contour accuracy metrics by approximately 50% or more. Mixed effects modeling confirmed that RSIrs maps were the main variable driving improvement in all metrics. System Usability Scores indicated RSIrs maps significantly improved the contouring experience (72 vs. 58, p < 0.001).

CONCLUSIONS

Radiation oncologists struggle with accurately delineating visible PCa tumors on conventional MRI. RSIrs maps improve radiation oncologists' ability to target MRI-visible tumors for prostate tumor boost.

Dose-Dependent Atrophy in Bilateral Amygdalae and Nuclei After Brain Radiation Therapy and Its Association With Mood and Memory Outcomes on a Longitudinal Clinical Trial

PURPOSE

Amygdalae are bilateral, almond-shaped structures located anterior to the hippocampi, critical to limbic system functions of emotional processing and memory consolidation. The amygdalae are heterogeneous, composed of multiple nuclei with distinct structural and functional properties. We prospectively assessed associations between longitudinal changes in amygdala morphometry, including component nuclei, and functional outcomes in patients with primary brain tumors receiving radiation therapy (RT).

METHODS AND MATERIALS

On a prospective longitudinal trial, 63 patients underwent high-resolution volumetric brain magnetic resonance imaging and testing for mood (Beck Depression Inventory and Beck Anxiety Inventory), memory (Brief Visuospatial Memory Test-Revised [BVMT] Total Recall and Delayed Recall; Hopkins Verbal Learning Test-Revised [HVLT] Total Recall and Delayed Recall), and health-related quality-of-life outcomes (Functional Assessment of Cancer Therapy-Brain Social/Family Well-Being and Emotional Well-Being) at baseline and 3, 6, and 12 months after RT. Amygdalae, including 8 nuclei, were autosegmented bilaterally using validated techniques. Linear mixed-effects models assessed longitudinal change in amygdalae and nuclei volumes and associations with dose and outcomes. Wilcoxon rank sum tests compared amygdala volume change between patient groups with worse and more stable outcomes at each time point.

RESULTS

Atrophy was found in the right amygdala at 6 months (P = .001) and the left amygdala at 12 months (P = .046). A higher dose was associated with atrophy of the left amygdala (P = .013) at 12 months. The right amygdala showed dose-dependent atrophy at 6 months (P = .016) and 12 months (P = .001). Worse BVMT-Total, HVLT-Total, and HVLT-Delayed performance was associated with smaller left lateral (P = .014, P = .004, and P = .007, respectively) and left basal (P = .034, P = .016, and P = .026, respectively) nuclei volumes. Increased anxiety at 6 months was associated with greater combined (P = .031) and right (P = .007) amygdala atrophy. Greater left amygdala atrophy (P = .038) was noted in patients with decreased emotional well-being at 12 months.

CONCLUSIONS

Bilateral amygdalae and nuclei undergo time- and dose-dependent atrophy after brain RT. Atrophy in amygdalae and specific nuclei was associated with poorer memory, mood, and emotional well-being. Amygdalae-sparing treatment planning may preserve neurocognitive and neuropsychiatric outcomes in this population.

Use of focal radiotherapy boost for prostate cancer: radiation oncologists' perspectives and perceived barriers to implementation

BACKGROUND

In a recent phase III randomized control trial, delivering a focal radiotherapy (RT) boost to tumors visible on MRI was shown to improve disease-free survival and regional/distant metastasis-free survival for patients with prostate cancer-without increasing toxicity. The aim of this study was to assess how widely this technique is being applied in current practice, as well as physicians' perceived barriers toward its implementation.

METHODS

We invited radiation oncologists to complete an online questionnaire assessing their use of intraprostatic focal boost in December 2022 and February 2023. To include perspectives from a broad range of practice settings, the invitation was distributed to radiation oncologists worldwide via email list, group text platform, and social media.

RESULTS

263 radiation oncologist participants responded. The highest-represented countries were the United States (42%), Mexico (13%), and the United Kingdom (8%). The majority of participants worked at an academic medical center (52%) and considered their practice to be at least partially genitourinary (GU)-subspecialized (74%). Overall, 43% of participants reported routinely using intraprostatic focal boost. Complete GU-subspecialists were more likely to implement focal boost, with 61% reporting routine use. In both high-income and low-to-middle-income countries, less than half of participants routinely use focal boost. The most cited barriers were concerns about registration accuracy between MRI and CT (37%), concerns about risk of additional toxicity (35%), and challenges to accessing high-quality MRI (29%).

CONCLUSIONS

Two years following publication of a randomized trial of patient benefit without increased toxicity, almost half of the radiation oncologists surveyed are now routinely offering focal RT boost. Further adoption of this technique might be aided by increased access to high-quality MRI, better registration algorithms of MRI to CT simulation images, physician education on benefit-to-harm ratio, and training on contouring prostate lesions on MRI.

Use of Focal Radiotherapy Boost for Prostate Cancer and Perceived Barriers toward its Implementation: A Survey

PURPOSE/OBJECTIVE(S)

In a recent phase III randomized control trial (FLAME), delivering a focal radiotherapy (RT) boost to tumors visible on MRI was shown to improve outcomes for prostate cancer patients without increasing toxicity. The aim of this study was to assess how widely this technique is being applied in current practices worldwide as well as physicians' perceived barriers toward its implementation.

MATERIALS/METHODS

An online survey assessing the use of intraprostatic focal boost was conducted in December 2022 and February 2023. The survey link was distributed to radiation oncologists worldwide via email list, group text platform, and social media. Survey questions included how many prostate cancer cases participants treat in a typical month; how often they use focal boost, if at all; the degree to which their practice is genitourinary (GU)-subspecialized; main barriers to implementing focal boost more often in their practice; and demographic information. Subgroup analyses were also conducted for participants from high-income or low-to-middle-income countries, as defined by the World Bank.

RESULTS

The survey initially collected 205 responses from various countries over a two-week period in December 2022. The survey was then reopened for one week in February 2023 to allow for more participation, leading to a total of 263 responses. The highest-represented countries were the United States (42%), Mexico (13%), and the United Kingdom (8%). The majority of respondents worked at an academic medical center (52%) and considered their practice to be at least partially GU-subspecialized (74%). 57% of participants overall reported not routinely using intraprostatic focal boost. Even among complete subspecialists, a substantial proportion (39%) do not routinely use focal boost. Less than half of participants in both high-income and low-to-middle-income countries were shown to routinely use focal boost. Perceived barriers to implementation are shown in Table 1.

CONCLUSION

Despite the promising level 1 results of the FLAME trial, many radiation oncologists worldwide are not routinely offering focal RT boost. Adoption of this technique might be accelerated by increased access to high-quality MRI, better registration algorithms of MRI to CT simulation images, physician education on benefit-to-harm ratio, automated planning algorithms, and physician training on contouring prostate lesions on MRI.

Clinical Impact of Contouring Variability for Prostate Cancer Tumor Boost

PURPOSE/OBJECTIVE(S)

In the FLAME randomized phase III trial, adding a focal radiotherapy (RT) boost to tumors visible on MRI improved prostate cancer disease-free survival, local control, and regional/distant metastasis-free survival without increasing toxicity. In a prospective study (ReIGNITE RT Boost), we found substantial variability in radiation oncologists' attempts to contour prostate cancer tumors on MRI. Participants' accuracy and reliability improved when they used a quantitative MRI biomarker for cancer called the restriction spectrum imaging restriction score (RSIrs). Here, we measure the impact of radiation oncologists' tumor contour attempts on RT plans and predicted probability of biochemical failure.

MATERIALS/METHODS

A total of 44 radiation oncologists (participants) from multiple institutions contoured prostate tumors on 30 patient cases, some with only conventional MRI and some with conventional MRI plus RSIrs maps. We developed a knowledge-based planning automated algorithm to generate RT plans with focal tumor boost per the FLAME trial protocol: 77 Gy in 35 fractions to prostate and integrated boost up to 95 Gy to the focal target, provided no normal tissue constraints were violated. We applied this algorithm to each participant's tumor contour and compared dosimetric parameters to those achieved when using the expert-defined tumor (consensus of two radiologists and a radiation oncologist). The primary metric was dose covering 98% of the expert-defined tumor (D98%), which was associated with probability of biochemical failure in a model published with the FLAME trial.

RESULTS

In this preliminary analysis, 42 target volumes were analyzed from 20 participants and two patient cases: case 1 was contoured with conventional MRI alone and case 2 with RSIrs. All plans had adequate coverage of the prostate and met all key normal tissue constraints. For case 1 (without RSIrs), the expert's D98% was 87.1 Gy. By comparison, median D98% for participants was 82.2 Gy (IQR 77.8 - 84.6 Gy). Per the FLAME trial model, the predicted probability of biochemical failure at 7 years is 6% for the expert, but participants' plans yielded a median failure probability of 11% (IQR 18 - 9%). For case 2 (with RSIrs), the expert's D98% was 82.8 Gy, while median D98% for participants was 80.6 Gy (IQR 80.0 - 81.0 Gy). Predicted probability of biochemical failure is 12% for the expert-defined target and median 13% (IQR 14 - 13%) for participants.

CONCLUSION

Variability in radiation oncologists' prostate tumor contours can lead to clinically meaningful changes to focal RT boost plans. The probability of biochemical failure for one patient case increased from 6% to a median of 11% when using conventional MRI alone. Use of RSIrs may mitigate this problem by increasing the accuracy and reliability of radiation oncologists' tumor contours.

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer

Purpose

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy.

Experimental design

Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint.

Results

Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC.

Conclusion

The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.

Agent Orange exposure and prostate cancer risk in the Million Veteran Program

Purpose

Exposure to Agent Orange, a known carcinogen, might increase risk of prostate cancer (PCa). We sought to investigate the association of Agent Orange exposure and PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population of US Vietnam War veterans.

Methods & Materials

This study utilized the Million Veteran Program (MVP), a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. Agent Orange exposure was obtained using records from the Department of Veterans Affairs (VA) using the US government definition of Agent Orange exposure: active service in Vietnam while Agent Orange was in use. Only veterans who were on active duty (anywhere in the world) during the Vietnam War were included in this analysis (211,180 participants). Genetic risk was assessed via a previously validated polygenic hazard score calculated from genotype data. Age at diagnosis of any PCa, diagnosis of metastatic PCa, and death from PCa were assessed via Cox proportional hazards models.

Results

Exposure to Agent Orange was associated with increased PCa diagnosis (HR 1.04, 95% CI 1.01-1.06, p=0.003), primarily among Non-Hispanic White men (HR 1.09, 95% CI 1.06- 1.12, p<0.001). When accounting for race/ethnicity and family history, Agent Orange exposure remained an independent risk factor for PCa diagnosis (HR 1.06, 95% CI 1.04-1.09, p<0.05). Univariable associations of Agent Orange exposure with PCa metastasis (HR 1.08, 95% CI 0.99-1.17) and PCa death (HR 1.02, 95% CI 0.84-1.22) did not reach significance on multivariable analysis. Similar results were found when accounting for polygenic hazard score.

Conclusions

Among US Vietnam War veterans, Agent Orange exposure is an independent risk factor for PCa diagnosis, though associations with PCa metastasis or death are unclear when accounting for race/ethnicity, family history, and/or polygenic risk.

Use of focal radiotherapy boost for prostate cancer and perceived barriers toward its implementation: a survey

Background

In a recent phase III randomized control trial (FLAME), delivering a focal radiotherapy (RT) boost to tumors visible on MRI was shown to improve outcomes for prostate cancer patients without increasing toxicity. The aim of this study was to assess how widely this technique is being applied in current practice as well as physicians' perceived barriers toward its implementation.

Methods

An online survey assessing the use of intraprostatic focal boost was conducted in December 2022 and February 2023. The survey link was distributed to radiation oncologists worldwide via email list, group text platform, and social media.

Results

The survey initially collected 205 responses from various countries over a two-week period in December 2022. The survey was then reopened for one week in February 2023 to allow for more participation, leading to a total of 263 responses. The highest-represented countries were the United States (42%), Mexico (13%), and the United Kingdom (8%). The majority of participants worked at an academic medical center (52%) and considered their practice to be at least partially genitourinary (GU)-subspecialized (74%). 57% of participants reported not routinely using intraprostatic focal boost. Even among complete subspecialists, a substantial proportion (39%) do not routinely use focal boost. Less than half of participants in both high-income and low-to-middle-income countries were shown to routinely use focal boost. The most commonly cited barriers were concerns about registration accuracy between MRI and CT (37%), concerns about risk of additional toxicity (35%), and challenges to accessing high-quality MRI (29%).

Conclusion

Despite level 1 evidence from the FLAME trial, most radiation oncologists surveyed are not routinely offering focal RT boost. Adoption of this technique might be accelerated by increased access to high-quality MRI, better registration algorithms of MRI to CT simulation images, physician education on benefit-to-harm ratio, and training on contouring prostate lesions on MRI.

Use of Monitoring Tests Among Patients With Localized Prostate Cancer Managed With Observation

PURPOSE

It is unknown whether compliance with recommended monitoring tests during observation of localized prostate cancer has changed over time.

MATERIALS AND METHODS

We performed a retrospective cohort study of Medicare beneficiaries diagnosed with low- or intermediate-risk prostate cancer in 2004-2016 who were initially managed with observation for a minimum of 12 months. The primary objective was to examine rates of PSA testing, prostate biopsy, and prostate MRI. We used multivariable mixed effects Poisson regression to determine whether rates of PSA testing and prostate biopsy increased over time. In addition, we identified clinical, sociodemographic, and provider factors associated with the frequency of monitoring tests during observation.

RESULTS

We identified 10,639 patients diagnosed at a median age of 73 (IQR 69-77) years. The median follow-up time was 4.3 (IQR 2.7-6.6) years after diagnosis. Among patients managed without treatment for 5 years, 98% received at ≥1 PSA test, 48.0% ≥1 additional prostate biopsy, and 31.0% ≥1 prostate MRI. Among patients managed with observation for ≥12 months, mixed effects Poisson regression revealed that rates of PSA testing and biopsy increased over time (per calendar year: RR 1.02, 95% CI: 1.02-1.03 and RR 1.10, 95% CI: 1.08-1.11, respectively). Clinical and sociodemographic factors including age, clinical risk, race/ethnicity, census tract poverty, and region were associated with rates of biopsy and PSA testing.

CONCLUSIONS

Use of recommended monitoring tests including repeat prostate biopsy remains low among Medicare beneficiaries undergoing observation for low- and intermediate-risk prostate cancer.

NCCN Guidelines® Insights: Prostate Cancer Early Detection, Version 1.2023

The NCCN Guidelines for Prostate Cancer Early Detection provide recommendations for individuals with a prostate who opt to participate in an early detection program after receiving the appropriate counseling on the pros and cons. These NCCN Guidelines Insights provide a summary of recent updates to the NCCN Guidelines with regard to the testing protocol, use of multiparametric MRI, and management of negative biopsy results to optimize the detection of clinically significant prostate cancer and minimize the detection of indolent disease.

Healthy lifestyle, Agent Orange exposure, and inherited PCa risk: An analysis of the Million Veteran Program

210

Background: Prostate cancer (PCa) risk is understood to be mostly unmodifiable and inherited, but there is evidence that environmental and behavioral factors may also contribute. A recent study of health professional cohorts suggests a healthy lifestyle can mitigate a high inherited risk of lethal PCa. It is unknown how modifiable factors affect PCa risk in more diverse populations. Our objective was to determine the effects of healthy lifestyle and Agent Orange exposure on PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population. Methods: The Million Veteran Program (MVP) is a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. Healthy lifestyle was quantified as: A healthy lifestyle score (range 0-3) was calculated with a point assigned for each of the following at MVP enrollment: not a current smoker, body mass index (BMI) 30 and strenuous activity 2 days per week. Agent Orange exposure was obtained from VA records. Genetic risk was assessed via a polygenic hazard score using genotype data. Results: Healthy lifestyle was independently associated with reduced metastatic PCa (HR 0.82, 95% CI 0.77–0.87, p<0.001) and fatal PCa (HR 0.76, 95% CI 0.68–0.86, p<0.01) when accounting for family history, genetic risk, and race/ethnicity. The benefit of healthy lifestyle was also observed in Black participants on subset analysis. Agent Orange exposure was an independent factor for PCa diagnosis (HR 1.06, 95% CI 1.04-1.09). Conclusions: Adherence to a healthy lifestyle is associated with reduced risk of metastatic or fatal PCa, which offsets inherited risk.

Patient experiences with tissue-based genomic testing during active surveillance for prostate cancer

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Background: Tissue-based gene expression (genomic) tests improve estimates of prostate cancer aggressiveness and are increasingly used for patients considering or engaged in active surveillance; however, little is known about patient experiences with genomic testing and its role in decision-making for active surveillance. Methods: We performed a qualitative descriptive study consisting of in-depth, semi-structured interviews of patients with low- or favorable-intermediate-risk prostate cancer managed with active surveillance. The interview guide focused on experiences with biopsy-based genomic testing during their decision-making for prostate cancer management. We used purposive sampling to include patients who received genomic testing as part of routine clinical care and we over-sampled Black and Latino men. We continued interviews until thematic saturation was reached, iteratively created a code key and used conventional content data analysis. Results: The mean age was 68 years (range 51-79; n=20). At initial biopsy, 17 (85%) had a Gleason grade group 1, and 3 (15%) had a grade group 2 tumor. Fourteen (70%) participants identified their race/ethnicity as White, 5 (25%) as Black, and 2 (10%) as Latino. The decision to undergo genomic testing was driven by both participants and physicians’ recommendations; however, some participants were unaware that testing had occurred. Overall, participants understood the role of genomic testing in estimating their prostate cancer risk, and the test results increased their confidence in the decision for active surveillance. However, participants did not understand the difference between tissue-based gene expression tests and germline genetic tests, and commonly believed that tissue-based tests measured hereditary cancer risk. While some participants expressed satisfaction with the explanations provided by their physicians, others felt that communication was inaccessible and lacked sufficient detail. Conclusions: Patients interact with and are influenced by the results of biopsy-based genomic testing during active surveillance for prostate cancer, however testing may increase informational needs. Our findings indicate areas for improvement in patient counseling that can be used to increase patient knowledge and comfort with genomic testing.

“It just makes sense to me”: Patient experiences with prostate MRI during prostate cancer active surveillance

334

Background: Although prostate MRI is commonly used in the diagnosis, staging and active surveillance of prostate cancer, little is known about patient perspectives on MRI imaging. Methods: We performed a qualitative descriptive study consisting of in-depth, semi-structured interviews of patients with low and intermediate risk prostate cancer managed with active surveillance. Interviews focused on experiences with prostate MRI and MRI-ultrasound fusion biopsy during active surveillance monitoring. We purposively sampled patients who received prostate MRI as part of their clinical care, over-sampling Black and Latino men, and conducted interviews until thematic saturation. We performed conventional content analysis to analyze data. Results: The mean sample (n=20) age was 68 years (range 51-79). Fourteen (70%) participants identified as White, 5(25%) as Black, and 2(10%) as Hispanic/Latino. At diagnosis, 17 (85%) had a Gleason grade group 1, and 3 (15%) had a grade group 2 tumor. Overall, participants viewed prostate MRI as a valuable tool that accurately localizes and monitors prostate cancer over time. Prostate MRI was seen as central to active surveillance monitoring. We identified five thematic categories: (1) the experiential aspects of undergoing an MRI scan; (2) the experience of visualizing one’s own prostate and prostate cancer; (3) adequacy of provider explanations of MRI results; (4) confidence in prostate MRI in decision-making; and (5) the role of prostate MRI in longitudinal follow-up during active surveillance, including an interest using MRI to modify the timing of, or replace, prostate biopsy. Conclusions: These findings reveal that patients highly value prostate MRI as a tool that enhances the confidence in the initial diagnosis and monitoring of prostate cancer. This work can inform future studies to optimize the patient experience, education and counseling during active surveillance for prostate cancer.

The relationship between radiation dose and bevacizumab-related imaging abnormality in patients with brain tumors: A voxel-wise normal tissue complication probability (NTCP) analysis

PURPOSE

Bevacizumab-related imaging abnormality (BRIA), appearing as areas of restricted diffusion on magnetic resonance imaging (MRI) and representing atypical coagulative necrosis pathologically, has been observed in patients with brain tumors receiving radiotherapy and bevacizumab. We investigated the role of cumulative radiation dose in BRIA development in a voxel-wise analysis.

METHODS

Patients (n = 18) with BRIA were identified. All had high-grade gliomas or brain metastases treated with radiotherapy and bevacizumab. Areas of BRIA were segmented semi-automatically on diffusion-weighted MRI with apparent diffusion coefficient (ADC) images. To avoid confounding by possible tumor, hypoperfusion was confirmed with perfusion imaging. ADC images and radiation dose maps were co-registered to a high-resolution T1-weighted MRI and registration accuracy was verified. Voxel-wise normal tissue complication probability analyses were performed using a logistic model analyzing the relationship between cumulative voxel equivalent total dose in 2 Gy fractions (EQD2) and BRIA development at each voxel. Confidence intervals for regression model predictions were estimated with bootstrapping.

RESULTS

Among 18 patients, 39 brain tumors were treated. Patients received a median of 4.5 cycles of bevacizumab and 1-4 radiation courses prior to BRIA appearance. Most (64%) treated tumors overlapped with areas of BRIA. The median proportion of each BRIA region of interest volume overlapping with tumor was 98%. We found a dose-dependent association between cumulative voxel EQD2 and the relative probability of BRIA (β0 = -5.1, β1 = 0.03 Gy-1, γ = 1.3).

CONCLUSIONS

BRIA is likely a radiation dose-dependent phenomenon in patients with brain tumors receiving bevacizumab and radiotherapy. The combination of radiation effects and tumor microenvironmental factors in potentiating BRIA in this population should be further investigated.

Polygenic risk of any, metastatic, and fatal prostate cancer in the Million Veteran Program

BACKGROUND

Genetic scores may provide an objective measure of prostate cancer risk and thus inform screening decisions. We evaluated whether a polygenic hazard score based on 290 genetic variants (PHS290) is associated with prostate cancer risk in a diverse population, including Black men, who have higher average risk of prostate cancer death but are often treated as a homogeneously high-risk group.

METHODS

This was a retrospective analysis of the Million Veteran Program, a national, population-based cohort study of US military veterans conducted 2011-2021. Cox proportional hazards analyses tested for association of genetic and other risk factors (including self-reported race and ethnicity and family history) with age at death from prostate cancer, age at diagnosis of metastatic (nodal or distant) prostate cancer, and age at diagnosis of any prostate cancer.

RESULTS

A total of 590 750 male participants were included. Median age at last follow-up was 69 years. PHS290 was associated with fatal prostate cancer in the full cohort and for each racial and ethnic group (P < .001). Comparing men in the highest 20% of PHS290 with those in the lowest 20% (based on percentiles from an independent training cohort), the hazard ratio for fatal prostate cancer was 4.42 (95% confidence interval = 3.91 to 5.02). When accounting for guideline-recommended risk factors (family history, race, and ethnicity), PHS290 remained a strong independent predictor of any, metastatic, and fatal prostate cancer.

CONCLUSIONS

PHS290 stratified US veterans of diverse ancestry for lifetime risk of prostate cancer, including metastatic and fatal cancer. Predicting genetic risk of lethal prostate cancer with PHS290 might inform individualized decisions about prostate cancer screening.

Exploring new frontiers in prostate cancer research: Report from the 2022 Coffey-Holden prostate cancer academy meeting

INTRODUCTION

The 2022 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, "Exploring New Frontiers in Prostate Cancer Research," was held from June 23 to 26, 2022, at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA.

METHODS

The CHPCA Meeting is an annual discussion-oriented scientific conference organized by the Prostate Cancer Foundation, that focuses on emerging and next-step topics deemed critical for making the next major advances in prostate cancer research and clinical care. The 2022 CHPCA Meeting included 35 talks over 10 sessions and was attended by 73 academic investigators.

RESULTS

Major topic areas discussed at the meeting included: prostate cancer diversity and disparities, the impact of social determinants on research and patient outcomes, leveraging real-world and retrospective data, development of artificial intelligence biomarkers, androgen receptor (AR) signaling biology and new strategies for targeting AR, features of homologous recombination deficient prostate cancer, and future directions in immunotherapy and nuclear theranostics.

DISCUSSION

This article summarizes the scientific presentations from the 2022 CHPCA Meeting, with the goal that dissemination of this knowledge will contribute to furthering global prostate cancer research efforts.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

PRState: Incorporating genetic ancestry in prostate cancer risk scores for men of African ancestry

BACKGROUND

Prostate cancer (PrCa) is one of the most genetically driven solid cancers with heritability estimates as high as 57%. Men of African ancestry are at an increased risk of PrCa; however, current polygenic risk score (PRS) models are based on European ancestry groups and may not be broadly applicable. The objective of this study was to construct an African ancestry-specific PrCa PRS (PRState) and evaluate its performance.

METHODS

African ancestry group of 4,533 individuals in ELLIPSE consortium was used for discovery of African ancestry-specific PrCa SNPs. PRState was constructed as weighted sum of genotypes and effect sizes from genome-wide association study (GWAS) of PrCa in African ancestry group. Performance was evaluated using ROC-AUC analysis.

RESULTS

We identified African ancestry-specific PrCa risk loci on chromosomes 3, 8, and 11 and constructed a polygenic risk score (PRS) from 10 African ancestry-specific PrCa risk SNPs, achieving an AUC of 0.61 [0.60-0.63] and 0.65 [0.64-0.67], when combined with age and family history. Performance dropped significantly when using ancestry-mismatched PRS models but remained comparable when using trans-ancestry models. Importantly, we validated the PRState score in the Million Veteran Program (MVP), demonstrating improved prediction of PrCa and metastatic PrCa in individuals of African ancestry.

CONCLUSIONS

African ancestry-specific PRState improves PrCa prediction in African ancestry groups in ELLIPSE consortium and MVP. This study underscores the need for inclusion of individuals of African ancestry in gene variant discovery to optimize PRSs and identifies African ancestry-specific variants for use in future studies.

Tri-Compartmental Restriction Spectrum Imaging Breast Model Distinguishes Malignant Lesions from Benign Lesions and Healthy Tissue on Diffusion-Weighted Imaging

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm²) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C₁ compartment and product of the restricted and intermediate diffusion compartments (C₁ and C₂). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

BACKGROUND

Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.

METHODS

In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.

RESULTS

The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.

CONCLUSIONS

We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T₂-weighted images were annotated. Changes in distance between DWI- and T₂-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T₂-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10^-20). On average (± SD), participants' largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0-7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10^-12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

Genetic Stratification of Age-Dependent Parkinson's Disease Risk by Polygenic Hazard Score

BACKGROUND

Parkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders.

OBJECTIVES

We aimed to develop and validate a polygenic hazard score model in sporadic PD.

METHODS

Using a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls.

RESULTS

A polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups.

CONCLUSIONS

We demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Using Polygenic Hazard Scores to Predict Age at Onset of Alzheimer's Disease in Nordic Populations

BACKGROUND

Polygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer's disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed.

OBJECTIVE

The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations.

METHODS

We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886).

RESULTS

We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model.

CONCLUSION

PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.

Characterization of the diffusion signal of breast tissues using multi-exponential models

PURPOSE

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues.

METHODS

The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.

RESULTS

A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D_1,3 = 0, D_2,3 = 1.5 × 10^-3 , and D_3,3 = 10.8 × 10^-3 mm² /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent.

CONCLUSION

Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men

BACKGROUND

Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.

METHODS

Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.

RESULTS

Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10^-15).

CONCLUSIONS

PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

BACKGROUND

Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).

MATERIALS AND METHOD

180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

RESULTS

166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

CONCLUSIONS

Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging

BACKGROUND

Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI₄ ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI₄ -C₁ , yielded greatest tumor conspicuity.

PURPOSE

To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI₄ -C₁ ) as a quantitative voxel-level classifier of PCa.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.

FIELD STRENGTH/SEQUENCE

A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.

ASSESSMENT

High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI₄ -C₁ and conventional ADC. Classifier images were also generated.

STATISTICAL TESTS

Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI₄ -C₁ was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR₉₀ ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.

RESULTS

RSI₄ -C₁ outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR₉₀ (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).

DATA CONCLUSION

RSI₄ -C₁ yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10-180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

Common genetic and clinical risk factors: Association with fatal prostate cancer in the Cohort of Swedish Men

65

Background: Clinical variables (age, family history, and genetics) are commonly used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores, including PHS, are associated with all prostate cancer and are not specific for fatal cancers, PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to available clinical variables improves associations with prostate cancer death. Methods: Genotype and phenotype data were obtained from a nested case-control subset (n=3,279; 2,163 were diagnosed with prostate cancer, 278 died of prostate cancer) of the longitudinal, population-based Cohort of Swedish Men. PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes history, and body mass index) were independently tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models were constructed with clinical variables and PHS. Log-likelihood tests compared models. Results: Median age at last follow-up and at prostate cancer death were 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol intake (HR 1.74 [1.40-2.15]), and diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. A multivariable clinical model including PHS46 improved associations for fatal disease ( p<10−15). On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol intake (HR 1.45 [1.19-1.76]), and diabetes (HR 0.62 [0.42-0.90]) all remained associated with prostate cancer death. Similar results were found using the newer PHS166. Conclusions: PHS had the most robust association with fatal prostate cancer in a multivariable model with common clinical risk factors, including family history. Adding PHS to clinical variables may improve individualized prostate cancer risk stratification strategies.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

PURPOSE

Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

EXPERIMENTAL DESIGN

Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.

RESULTS

Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).

CONCLUSIONS

The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy

Background

Whole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans.

Methods

We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient’s T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020).

Results

Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53–13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated.

Conclusions

An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

The effect of sample size on polygenic hazard models for prostate cancer

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

African-specific improvement of a polygenic hazard score for age at diagnosis of prostate cancer

Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46) showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify single nucleotide polymorphisms (SNPs) that might improve performance in this population. Anonymized genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Ten iterations of a 10-fold cross-validation search were conducted to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African was compared using the same cross-validated approach. Three SNPs (rs76229939, rs74421890 and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47-4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65-0.53). In conclusion, we identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans.

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data

BACKGROUND

A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

METHODS

United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

RESULTS

The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

CONCLUSIONS

PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

IMPACT

Personalized genetic risk assessments could inform prostate cancer screening decisions.

Abstract 3506: African-specific improvement of a polygenic hazard score to predict age of onset of prostate cancer

Introduction: Polygenic hazard models can be used in predicting age of onset of prostate cancer. A recently published model, PHS46, showed reduced performance in men with African genetic ancestry - the hazard ratio for aggressive prostate cancer in individuals with high values of PHS46 was approximately half as large for those with African genetic ancestry (2.4) as it was for those with European (5.6) or Asian (5.2) ancestry. We used a cross-validated search to identify SNPs that would add statistical value to a base model of PHS46 among African men and help bridge the performance gap in this population.

Material and Methods: Anonymized genotypic data were obtained from 6,253 men with African genetic ancestry from the PRACTICAL consortium. Ten iterations of a ten-fold cross-validation search were conducted, where SNPs were added greedily and stepwise to the PHS46 Cox proportional hazards model using age of onset of any prostate cancer as the dependent variable. Individuals without prostate cancer were censored at age of last follow-up. SNPs were selected for the final, enhanced-PHS46 model if they were selected in more than 50% of the folds. The predictive performances of PHS46 and enhanced-PHS46 were compared using the same cross-validated approach.

Results: Three SNPs (rs76229939, rs74421890, and rs5013678) were selected for inclusion in enhanced-PHS46 model. All three SNPs are located on chromosome 8q.24. Enhanced-PHS46 model showed substantial improvements in all performance metrics measured, including a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65 to 0.53), a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47 to 4.34) and a 95% increase in the relative hazard of those in the upper 2% compared to the middle 40% (2.1 to 4.1). Similar improvements were demonstrated using the age of onset of aggressive prostate cancer.

Conclusions: We identified three SNPs (rs76229939, rs74421890, and rs5013678) that substantially improved the performance of PHS46 in a dataset of men with African genetic ancestry. A strategy of building on established models might be applied to other groups generally under-represented in genome-wide association studies.

Citation Format: Roshan Karunamuni, Minh-Phuong C. Huynh-Le, Chun C. Fan, Ian G. Mills, Ole A. Andreassen, Anders M. Dale, Tyler M. Seibert, The PRACTICAL Consortium. African-specific improvement of a polygenic hazard score to predict age of onset of prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3506.

Age dependence of modern clinical risk groups for localized prostate cancer-A population-based study

BACKGROUND

Optimal prostate cancer (PCa) screening strategies will focus on men likely to have potentially lethal disease. Age-specific incidence rates (ASIRs) by modern clinical risk groups could inform risk stratification efforts for screening.

METHODS

This cross-sectional population study identified all men diagnosed with PCa in Norway from 2014 to 2017 (n = 20,356). Age, Gleason score (primary plus secondary), and clinical stage were extracted. Patients were assigned to clinical risk groups: low, favorable intermediate, unfavorable intermediate, high, regional, and metastatic. Chi-square tests analyzed the independence of Gleason scores and modern PCa risk groups with age. ASIRs for each risk group were calculated as the product of Norwegian ASIRs for all PCa and the proportions observed for each risk category.

RESULTS

Older age was significantly associated with a higher Gleason score and more advanced disease. The percentages of men with Gleason 8 to 10 disease among men aged 55 to 59, 65 to 69, 75 to 79, and 85 to 89 years were 16.5%, 23.4%, 37.2%, and 59.9%, respectively (P < .001); the percentages of men in the same age groups with at least high-risk disease were 29.3%, 39.1%, 60.4%, and 90.6%, respectively (P < .001). The maximum ASIRs (per 100,000 men) for low-risk, favorable intermediate-risk, unfavorable intermediate-risk, high-risk, regional, and metastatic disease were 157.1 for those aged 65 to 69 years, 183.8 for those aged 65 to 69 years, 194.8 for those aged 70 to 74 years, 408.3 for those aged 75 to 79 years, 159.7 for those aged ≥85 years, and 314.0 for those aged ≥85 years, respectively. At the ages of 75 to 79 years, the ASIR of high-risk disease was approximately 6 times greater than the ASIR at 55 to 59 years.

CONCLUSIONS

The risk of clinically significant localized PCa increases with age. Healthy older men may benefit from screening.