Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%–20%. Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63–72) years, 9 (7–15) ng/mL, and 0.20 (0.13–0.32) ng/mL2, respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. Conclusions: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.</p

Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk

Importance: Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective: To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants: This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions: Prostate MRI followed by prostate biopsy. Main Outcomes and Measures: The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results: A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance: In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.</p

Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging

Background and objective: The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa). Methods: Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort (n = 427) or a validation (n = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis. Results: Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16–0.54; p < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62–5.45; p < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter. Conclusions and clinical implications: Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations. Patient summary: For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy

Metabolic cold acclimation of 'Polka' and 'Honeoye' strawberries under natural field conditions

The winter hardiness of strawberry varieties used in perennial production systems varies greatly. Still, little information is available on how plant metabolism adapts to cold and freezing temperatures under natural temperature and light conditions. In order to examine the hardening process of overwintering meristematic tissue in Fragaria ananassa, crown samples of field-grown var. ‘Polka’ and ‘Honeoye’ were consecutively collected over a period of 15 weeks, i.e. from the end of the season (week 35/ end August) until midwinter (week 50/ December). Samples were subjected to qGC MS metabolite profiling to assess the reconfiguration of central metabolism, and characterize the regulation of selected compatible solutes. Besides changes in amino acid patterns (glutamic acid, aspartic acid, and asparagine), monosaccharide levels (fructose) increased strongly in ‘Honeoye’ (180 fold compared to start control) towards the end of the acclimation period. In contrast, ‘Polka’ showed a concentration peak (36-fold) in week 47 and a decline towards week 50. Also sucrose levels were steadily increased throughout the cold hardening period with averagely 6-fold higher levels in ‘Honeoye’ compared to ‘Polka’, thus underscoring cultivar-dependent differences. However, both varieties showed a decline in sucrose levels after week 47. Particularly, the raffinose pathway was affected leading to strongly and transiently increased levels of the precursor galactinol (week 42/ mid-October) and the trisaccharide raffinose (weeks 43 to 47/ end October to mid-November). While galactinol biosynthesis was earlier induced in ‘Polka’ (week 38) compared to ‘Honeoye’ (week 39), subsequent raffinose production was delayed in ‘Polka’ (week 47) compared to ‘Honeoye’ (week 45). Major metabolic changes in both varieties coincided with a decrease in day length below 14 h in mid-September, and a consistent drop below 10°C average day temperature by the end of September

Metabolic cold acclimation of 'Polka' and 'Honeoye' strawberries under natural field conditions

The winter hardiness of strawberry varieties used in perennial production systems varies greatly. Still, little information is available on how plant metabolism adapts to cold and freezing temperatures under natural temperature and light conditions. In order to examine the hardening process of overwintering meristematic tissue in Fragaria ananassa, crown samples of field-grown var. ‘Polka’ and ‘Honeoye’ were consecutively collected over a period of 15 weeks, i.e. from the end of the season (week 35/ end August) until midwinter (week 50/ December). Samples were subjected to qGC MS metabolite profiling to assess the reconfiguration of central metabolism, and characterize the regulation of selected compatible solutes. Besides changes in amino acid patterns (glutamic acid, aspartic acid, and asparagine), monosaccharide levels (fructose) increased strongly in ‘Honeoye’ (180 fold compared to start control) towards the end of the acclimation period. In contrast, ‘Polka’ showed a concentration peak (36-fold) in week 47 and a decline towards week 50. Also sucrose levels were steadily increased throughout the cold hardening period with averagely 6-fold higher levels in ‘Honeoye’ compared to ‘Polka’, thus underscoring cultivar-dependent differences. However, both varieties showed a decline in sucrose levels after week 47. Particularly, the raffinose pathway was affected leading to strongly and transiently increased levels of the precursor galactinol (week 42/ mid-October) and the trisaccharide raffinose (weeks 43 to 47/ end October to mid-November). While galactinol biosynthesis was earlier induced in ‘Polka’ (week 38) compared to ‘Honeoye’ (week 39), subsequent raffinose production was delayed in ‘Polka’ (week 47) compared to ‘Honeoye’ (week 45). Major metabolic changes in both varieties coincided with a decrease in day length below 14 h in mid-September, and a consistent drop below 10°C average day temperature by the end of September

Metabolic cold acclimation of 'Polka' and 'Honeoye' strawberries under natural field conditions

The winter hardiness of strawberry varieties used in perennial production systems varies greatly. Still, little information is available on how plant metabolism adapts to cold and freezing temperatures under natural temperature and light conditions. In order to examine the hardening process of overwintering meristematic tissue in Fragaria ananassa, crown samples of field-grown var. ‘Polka’ and ‘Honeoye’ were consecutively collected over a period of 15 weeks, i.e. from the end of the season (week 35/ end August) until midwinter (week 50/ December). Samples were subjected to qGC MS metabolite profiling to assess the reconfiguration of central metabolism, and characterize the regulation of selected compatible solutes. Besides changes in amino acid patterns (glutamic acid, aspartic acid, and asparagine), monosaccharide levels (fructose) increased strongly in ‘Honeoye’ (180 fold compared to start control) towards the end of the acclimation period. In contrast, ‘Polka’ showed a concentration peak (36-fold) in week 47 and a decline towards week 50. Also sucrose levels were steadily increased throughout the cold hardening period with averagely 6-fold higher levels in ‘Honeoye’ compared to ‘Polka’, thus underscoring cultivar-dependent differences. However, both varieties showed a decline in sucrose levels after week 47. Particularly, the raffinose pathway was affected leading to strongly and transiently increased levels of the precursor galactinol (week 42/ mid-October) and the trisaccharide raffinose (weeks 43 to 47/ end October to mid-November). While galactinol biosynthesis was earlier induced in ‘Polka’ (week 38) compared to ‘Honeoye’ (week 39), subsequent raffinose production was delayed in ‘Polka’ (week 47) compared to ‘Honeoye’ (week 45). Major metabolic changes in both varieties coincided with a decrease in day length below 14 h in mid-September, and a consistent drop below 10°C average day temperature by the end of September

Reducing prostate biopsies and magnetic resonance imaging with prostate cancer risk stratification

Objectives: To recalibrate and validate the European Randomized Study of Screening for Prostate Cancer risk calculators (ERSPC RCs) 3/4 and the magnetic resonance imaging (MRI)-ERSPC-RCs to a contemporary Norwegian setting to reduce upfront prostate multiparametric MRI (mpMRI) and prostate biopsies. Patients and Methods: We retrospectively identified and entered all men who underwent prostate mpMRI and subsequent prostate biopsy between January 2016 and March 2017 in a Norwegian centre into a database. mpMRI was reported using PI-RADS v2.0 and clinically significant prostate cancer (csPCa) defined as Gleason ≥ 3 + 4. Probabilities of csPCa and any prostate cancer (PCa) on biopsy were calculated by the ERSPC RCs 3/4 and the MRI-ERSPC-RC and compared with biopsy results. RCs were then recalibrated to account for differences in prevalence between the development and current cohorts (if indicated), and calibration, discrimination and clinical usefulness assessed. Results: Three hundred and three patients were included. The MRI-ERSPC-RCs were perfectly calibrated to our cohort, although the ERSPC RCs 3/4 needed recalibration. Area under the receiver operating curve (AUC) for the ERSPC RCs 3/4 was 0.82 for the discrimination of csPCa and 0.77 for any PCa. The AUC for the MRI-ERSPC-RCs was 0.89 for csPCa and 0.85 for any PCa. Decision curve analysis showed clear net benefit for both the ERSPC RCs 3/4 (>2% risk of csPCa threshold to biopsy) and for the MRI-ERSPC-RCs (>1% risk of csPCa threshold), with a greater net benefit for the MRI-RCs. Using a >10% risk of csPCa or 20% risk of any PCa threshold for the ERSPC RCs 3/4, 15.5% of mpMRIs could be omitted, missing 0.8% of csPCa. Using the MRI-ERSPC-RCs, 23.4% of biopsies could be omitted with the same threshold, missing 0.8% of csPCa. Conclusion: The ERSPC RCs 3/4 and MRI-ERSPC-RCs can considerably reduce both upfront mpMRI and prostate biopsies with little risk of missing csPCa

Can a Peritoneal Conduit Become an Artery?

Objective Current vascular grafts all have limitations. This study examined peritoneum as a potential graft material and the in vivo transfer of peritoneum into a functional artery like conduit after end to end anastomosis into the common carotid artery of sheep. The aim was to investigate whether implantation of a peritoneal tube into the arterial tree results in a structure with function, histological findings, and gene expression like an artery, and whether such arterialisation occurs through a conversion of the phenotype of peritoneal cells or from host cell migration into the implant. Methods Peritoneum with adherent rectus aponeurosis from sheep was used to form tubular vascular grafts that were implanted into the common carotid artery of six sheep, then removed after five months. Two sheep received allogenic peritoneal grafts and four sheep received autologous peritoneal grafts. Results One sheep died shortly after implantation, so five of the six sheep were followed. Five months after implantation, four of the five remaining grafts were patent. Three of four patent grafts were aneurysmal. The four patent grafts had developed an endothelial layer indistinguishable from that of the adjacent normal artery, and a medial layer with smooth muscle cells with a surrounding adventitia. The new conduit displayed vasomotor function not present at the time of implantation. DNA genotyping showed that the media in the new conduit consisted of recipient smooth muscle cells. Little difference in mRNA expression was demonstrated between the post-implantation conduit and normal artery. Conclusion During a five month implantation period in the arterial system, peritoneum converted into a tissue that histologically and functionally resembled a normal artery, with a functional genetic expression that resembled that of an artery. Single nucleotide polymorphism analysis indicated that this conversion occurs through host cell migration into the graft

Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk

Importance: Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective: To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants: This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions: Prostate MRI followed by prostate biopsy. Main Outcomes and Measures: The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results: A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance: In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.</p

Dehydrin, alcohol dehydrogenase, and central metabolite levels are associated with cold tolerance in diploid strawberry (Fragaria spp.)

The use of artificial freezing tests, identification of biomarkers linked to or directly involved in the low-temperature tolerance processes, could prove useful in applied strawberry breeding. This study was conducted to identify genotypes of diploid strawberry that differ in their tolerance to low-temperature stress and to investigate whether a set of candidate proteins and metabolites correlate with the level of tolerance. 17 Fragaria vesca, 2 F. nilgerrensis, 2 F. nubicola, and 1 F. pentaphylla genotypes were evaluated for low-temperature tolerance. Estimates of temperatures where 50 % of the plants survived (LT50) ranged from −4.7 to −12.0 °C between the genotypes. Among the F. vesca genotypes, the LT50 varied from −7.7 °C to −12.0 °C. Among the most tolerant were three F. vesca ssp. bracteata genotypes (FDP821, NCGR424, and NCGR502), while a F. vesca ssp. californica genotype (FDP817) was the least tolerant (LT50 −7.7 °C). Alcohol dehydrogenase (ADH), total dehydrin expression, and content of central metabolism constituents were assayed in select plants acclimated at 2 °C. The LT50 estimates and the expression of ADH and total dehydrins were highly correlated (r adh = −0.87, r dehyd = −0.82). Compounds related to the citric acid cycle were quantified in the leaves during acclimation. While several sugars and acids were significantly correlated to the LT50 estimates early in the acclimation period, only galactinol proved to be a good LT50 predictor after 28 days of acclimation (r galact = 0.79). It is concluded that ADH, dehydrins, and galactinol show great potential to serve as biomarkers for cold tolerance in diploid strawberry