Risk calculators have been created on the basis of predictive modeling to aid clinicians and patients in deciding whether to pursue prostate biopsy. Ideally, these risk calculators would reduce the ...diagnosis of low‐risk prostate cancer and increase the diagnosis of clinically significant prostate cancer. However, before these prediction models are to be available for widespread use, they must be carefully validated, discriminated, and calibrated to ensure their performance and applicability.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Human tubulin beta class IVa (TUBB4A) is a member of the β-tubulin family. In most normal tissues, expression of TUBB4A is little to none, but it is highly expressed in human prostate cancer. Here we ...show that high expression levels of TUBB4A are associated with aggressive prostate cancers and poor patient survival, especially for African-American men. Additionally, in prostate cancer cells, TUBB4A knockout (KO) reduces cell growth and migration but induces DNA damage through increased γH2AX and 53BP1. Furthermore, during constricted cell migration, TUBB4A interacts with MYH9 to protect the nucleus, but either TUBB4A KO or MYH9 knockdown leads to severe DNA damage and reduces the NF-κB signaling response. Also, TUBB4A KO retards tumor growth and metastasis. Functional analysis reveals that TUBB4A/GSK3β binds to the N-terminal of MYH9, and that TUBB4A KO reduces MYH9-mediated GSK3β ubiquitination and degradation, leading to decreased activation of β-catenin signaling and its relevant epithelial-mesenchymal transition. Likewise, prostate-specific deletion of Tubb4a reduces spontaneous tumor growth and metastasis via inhibition of NF-κB, cyclin D1, and c-MYC signaling activation. Our results suggest an oncogenic role of TUBB4A and provide a potentially actionable therapeutic target for prostate cancers with TUBB4A overexpression.
Prostate MRI is currently the best diagnostic imaging method for detecting PCa. Magnetic resonance imaging (MRI)/ultrasonography (US) fusion allows the sensitivity and specificity of MRI to be ...combined with the real‐time capabilities of transrectal ultrasonography (TRUS). Multiple approaches and techniques exist for MRI/US fusion and include direct ‘in bore’ MRI biopsies, cognitive fusion, and MRI/US fusion via software‐based image coregistration platforms.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Background
Most Prostate Imaging–Reporting and Data System (PI‐RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying ...clinical and magnetic resonance imaging (MRI)–derived risk fac‐ tors that predict CSPCa in men with PI‐RADS 3 lesions.
Methods
This study analyzed the detection of CSPCa in men who underwent MRI‐targeted biopsy for PI‐RADS 3 lesions. Multivariable logistic regression models with goodness‐of‐fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.
Results
Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate‐specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio OR, 1.36; 95% confidence interval CI, 1.05–1.85; p < .01), age (OR, 1.05; 95% CI, 1.02–1.07; p < .01), and a biopsy‐naive status (OR, 1.83; 95% CI, 1.38–2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24–0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.
Conclusions
For PI‐RADS 3 lesions, an elevated PSA density, older age, and a biopsy‐naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI‐RADS 3 biopsies while missing few CSPCas.
Lay summary
Among men with an equivocal lesion (Prostate Imaging–Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate‐specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy.
However, men with at least one negative biopsy have a lower risk of CSPCa.
A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa.
In men with a Prostate Imaging–Reporting and Data System (PI‐RADS) 3 index lesion, an elevated prostate‐specific antigen density, older age, an anteriorly located lesion, and a biopsy‐naive status were associated with the detection of clinically significant prostate cancer on subsequent biopsy, whereas a history of a prior negative biopsy was negatively associated. A novel predictive model could prevent PI‐RADS 3 biopsies while missing few cases of clinically significant prostate cancer.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
It remains unclear what the optimum management strategy is for young men with low‐risk prostate cancer. The authors address the increasing role, safety, and acceptance of active surveillance as a ...management option for these younger patients.
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Objective:
To determine which otolaryngology residency programs have social media platforms and to review which programs are utilizing platforms to advertise virtual open houses and virtual ...subinternships for residency applicants.
Study Design:
Cross-sectional study.
Setting:
The study was conducted online by reviewing all accredited otolaryngology residency programs in the United States participating in the Electronic Residency Application Service.
Methods:
Otolaryngology residency programs were reviewed for social media presence on Instagram, Twitter, and Facebook. Social media posts were evaluated for virtual open houses and virtual subinternships. Residency websites and the Visiting Student Application Service were evaluated for the presence of virtual subinternships. All data were collected between September 5, 2020, and September 9, 2020. This study did not require approval from the University of Alabama at Birmingham Institutional Review Board for Human Use.
Results:
Among 118 otolaryngology residency programs, 74 (62.7%) participate on Instagram, 52 (44.1%) participate on Twitter, and 44 (37.3%) participate on Facebook. Fifty-one Instagram accounts, 20 Twitter accounts, and 4 Facebook accounts have been created during 2020. Forty-two (36%), 30 (25.4%), and 15 (13%) programs are promoting virtual open houses on Instagram, Twitter, and Facebook, respectively. Two programs on the Visiting Student Application Service offered virtual subinternships. Seven residency program websites offered virtual subinternships. Nine, 6, and 1 program offered virtual subinternships on Instagram, Twitter, and Facebook, respectively.
Conclusion:
This study demonstrates that social media presence on Instagram and Twitter among otolaryngology residency programs has substantially grown in 2020 at a higher rate compared to previous years. These data suggest that otolaryngology residency programs are finding new ways to reach out to applicants amid an unprecedented type of application cycle due to the challenges presented by COVID-19. Many programs are advertising virtual open houses via social media platforms to connect with applicants, and a few programs are offering virtual subinternships to replace traditional subinternships.
Objectives
To develop and validate a prostate cancer (PCa) risk calculator (RC) incorporating multiparametric magnetic resonance imaging (mpMRI) and to compare its performance with that of the ...Prostate Biopsy Collaborative Group (PBCG) RC.
Patients and Methods
Men without a PCa diagnosis receiving mpMRI before biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015–2020) were included. Data from a separate institution were used for external validation. The primary outcome was diagnosis of no cancer, grade group (GG)1 PCa, and clinically significant (cs)PCa (≥GG2). Binary logistic regression was used to explore standard clinical and mpMRI variables (prostate volume, Prostate Imaging‐Reporting Data System PI‐RADS version 2.0 lesions) with the final PLUM RC, based on a multinomial logistic regression model. Receiver‐operating characteristic curve, calibration curves, and decision‐curve analysis were evaluated in the training and validation cohorts.
Results
A total of 1010 patients were included for development (N = 674 training 47.8% PCa, 30.9% csPCa, N = 336 internal validation) and 371 for external validation. The PLUM RC outperformed the PBCG RC in the training (area under the curve AUC 85.9% vs 66.0%; P < 0.001), internal validation (AUC 88.2% vs 67.8%; P < 0.001) and external validation (AUC 83.9% vs 69.4%; P < 0.001) cohorts for csPCa detection. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a threshold probability of 15%, the PLUM RC vs the PBCG RC could avoid 13.8 vs 2.7 biopsies per 100 patients without missing any csPCa. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for the PBCG RC. The PLUM RC compared favourably with the Stanford Prostate Cancer Calculator (SPCC; AUC 84.1% vs 81.1%; P = 0.002) and the MRI‐European Randomized Study of Screening for Prostate Cancer (ERSPC) RC (AUC 84.5% vs 82.6%; P = 0.05).
Conclusions
The mpMRI‐based PLUM RC significantly outperformed the PBCG RC and compared favourably with other mpMRI‐based RCs. A large proportion of biopsies could be avoided using the PLUM RC in shared decision making while maintaining optimal detection of csPCa.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK