scholarly journals Radiologists Should Integrate Clinical Risk Factors with MRI Findings for Meaningful Prostate Cancer Staging

Radiology ◽  
2020 ◽  
Vol 296 (1) ◽  
pp. 96-97
Author(s):  
Anwar R. Padhani ◽  
Giuseppe Petralia
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Cancer Staging ◽  
Clinical Risk Factors ◽  
Mri Findings ◽  
Clinical Risk ◽  
Prostate Cancer Staging

scholarly journals OBESITY AND PROSTATE CANCER CLINICAL RISK FACTORS AT PRESENTATION: DATA FROM CaPSURE

2005 ◽  
Vol 173 (3) ◽  
pp. 732-736 ◽  
Author(s):  
CHRISTOPHER J. KANE ◽  
WILLIAM W. BASSETT ◽  
NATALIA SADETSKY ◽  
STEFANIE SILVA ◽  
KATRINE WALLACE ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Clinical Risk Factors ◽  
Clinical Risk

scholarly journals Clinical Risk Factors for Osteoporotic Fractures in Men With Non-Metastatic Prostate Cancer on Androgen Deprivation Therapy With or Without Anti-Osteoporosis Treatment

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A243-A244
Author(s):  
Hajerah Sonnabend ◽  
Vishnu Priya Pulipati ◽  
Sanford Baim ◽  
Todd Beck ◽  
J Alan Simmons ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Cancer Therapy ◽  
Androgen Deprivation Therapy ◽  
Androgen Deprivation ◽  
Clinical Risk Factors ◽  
Mineral Density ◽  
Steroid Use ◽  
Clinical Risk ◽  
Significant Difference

Abstract Introduction: Androgen deprivation therapy (ADT) decreases bone mineral density and increases osteoporotic fracture (OsteoFx) risk. Hypothesis: To assess OsteoFx clinical risk factors (CRF) most predictive of future OsteoFx among men with prostate cancer on ADT. Methods: 4370 electronic medical records were reviewed of adult men with prostate cancer on cancer therapy +/- anti-osteoporosis therapy (Anti-OsteoRx) from 2011–2019. Cancer therapy included ADT (anti-androgens, GnRH agonists & antagonists, orchiectomy) and supplemental cancer therapy (SupplRx) (prostatectomy, brachytherapy, radiation, immunotherapy, and chemotherapy). Anti-OsteoRx included bisphosphonates, denosumab, and parathyroid hormone analogs. Patients with other cancers within 5 years of initial visit, metastasis, and traumatic fractures were excluded. Retrospective analysis was done to determine baseline characteristics, type and duration of ADT, Anti-OsteoRx, SupplRx, and osteoporosis CRF. Results: 615 men on ADT +/- SupplRx +/- Anti-OsteoRx were included in the study. 10.08% had OsteoFx irrespective of SupplRx or Anti-OsteoRx. Comparing the OsteoFx group to the non-fracture group, the following CRF were found to be statistically significant (p <0.05): age at prostate cancer diagnosis (75.10 +/- 11.80 vs 71.59 +/- 9.80 y), diabetes mellitus (DM) (33.9 vs 19%), pre-existing comorbidities affecting bone (PreCo) (41.9 vs 24.8%), steroid use (11.3 vs 4.0%), and anti-convulsant and proton-pump inhibitor (med) use (45.2 vs 26.8%). 9.89% of 374 men on ADT only without (wo) Anti-OsteoRx fractured. Statistically significant CRF for OsteoFx were age (76.86 +/- 10.55 vs 73.02 +/- 10.06 y), DM (40.5 vs 19.6%), PreCo (45.9 vs. 26.4%), and med use (48.6 vs. 25.5%). In the following subgroups there were no statistically significant difference in CRF:•7.64% of 170 men on ADT + SupplRx wo Anti-OsteoRx •19.23% of 52 men on ADT only + Anti-OsteoRx •10.52% of 19 men on ADT + SupplRx + Anti-OsteoRx To increase statistical power, patients on ADT +/- SupplRx were assessed:•Among 71 men on ADT +/- SupplRx + Anti-OsteoRx, there were no statistically significant differences in CRF•Among the 544 men on ADT +/- SupplRx wo Anti-OsteoRx, significant CRF for OsteoFx were age (75.16 + 11.70 vs 71.37 + 9.85 y), DM (38 vs 19.4%), PreCo (38 vs 24.1%), steroid use (12 vs 3.8%), and med use (48 vs 24.3%) Discussion: Men with prostate cancer requiring ADT have a higher incidence of osteoporosis defined by DXA prior to initiating ADT compared to age-matched cohorts (Hussain et al). Our study revealed ADT with CRF is associated with OsteoFx irrespective of SupplRx or Anti-OsteoRx. Limitations include inability to evaluate efficacy of Anti-OsteoRx due to insufficient power. Conclusion: OsteoFx risk assessment utilizing CRF, FRAX, DXA with timely intervention may prevent OsteoFx in these high-risk patients.

Inclusion of clinical risk factors into NTCP modelling of late rectal toxicity after high dose radiotherapy for prostate cancer

2011 ◽  
Vol 100 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Tiziana Rancati ◽  
Claudio Fiorino ◽  
Gianni Fellin ◽  
Vittorio Vavassori ◽  
Emanuela Cagna ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Clinical Risk Factors ◽  
High Dose ◽  
Rectal Toxicity ◽  
Clinical Risk ◽  
Late Rectal Toxicity ◽  
Ntcp Modelling

scholarly journals A novel DNA methylation score accurately predicts death from prostate cancer in men with low to intermediate clinical risk factors

Oncotarget ◽  
2016 ◽  
Vol 7 (44) ◽  
pp. 71833-71840 ◽  
Author(s):  
Amar S. Ahmad ◽  
Nataša Vasiljević ◽  
Paul Carter ◽  
Daniel M Berney ◽  
Henrik Møller ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Dna Methylation ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Methylation Score

scholarly journals MP53-04 IDENTIFICATION OF HIGH-GRADE PROSTATE CANCER USING URINE-BASED MOLECULAR BIOMARKERS COMBINED WITH CLINICAL RISK FACTORS

2016 ◽  
Vol 195 (4S) ◽  
Author(s):  
Leander Van Neste ◽  
Geert Trooskens ◽  
Rianne J. Hendriks ◽  
Jack Schalken ◽  
Wim Van Criekinge
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Molecular Biomarkers ◽  
Clinical Risk Factors ◽  
High Grade ◽  
Clinical Risk

scholarly journals A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Liang ◽  
Xin Zhi ◽  
Ya Sun ◽  
Huarong Li ◽  
Jiajun Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Diagnostic Performance ◽  
Clinical Risk Factors ◽  
Net Benefit ◽  
Combined Model ◽  
Clinical Model ◽  
Clinical Risk ◽  
Wide Range ◽  
Multiparametric Ultrasound

ObjectivesTo evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).MethodsA total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.ResultsThe multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.ConclusionsClinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

scholarly journals Skin Rash Following Administration of Apalutamide in Japanese Patients with Advanced Prostate Cancer: An Integrated Analysis of the Phase 3 SPARTAN and TITAN Studies and a Phase 1 Open-Label Study

2020 ◽  
Author(s):  
Hiroji Uemura ◽  
Yosuke Koroki ◽  
Yuki Iwaki ◽  
Keiichiro Imanaka ◽  
Takeshi Kambara ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Skin Rash ◽  
Phase 1 ◽  
Japanese Patients ◽  
Clinical Risk Factors ◽  
Integrated Analysis ◽  
Plasma Exposure ◽  
Phase 3 ◽  
Clinical Risk

Abstract Background: A higher incidence of apalutamide-related skin rash has been observed in Japanese patients with prostate cancer (PC). Methods: This integrated analysis of data of Japanese patients from 2 global Phase 3 studies, SPARTAN (NCT01946204; patients with non‑metastatic castration-resistant PC [nmCRPC]) and TITAN (NCT02489318; patients with metastatic castration-sensitive PC [mCSPC]), and the Phase 1 study 56021927PCR1008 (NCT02162836; patients with metastatic CRPC [mCRPC]), assessed clinical risk factors of apalutamide-related skin rash as well as the potential correlation with plasma exposure to apalutamide. Kaplan-Meier method was used for time-to-event analyses. Clinical risk factors for skin rash were assessed using odds ratio. Results: Data from 68 patients ( SPARTAN: n=34, TITAN: n=28, 56021927PCR1008: n=6) receiving apalutamide 240 mg orally once-daily were analyzed. Rash (13 [19.1%]) and maculo-papular rash (11 [16.2%]) were the most frequently reported skin rash. All Grade and Grade 3 skin rash occurred in 35 (51.5%) and 10 (14.7%) patients, respectively. Most (85.7%) skin rash occurred within 4 months of apalutamide initiation and resolved in a median time of 1 month following the use of antihistamines, topical or systemic corticosteroids, with/without apalutamide dose interruptions/reductions. Median time-to-remission of first incidence of rash and maximum grade incidence of rash were 1.0 month (IQR: 0.36-1.81) and 1.0 month (IQR: 0.30-2.43), respectively. No significant clinical risk factors for the incidence of skin rash were observed. Areas under the curve (0-24 hours) (AUC 0-24, ss ) at steady‑state of plasma apalutamide concentration were numerically slightly higher in patients with skin rash than those without. Conclusions: No clinical risk factors for rash could be detected. There is a potential correlation between incidence of skin rash and plasma exposure to apalutamide. In general, apalutamide‑related skin rash is easily managed, with appropriate treatment with or without dose adjustment.

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