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

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.

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Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations

Frontiers in Neurology ◽

10.3389/fneur.2021.767165 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shaozhi Zhao ◽

Qi Zhao ◽

Yuming Jiao ◽

Hao Li ◽

Jiancong Weng ◽

...

Keyword(s):

Risk Factors ◽

Clinical Features ◽

Arteriovenous Malformations ◽

Clinical Usefulness ◽

Clinical Risk Factors ◽

Combined Model ◽

Clinical Model ◽

Clinical Risk ◽

Brain Arteriovenous Malformations ◽

Tof Mra

Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy.Methods: This retrospective study enrolled 176 patients with unruptured bAVMs. After manual lesion segmentation, a total of 858 radiomics features were extracted from time-of-flight magnetic resonance angiography (TOF-MRA). A radiomics model was constructed, and a radiomics score was calculated. Meanwhile, the demographic and angioarchitectural characteristics of patients were assessed to build a clinical model. Incorporating the radiomics score and independent clinical risk factors, a combined model was constructed. The performance of the models was assessed with respect to discrimination, calibration, and clinical usefulness.Results: The clinical model incorporating 3 clinical features had an area under the curve (AUC) of 0.71. Fifteen radiomics features were used to build the radiomics model, which had a higher AUC of 0.78. Incorporating the radiomics score and clinical risk factors, the combined model showed a favorable discrimination ability and calibration, with an AUC of 0.82. Decision curve analysis (DCA) demonstrated that the combined model outperformed the clinical model and radiomics model in terms of clinical usefulness.Conclusions: The radiomics features extracted from TOF-MRA were associated with epilepsy in patients with unruptured bAVMs. The radiomics-clinical nomogram, which was constructed based on the model incorporating the radiomics score and clinical features, showed favorable predictive efficacy for bAVM-related epilepsy.

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

The Journal of Urology ◽

10.1097/01.ju.0000152408.25738.23 ◽

2005 ◽

Vol 173 (3) ◽

pp. 732-736 ◽

Cited By ~ 39

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

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

Journal of the Endocrine Society ◽

10.1210/jendso/bvab048.495 ◽

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.

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