scholarly journals Serum miRNAs Support the Indication for MRI-Ultrasound Fusion-Guided Biopsy of the Prostate in Patients with Low-PI-RADS Lesions

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1315
Author(s):  
Bastian Keck ◽  
Angelika Borkowetz ◽  
Julia Poellmann ◽  
Thilo Jansen ◽  
Moritz Fischer ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Binary Logistic Regression ◽  
Serum Levels ◽  
Patient Counseling ◽  
Grade Group ◽  
Logistic Regression Models ◽  
Serum Mirna ◽  
Group 2 ◽  
Clinically Significant ◽  
Additional Value

Multiparametric MRI (mpMRI) and targeted biopsy of the prostate enhance the tumor detection rate. However, the prediction of clinically significant prostate cancer (PCa) is still limited. Our study tested the additional value of serum levels of selected miRNAs in combination with clinical and mpMRI information for PCa prediction and classification. A total of 289 patients underwent targeted mpMRI-ultrasound fusion-guided prostate biopsy complemented by systematic biopsy. Serum miRNA levels of miRNAs (miR-141, miR-375, miR-21-5p, miR-320b, miR-210-3p, let-7c, and miR-486) were determined by quantitative PCR. Detection of any PCa and of significant PCa were the outcome variables. The patient age, pre-biopsy PSA level, previous biopsy procedure, PI-RADS score, and serum miRNA levels were covariates for regularized binary logistic regression models. The addition of miRNA expression of miR-486 and let-7c to the baseline model, containing only clinical parameters, increased the predictive accuracy. Particularly in patients with PI-RADS ≤3, we determined a sensitivity for detecting significant PCa (Gleason score≥7a corresponding to Grade group ≥2) of 95.2%, and an NPV for absence of significant PCa of 97.1%. This accuracy could be useful to support patient counseling in selected cases.

scholarly journals The Diagnostic Yield of Magnetic Resonance Cholangiopancreatography in the Setting of Acute Pancreaticobiliary Disease — A Single Center Experience

2021 ◽  
pp. 084653712110137
Author(s):  
Sultan Yahya ◽  
Abdullah Alabousi ◽  
Peri Abdullah ◽  
Milita Ramonas
Keyword(s):  
Magnetic Resonance ◽  
Diagnostic Yield ◽  
Binary Logistic Regression ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Significant Discrepancy ◽  
Logistic Regression Models ◽  
Single Center Experience ◽  
New Information ◽  
Clinically Significant ◽  
Enzyme Elevation

Purpose: To discern whether preceding ultrasound (US) results, patient demographics and biochemical markers can be implemented as predictors of an abnormal Magnetic Resonance Cholangiopancreatography (MRCP) study in the context of acute pancreaticobiliary disease. Methods: A retrospective study was performed assessing US results, age, gender, elevated lipase and biliary enzymes for consecutive patients who underwent an urgent MRCP following an initial US for acute pancreaticobiliary disease between January 2017-December 2018. Multivariable binary logistic regression models were constructed to assess for predictors of clinically significant MRCPs, and discrepant US/MRCP results. Results: A total of 155 patients (mean age 56, 111 females) were included. Age (OR 1.03, P < 0.05), hyperlipasemia (OR 5.33, P < 0.05) and a positive US (OR 40.75, P < 0.05) were found to be independent predictors for a subsequent abnormal MRCP. Contrarily, gender and elevated biliary enzymes were not reliable predictors of an abnormal MRCP, or significant MRCP/US discrepancies. Of 66 cases (43%) of discordant US/MRCPs, half had clinically significant discrepant findings such as newly discovered choledocholithiasis and pancreaticobiliary neoplasia. Age was the sole predictor for a significant US/MRCP discrepancy, with 2% increase in the odds of a significant discrepancy per year of increase in age. Conclusion: An abnormal US, hyperlipasemia and increased age serve as predictors for a subsequent abnormal MRCP, as opposed to gender and biliary enzyme elevation. Age was the sole predictor of a significant US/MRCP discrepancy that provided new information which significantly impacted subsequent management. In the remaining cases, however, MRCP proved useful in reaffirming the clinical diagnosis and avoiding further investigations.

scholarly journals Patient-level detection of grade group ≥2 prostate cancer using quantitative diffusion MRI

2021 ◽  
Author(s):  
Allison Y Zhong ◽  
Leonardino A Digma ◽  
Troy Hussain ◽  
Christine H Feng ◽  
Christopher C Conlin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Characteristic Curve ◽  
Grade Group ◽  
Bootstrap Confidence Intervals ◽  
Patient Level ◽  
Apparent Diffusion ◽  
Spectrum Imaging ◽  
Group 2 ◽  
Clinically Significant ◽  
Group 1

Purpose: Multiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa. Materials and Methods: Retrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone. Results: 151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (p<0.0001) were superior to ADC for patient-level detection of csPCa. The performance of RSIrs was comparable to that of PI-RADS (p=0.6). AUC for PI-RADS+RSIrs was 0.84 [0.77, 0.90], superior to PI-RADS or RSIrs, alone (p=0.008, p=0.009). Conclusions: RSIrs was superior to conventional ADC and comparable to (routine, clinical) PI-RADS for patient-level detection of csPCa. The combination of PI-RADS and RSIrs was superior to either alone. RSIrs is a promising quantitative marker worthy of prospective study in the setting of csPCa detection.

Evaluation of Proclarix, a prostate cancer risk score, used together with magnetic resonance imaging for the diagnosis of clinically significant prostate cancer.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 278-278
Author(s):  
Hayley Pye ◽  
Hashim Ahmed ◽  
Susan Heavey ◽  
Urszula Stopka-Farooqui ◽  
Edward Johnston ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Risk Score ◽  
Prostate Cancer Risk ◽  
Significant Advance ◽  
Resonance Imaging ◽  
Grade Group ◽  
Group 2 ◽  
Clinically Significant

278 Background: The use of multi-parametric magnetic resonance imaging (mpMRI) has been a significant advance in the diagnosis of prostate cancer (PCa) recommended in a number of guidelines. There are considerable resource implications in scanning all men at risk of PCa. Furthermore, a significant number of mpMRIs are reported as indeterminate, leading to unnecessary biopsies. Proclarix is a CE-marked test based on two novel biomarkers, thrombospondin 1 (THBS1) and cathepsin D (CTSD), combined with PSA and age. A software algorithm returns a risk score that can be used as an aid in the identification of clinically significant PCa (any Grade Group 2 or greater). We aimed to assess the potential of Proclarix to identify those men who could safely avoid an upfront mpMRI or those men who could avoid biopsy when the mpMRI was indeterminate. Methods: Proclarix was correlated retrospectively with diagnostic data from 282 men recruited in the INNOVATE study (NCT02689271). INNOVATE involved men undergoing mpMRI followed by targeted and systematic biopsies in those with a suspicious mpMRI. Results: Median age and PSA were 66 (IQR 59-70) and 5.4 (3.8-7.8) ng/mL. 182 (65%) men underwent biopsy and 78 (43%) had GG≥2 PCa. Application of Proclarix in all 282 men undergoing mpMRI resulted in a sensitivity for clinically significant PCa (GG≥2) of 91%, a negative predictive value (NPV) of 92% and 38% specificity. When normalized to the same sensitivity of 91%, %fPSA resulted in both lower NPV (89%) and specificity (28%) when compared to Proclarix. 144 (51%) men had an indeterminate mpMRI of whom 84 (58%) had a biopsy and 13 (15%) had GG≥2 PCa. In these men, Proclarix had an NPV of 100%, at 100% sensitivity and a specificity of 34%. When results were compared using equal sensitivity, PSA density (cut-off 0.05 ng/mL), which is frequently used to inform the need for biopsy, had 10% specificity. Conclusions: The use of Proclarix could potentially allow 38% of men to avoid undergoing an mpMRI. In men with an indeterminate mpMRI, Proclarix could allow one-third to safely avoid biopsies without missing any clinically significant cancer.

MRI-based nomogram for the prediction of prostate cancer diagnosis: A multi-centre validated patient–physician decision tool

2022 ◽  
pp. 205141582110659
Author(s):  
Edwin M Chau ◽  
Beth Russell ◽  
Aida Santaolalla ◽  
Mieke Van Hemelrijck ◽  
Stuart McCracken ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Gleason Grade ◽  
Grade Group ◽  
Group 2 ◽  
Clinically Significant ◽  
Significant Disease

Objective: To update and externally validate a magnetic resonance imaging (MRI)-based nomogram for predicting prostate biopsy outcomes with a multi-centre cohort. Materials and methods: Prospective data from five UK-based centres were analysed. All men were biopsy naïve. Those with missing data, no MRI, or prostate-specific antigen (PSA) > 30 ng/mL were excluded. Logistic regression analysis was used to confirm predictors of prostate cancer outcomes including MRI-PIRADS (Prostate Imaging Reporting and Data System) score, PSA density, and age. Clinically significant disease was defined as International Society of Urological Pathology (ISUP) Grade Group ⩾ 2 (Gleason grade ⩾ 7). Biopsy strategy included transrectal and transperineal approaches. Nomograms were produced using logistic regression analysis results. Results: A total of 506 men were included in the analysis with median age 66 (interquartile range (IQR) = 60–69). Median PSA was 6.6 ng/mL (IQR = 4.72–9.26). PIRADS ⩾ 3 was reported in 387 (76.4%). Grade Group ⩾ 2 detection was 227 (44.9%) and 318 (62.8%) for any cancer. Performance of the MRI-based nomogram was an area under curve (AUC) of 0.84 (95% confidence interval (CI) = 0.81–0.88) for Grade Group ⩾ 2% and 0.85 (95% CI = 0.82–0.88) for any prostate cancer. Conclusion: We present external validation of a novel MRI-based nomogram in a multi-centre UK-based cohort, showing good discrimination in identifying men at high risk of having clinically significant disease. These findings support this risk calculator use in the prostate biopsy decision-making process. Level of evidence: 2c

Clinical utility of PSA density and PI-RADS for deferring biopsy for the detection of clinically significant prostate cancer.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 298-298
Author(s):  
Matthew Truong ◽  
Erica Stevens ◽  
Ryan Ward ◽  
Jennifer Bullen ◽  
Ethan Austhof ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Models ◽  
Diagnostic Group ◽  
Data Systems ◽  
Grade Group ◽  
Prostate Mri ◽  
Logistic Regression Models ◽  
Psa Density ◽  
Clinically Significant

298 Background: We sought to identify PSA density (PSAD) and Prostate Imaging-Reporting and Data Systems (PI-RADS) category cut-offs that would allow deferring biopsy in men with suspicion for clinically significant prostate cancer (csPCa). Methods: Our institution’s prostate MRI registry (n = 1718) was queried for patients who had MRI-guided biopsy (MRI-GB) and/or systematic biopsy (SB) performed after prostate MRI between January 2013 and October 2018 (n = 676). Patients in the diagnostic group (either biopsy naïve or with prior negative biopsy) and patients with PCa on active surveillance (AS) were considered eligible. PSA, PSAD, and PI-RADS category were entered into logistic regression models for predicting csPca (grade group [GG] ≥ 2) at biopsy. Receiver operating characteristic (ROC) analysis was performed to assess model accuracy and results were stratified by biopsy indication and PI-RADS categories. Results: Logistic regression models that combined PSAD and PI-RADS categories had the highest ROC’s in both the diagnostic and AS groups (AUC=0.830 and 0.778, respectively). For diagnostic group patients with PSAD ≤0.15, csPCa was found in 6/89 (6.7%) of negative MRI patients (i.e. PI-RADS ≤ 2), 4/90 (4.4%) of PI-RADS 3 patients, 59/159 (37%) of PI-RADS 4-5 patients. If a PSAD cutoff of ≤ 0.15 and PI-RADS category ≤ 3 MRI were used in combination as criteria for biopsy deferral, only 10/526 (1.9%) of patients would have had csPCa missed on subsequent biopsy. Among patients in the AS group with a negative MRI, 0/22(0%) and 3/8 (37.5%) had csPCA if the PSAD was ≤0.15 and >0.15, respectively. Conclusions: For the diagnostic group of patients undergoing prostate biopsy, PSAD cut off ≤0.15 is useful for deferring biopsy only in patients with a PI-RADS ≤ 3. Confirmatory biopsy in patients should be strongly considered before enrolling patients in AS even in the setting of a negative MRI if the PSAD is > 0.15.

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

scholarly journals Association between multimorbidity patterns and disability among older people covered by long-term care insurance in Shanghai, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zijing Wang ◽  
Wenjia Peng ◽  
Mengying Li ◽  
Xinghui Li ◽  
Tingting Yang ◽  
...  
Keyword(s):  
Older People ◽  
Functional Disability ◽  
Long Term Care ◽  
Strong Association ◽  
Binary Logistic Regression ◽  
Term Care ◽  
Logistic Regression Models ◽  
Disease Pattern ◽  
Long Term Care Insurance

Abstract Background Functional disability and multimorbidity are common among older people. However, little is known about the relationship between functional disability and different multimorbidity combinations. We aimed to identify multimorbidity patterns and explore the associations between these patterns and functional disability. Methods We investigated a multi-stage random sample of 1871 participants aged ≥60 years and covered by long-term care insurance in Shanghai, China. Multimorbidity was defined as the simultaneous presence of two or more chronic diseases in an individual. Participants completed scales to assess basic and instrumental activities of daily living (BADL and IADL, respectively). Multimorbidity patterns were identified via exploratory factor analysis. Binary logistic regression models were used to determine adjusted associations between functional disability and number and patterns of multimorbidity. Results Multimorbidity was present in 74.3% of participants. The prevalence of BADL disability was 50.7% and that of IADL disability was 90.7%. There was a strong association between multimorbidity and disability. We identified three multimorbidity patterns: musculoskeletal, cardio-metabolic, and mental-degenerative diseases. The cardio-metabolic disease pattern was associated with both BADL (OR 1.28, 95%CI 1.16–1.41) and IADL (OR 1.41, 95%CI 1.19–1.68) disability. The mental-degenerative disease pattern was associated with BADL disability (OR 1.55, 95%CI 1.40–1.72). Conclusions Multimorbidity and functional disability are highly prevalent among older people covered by long-term care insurance in Shanghai, and distinct multimorbidity patterns are differentially associated with functional disability. Appropriate long-term healthcare and prevention strategies for older people may help reduce multimorbidity, maintain functional ability, and improve health-related quality of life.

scholarly journals The number of household members as a risk factor for peptic ulcer disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mi Hong Yim ◽  
Keun Ho Kim ◽  
Bum Ju Lee
Keyword(s):  
Peptic Ulcer ◽  
Peptic Ulcer Disease ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Living Alone ◽  
Cross Sectional ◽  
Ulcer Disease ◽  
Logistic Regression Models ◽  
Household Members ◽  
H Pylori

AbstractPeptic ulcer disease (PUD) is caused by many sociodemographic and economic risk factors other than H. pylori infection. However, no studies reported an association between PUD and the number of household members. We showed the number of family members affected by PUD based on sex in a Korean population. This cross-sectional study used 1998–2009 data from the Korea National Health and Nutrition Examination Survey of the Korea Centers for Disease Control and Prevention. Multiple binary logistic regression models adjusted for confounders were constructed to analyze the association of PUD with the number of household members. The number of household members was associated with PUD, age, body mass index (BMI), waist circumference, systolic blood pressure, hemoglobin, glucose, location (urban/rural), income, education level, stress, current drinking, and smoking in both sexes. Men with other household members had a higher PUD risk compared to men or women living alone (reference), and the opposite was observed for women. Men with 4 household members had a higher PUD risk than men living alone in the model adjusted for age, BMI, income, location, education, and stress (OR = 2.04 [95% CI 1.28–3.27], p value = .003). Women with more than 6 household members had a lower PUD risk than women living alone in the adjusted model (OR = 0.50 [0.33–0.75], p value = .001). Women with more household members had a lower PUD risk. However, more men had PUD than women regardless of the number of household members.

scholarly journals Development of a chimeric odour blend for attracting gravid malaria vectors

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Betelehem Wondwosen ◽  
Mengistu Dawit ◽  
Yared Debebe ◽  
Habte Tekie ◽  
Sharon R. Hill ◽  
...  
Keyword(s):  
Vector Control ◽  
Binary Logistic Regression ◽  
Ground Level ◽  
Malaria Vectors ◽  
Volatile Components ◽  
Innovative Strategy ◽  
Breeding Sites ◽  
Logistic Regression Models ◽  
Plant Feeding ◽  
Global Vector

Abstract Background Odour-based tools targeting gravid malaria vectors may complement existing intervention strategies. Anopheles arabiensis are attracted to, and stimulated to oviposit by, natural and synthetic odours of wild and domesticated grasses associated with mosquito breeding sites. While such synthetic odour lures may be used for vector control, these may have limited efficacy when placed in direct competition with the natural source. In this study, workflows developed for plant-feeding pests was used to design and evaluate a chimeric odour blend based on shared attractive compounds found in domesticated grass odours. Methods Variants of a synthetic odour blend, composed of shared bioactive compounds previously identified in domesticated grasses, was evaluated sequentially in a two-choice olfactometer to identify a ratio-optimized attractive blend for malaria vectors. During this process, blends with ratios that were significantly more attractive than the previously identified synthetic rice blend were compared to determine which was most attractive in the two-choice olfactometer. To determine whether all volatile components of the most attractive blend were necessary for maximal attraction, subtractive assays were then conducted, in which individual components were removed for the most attractive blend, to define the final composition of the chimeric blend. Binary logistic regression models were used to determine significance in all two-choice assays. The chimeric blend was then assessed under field conditions in malaria endemic villages in Ethiopia, to assess the effect of dose, trap type, and placement relative to ground level. Field data were analyzed both descriptively and using a Welch-corrected t-test. Results A ratio-optimized chimeric blend was identified that significantly attracted gravid An. arabiensis under laboratory conditions. In the field, trap captures of An. arabiensis and Anopheles pharoensis were dependent on the presence of the lure, trap type (CDC, BG Sentinel and Suna traps), placement relevant to ground level, with low release rates generally luring more mosquitoes. Conclusions The workflow designed for the development of chimeric lures provides an innovative strategy to target odour-mediated behaviours. The chimeric lure identified here can be used in existing trapping systems, and be customized to increase sustainability, in line with goals of the Global Vector Control Response Group.

Naive patients with suspicious prostate cancer and positive multiparametric magnetic resonance imaging (mp-MRI): is it time for fusion target biopsy alone?

2021 ◽  
pp. 205141582110237
Author(s):  
Enrico Checcucci ◽  
Sabrina De Cillis ◽  
Daniele Amparore ◽  
Diletta Garrou ◽  
Roberta Aimar ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Detection Rate ◽  
Resonance Imaging ◽  
Fusion Biopsy ◽  
Grade Group ◽  
Final Pathology ◽  
Multiparametric Magnetic Resonance Imaging ◽  
Clinically Significant

Objectives: To determine if standard biopsy still has a role in the detection of prostate cancer or clinically significant prostate cancer in biopsy-naive patients with positive multiparametric magnetic resonance imaging. Materials and methods: We extracted, from our prospective maintained fusion biopsy database, patients from March 2014 to December 2018. The detection rate of prostate cancer and clinically significant prostate cancer and complication rate were analysed in a cohort of patients who underwent fusion biopsy alone (group A) or fusion biopsy plus standard biopsy (group B). The International Society of Urological Pathology grade group determined on prostate biopsy with the grade group determined on final pathology among patients who underwent radical prostatectomy were compared. Results: Prostate cancer was found in 249/389 (64.01%) and 215/337 (63.8%) patients in groups A and B, respectively ( P=0.98), while the clinically significant prostate cancer detection rate was 57.8% and 55.1% ( P=0.52). No significant differences in complications were found. No differences in the upgrading rate between biopsy and final pathology finding after radical prostatectomy were recorded. Conclusions: In biopsy-naive patients, with suspected prostate cancer and positive multiparametric magnetic resonance imaging the addition of standard biopsy to fusion biopsy did not increase significantly the detection rate of prostate cancer or clinically significant prostate cancer. Moreover, the rate of upgrading of the cancer grade group between biopsy and final pathology was not affected by the addition of standard biopsy. Level of evidence: Not applicable for this multicentre audit.

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