Improved Sensitivity and Specificity for Detection of Prostate Cancer

2007 ◽  
Author(s):  
Rao P. Gullapalli ◽  
Michael Naslund ◽  
John Papasdimitrou ◽  
Elliot Siegel
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity

scholarly journals Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neda Gholizadeh ◽  
Peter B. Greer ◽  
John Simpson ◽  
Jonathan Goodwin ◽  
Caixia Fu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Transition Zone ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Spectroscopic Imaging ◽  
Multiparametric Mri ◽  
Low Risk ◽  
Intermediate Risk

Abstract Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

scholarly journals Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3664
Author(s):  
Islam R. Abdelmaksoud ◽  
Ahmed Shalaby ◽  
Ali Mahmoud ◽  
Mohammed Elmogy ◽  
Ahmed Aboelfetouh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Transfer Learning ◽  
Sensitivity And Specificity ◽  
Benign Tumors ◽  
B Values ◽  
Cad System ◽  
Average Accuracy ◽  
Apparent Diffusion ◽  
Invasive Techniques ◽  
Fine Tune

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

scholarly journals Can mean ADC value and ADC ratio of benign prostate tissue to prostate cancer assist in the prediction of clinically significant prostate cancer within the PI-RADSv2 scoring system?

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Samar Ramzy Ragheb ◽  
Reem Hassan Bassiouny
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Gleason Score ◽  
Treatment Strategies ◽  
Serum Levels ◽  
Prostatic Tissue ◽  
Histopathological Analysis ◽  
Adc Value ◽  
The Mean ◽  
Clinically Significant

Abstract Background The aim of this study is to investigate whether quantitative DW metrics can provide additive value to the reliable categorization of lesions within existing PI-RADSv2 guidelines. Fifty-eight patients with clinically suspicious prostate cancer who underwent PR examination, PSA serum levels, sextant TRUS-guided biopsies, and bi-parametric MR imaging were included in the study. Results Sixty-six lesions were detected by histopathological analysis of surgical specimens. The mean ADC values were significantly lower in tumor than non-tumor tissue. The mean ADC value inversely correlated with Gleason score of tumors with a significant p value < 0.001.Conversely, a positive relationship was found between the ADC ratio (ADC of benign prostatic tissue to prostate cancer) and the pathologic Gleason score with a significant elevation of the ADC ratio along with an increase of the pathologic Gleason score (p < 0.001). ROC curves constructed for the tumor ADC and ADC ratio helped to distinguish pathologically aggressive (Gleason score ≥ 7) from non-aggressive (Gleason score ≤ 6) tumors and to correlate it with PIRADSv2 scoring to predict the presence of clinically significant PCA (PIRADSv2 DW ≥ 4). The ability of the tumor ADC and ADC ratio to predict highly aggressive tumors (GS> 7) was high (AUC for ADC and ADC ratio, 0.946 and 0.897; p = 0.014 and 0.039, respectively). The ADC cut-off value for GS ≥ 7 was < 0.7725 and for GS ≤ 6 was > 0.8620 with sensitivity and specificity 97 and 94%. The cutoff ADC ratio for predicting (GS > 7) was 1.42 and for GS ≤ 6 was > 1.320 with sensitivity and specificity 97 and 92%. By applying this ADC ratio cut-off value the sensitivity and specificity of reader 1 for correct categorization of PIRADSv2 DW > 4 increased from 90 and 68% to 95 and 90% and that of reader 2 increased from 94 and 88% to 97 and 92%, respectively. Conclusion Estimation of DW metrics (ADC and ADC ratio between benign prostatic tissue and prostate cancer) allow the non-invasive assessment of biological aggressiveness of prostate cancer and allow reliable application of the PIRADSv2 scoring to determine clinically significant cancer (DW score > 4) which may contribute in planning initial treatment strategies.

scholarly journals Clinical diagnosis of prostate cancer using digital rectal examination and prostate-specific antigen tests: a systematic review and meta-analysis of sensitivity and specificity

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Nelson C. Okpua ◽  
Simon I. Okekpa ◽  
Stanley Njaka ◽  
Augusta N. Emeh
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Antigen Test ◽  
Rectal Examination ◽  
Prostate Specific Antigen Test ◽  
Prostate Cancers ◽  
Antigen Tests

Abstract Background Being diagnosed with cancer, irrespective of type initiates a serious psychological concern. The increasing rate of detection of indolent prostate cancers is a source of worry to public health. Digital rectal examination and prostate-specific antigen tests are the commonly used prostate cancer screening tests. Understanding the diagnostic accuracies of these tests may provide clearer pictures of their characteristics and values in prostate cancer diagnosis. This review compared the sensitivities and specificities of digital rectal examination and prostate-specific antigen test in detection of clinically important prostate cancers using studies from wider population. Main body We conducted literature search in PubMed, Medline, Science Direct, Wiley Online, CINAHL, Scopus, AJOL and Google Scholar, using key words and Boolean operators. Studies comparing the sensitivity and specificity of digital rectal examination and prostate-specific antigen tests in men 40 years and above, using biopsy as reference standard were retrieved. Data were extracted and analysed using Review manager (RevMan 5.3) statistical software. The overall quality of the studies was good, and heterogeneity was observed across the studies. The result comparatively shows that prostate-specific antigen test has higher sensitivity (P < 0.00001, RR 0.74, CI 0.67–0.83) and specificity (P < 0.00001, RR 1.81, CI 1.54–2.12) in the detection of prostate cancers than digital rectal examination. Conclusion Prostate-specific antigen test has higher sensitivity and specificity in detecting prostate cancers from men of multiple ethnic origins. However, combination of prostate-specific antigen test and standardized digital rectal examination procedure, along with patients history, may improve the accuracy and minimize over-diagnoses of indolent prostate cancers.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals Prostate-Specific Antigen Density: A Measurement to Differentiate Benign Hypertrophy of Prostate from Prostate Carcinoma

2020 ◽  
Vol 12 (01) ◽  
pp. 44-48
Author(s):  
Chandan Kumar Nath ◽  
Bhupen Barman ◽  
Pranjal Phukan ◽  
Stephen L. Sailo ◽  
Biswajit Dey ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Prostate Specific Antigen Density ◽  
Significant Difference ◽  
The Mean ◽  
Set Up ◽  
The Individual

Abstract Background Determination of isolated prostate-specific antigen (PSA) in asymptomatic individuals has not demonstrated sufficient sensitivity and specificity to be useful in the routine evaluation of prostate disease. To enhance the accuracy of serum PSA we have used a proportion of serum PSA and prostate volume, which we refer to as prostate-specific antigen density (PSAD). Prostate volume in this study was calculated using transrectal ultrasonography (TRUS). Materials and Methods A total of 106 patients with prostatic disease clinically confined to the prostate glands were evaluated. Results and Observation The mean PSAD for prostate cancer was 0.15 ± 0.01 while that for benign hypertrophy of the prostate (BPH) was 0.11 ± 0.02 (p < 0.05). Significant difference (p < 0.05) was noted in the prostate volume in these two groups with the mean prostate volume measured by TRUS in the BPH to be 53.85 ± 9.71 mL compared with 58.14 ± 7.48 mL in the carcinoma. PSA density of 0.13 ng/mL can be used as a cutoff for the individual in our set-up who should go for prostate biopsy with sensitivity and specificity of over 90%. Conclusion These results suggest that PSAD may be useful in distinguishing BPH and prostate cancer.

scholarly journals Impact of the integration of proton magnetic resonance imaging spectroscopy to PI-RADS 2 for prediction of high grade and high stage prostate cancer

2017 ◽  
Vol 50 (5) ◽  
pp. 299-307 ◽  
Author(s):  
Michael S. Leapman ◽  
Zhen J. Wang ◽  
Spencer C. Behr ◽  
John Kurhanewicz ◽  
Ronald J. Zagoria ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Sensitivity And Specificity ◽  
Proton Magnetic Resonance ◽  
Imaging Spectroscopy ◽  
Roc Curves ◽  
High Grade ◽  
Prostate Imaging ◽  
Gleason Pattern ◽  
High Stage

Abstract Objective: To compare the predictions of dominant Gleason pattern ≥ 4 or non-organ confined disease with Prostate Imaging Reporting and Data System (PI-RADS v2) with or without proton magnetic resonance spectroscopic imaging (1H-MRSI). Materials and Methods: Thirty-nine men underwent 3-tesla endorectal multiparametric MRI including 1H-MRSI and prostatectomy. Two radiologists assigned PI-RADS v2 and 1H-MRSI scores to index lesions. Statistical analyses used logistic regressions, receiver operating characteristic (ROC) curves, and 2x2 tables for diagnostic accuracies. Results: The sensitivity and specificity of 1H-MRSI and PI-RADS v2 for high-grade prostate cancer (PCa) were 85.7% (57.1%) and 92.9% (100%), and 56% (68.0%) and 24.0% (24.0%). The sensitivity and specificity of 1H-MRSI and PI-RADS v2 for extra-prostatic extension (EPE) were 64.0% (40%) and 20.0% (48%), and 50.0% (57.1%) and 71.4% (64.3%). The area under the ROC curves (AUC) for prediction of high-grade prostate cancer were 0.65 and 0.61 for PI-RADS v2 and 0.72 and 0.70 when combined with 1H-MRSI (readers 1 and 2, p = 0.04 and 0.21). For prediction of EPE the AUC were 0.54 and 0.60 for PI-RADS v2 and 0.55 and 0.61 when combined with 1H-MRSI (p > 0.05). Conclusion: 1H-MRSI might improve the discrimination of high-grade prostate cancer when combined to PI-RADS v2, particularly for PI-RADS v2 score 4 lesions, but it does not affect the prediction of EPE.

scholarly journals Improved sensitivity and specificity for prostate cancer diagnosis based on the urine PCA3/PSA ratio acquired by sequence-specific RNA capture

2015 ◽  
Vol 34 (5) ◽  
pp. 2439-2444 ◽  
Author(s):  
KEWEN ZHENG ◽  
YALING DOU ◽  
LINFU HE ◽  
HANZHONG LI ◽  
ZHICAI ZHANG ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Cancer Diagnosis ◽  
Prostate Cancer Diagnosis

scholarly journals Routine PSA Testing: An Analysis of the Controversy Concerning Its Use

2005 ◽  
Vol 5 ◽  
pp. 147-150 ◽  
Author(s):  
Namath S. Hussain
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Diagnostic Tool ◽  
Psa Screening ◽  
Psa Testing ◽  
Important Diagnostic Tool

Prostate cancer is the leading cause of cancer in American males today. PSA screening has been used for over 10 years as an important diagnostic tool for the disease. Because of its lack of sensitivity and specificity, however, PSA testing should be used with caution.

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