Apparent Diffusion Coefficient Value and Ratio as Noninvasive Potential Biomarkers to Predict Prostate Cancer Grading: Comparison With Prostate Biopsy and Radical Prostatectomy Specimen

2015 ◽  
Vol 204 (3) ◽  
pp. 550-557 ◽  
Author(s):  
Francesco De Cobelli ◽  
Silvia Ravelli ◽  
Antonio Esposito ◽  
Francesco Giganti ◽  
Andrea Gallina ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Radical Prostatectomy ◽  
Apparent Diffusion Coefficient ◽  
Prostate Biopsy ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion ◽  
Prostatectomy Specimen ◽  
Cancer Grading ◽  
Potential Biomarkers

Apparent diffusion coefficient value is a strong predictor of unsuspected aggressiveness of prostate cancer before radical prostatectomy

2016 ◽  
Vol 34 (10) ◽  
pp. 1389-1395 ◽  
Author(s):  
Raphaele Renard Penna ◽  
Geraldine Cancel-Tassin ◽  
Eva Comperat ◽  
Pierre Mozer ◽  
Priscilla Léon ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Radical Prostatectomy ◽  
Apparent Diffusion Coefficient ◽  
Strong Predictor ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion

scholarly journals Assessment of Apparent Diffusion Coefficient Values as Predictor of Aggressiveness in Peripheral Zone Prostate Cancer: Comparison with Gleason Score

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Shayan Sirat Maheen Anwar ◽  
Zahid Anwar Khan ◽  
Rana Shoaib Hamid ◽  
Fahd Haroon ◽  
Raza Sayani ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Gleason Score ◽  
Case Series ◽  
Adc Value ◽  
Apparent Diffusion Coefficient Values ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion ◽  
Adc Values

Purpose. To determine association between apparent diffusion coefficient value on diffusion-weighted imaging and Gleason score in patients with prostate cancer. Methods. This retrospective case series was conducted at Radiology Department of Aga Khan University between June 2009 and June 2011. 28 patients with biopsy-proven prostate cancer were included who underwent ultrasound guided sextant prostate biopsy and MRI. MRI images were analyzed on diagnostic console and regions of interest were drawn. Data were entered and analyzed on SPSS 20.0. ADC values were compared with Gleason score using one-way ANOVA test. Results. In 28 patients, 168 quadrants were biopsied and 106 quadrants were positive for malignancy. 89 lesions with proven malignancy showed diffusion restriction. The mean ADC value for disease with a Gleason score of 6 was 935 mm2/s (SD=248.4 mm2/s); Gleason score of 7 was 837 mm2/s (SD=208.5 mm2/s); Gleason score of 8 was 614 mm2/s (SD=108 mm2/s); and Gleason score of 9 was 571 mm2/s (SD=82 mm2/s). Inverse relationship was observed between Gleason score and mean ADC values. Conclusion. DWI and specifically quantitative ADC values may help differentiate between low-risk (Gleason score, 6), intermediate-risk (Gleason score, 7), and high-risk (Gleason score 8 and 9) prostate cancers, indirectly determining the aggressiveness of the disease.

scholarly journals Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

2021 ◽  
Vol 10 ◽  
Author(s):  
Jinke Xie ◽  
Basen Li ◽  
Xiangde Min ◽  
Peipei Zhang ◽  
Chanyuan Fan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Diffusion Coefficient ◽  
Radical Prostatectomy ◽  
Apparent Diffusion Coefficient ◽  
Texture Features ◽  
Training Cohort ◽  
Machine Learning Methods ◽  
Significant Difference ◽  
Apparent Diffusion

ObjectiveTo evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).Materials and methodsFifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed.ResultsSix texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort.ConclusionA combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

Apparent diffusion coefficient value as a biomarker reflecting morphological and biological features of prostate cancer

2013 ◽  
Vol 46 (3) ◽  
pp. 555-561 ◽  
Author(s):  
Hyeyeol Bae ◽  
Soichiro Yoshida ◽  
Yoh Matsuoka ◽  
Hiroshi Nakajima ◽  
Eisaku Ito ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Biological Features ◽  
Apparent Diffusion Coefficient Value ◽  
Apparent Diffusion
Sign in / Sign up

Export Citation Format

Share Document