Radiomics Models Based on Apparent Diffusion Coefficient Maps for the Prediction of High‐Grade Prostate Cancer at Radical Prostatectomy: Comparison With Preoperative Biopsy

2021 ◽  
Author(s):  
Chao Han ◽  
Shuai Ma ◽  
Xiang Liu ◽  
Yi Liu ◽  
Changxin Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Radical Prostatectomy ◽  
Apparent Diffusion Coefficient ◽  
High Grade ◽  
Preoperative Biopsy ◽  
Apparent Diffusion ◽  
Apparent Diffusion Coefficient Maps

scholarly journals Apparent Diffusion Coefficient Values of the Benign Central Zone of the Prostate: Comparison With Low- and High-Grade Prostate Cancer

2015 ◽  
Vol 205 (2) ◽  
pp. 331-336 ◽  
Author(s):  
Rajan T. Gupta ◽  
Christopher R. Kauffman ◽  
Kirema Garcia-Reyes ◽  
Mark L. Palmeri ◽  
John F. Madden ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Central Zone ◽  
High Grade ◽  
Apparent Diffusion Coefficient Values ◽  
Apparent Diffusion

scholarly journals PO-0905: An individualised TCP model for radiotherapy of prostate cancer based on apparent diffusion coefficient maps

2015 ◽  
Vol 115 ◽  
pp. S467
Author(s):  
O. Carares-Magaz ◽  
U. Van der Heide ◽  
L. Reisæter ◽  
J. Rørvik ◽  
P. Steenbergen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Apparent Diffusion ◽  
Apparent Diffusion Coefficient Maps

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 values are superior to transrectal ultrasound-guided prostate biopsy for the assessment of prostate cancer aggressiveness

2016 ◽  
Vol 58 (2) ◽  
pp. 232-239 ◽  
Author(s):  
Chunmei Li ◽  
Min Chen ◽  
Jianye Wang ◽  
Xuan Wang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Transrectal Ultrasound ◽  
Risk Scores ◽  
High Grade ◽  
Clinical Risk ◽  
Cancer Aggressiveness ◽  
Apparent Diffusion ◽  
Adc Values

Background Few studies have focused on comparing the utility of diffusion-weighted imaging (DWI) and transrectal ultrasound (TRUS)-guided biopsy in predicting prostate cancer aggressiveness. Whether apparent diffusion coefficient (ADC) values can provide more information than TRUS-guided biopsy should be confirmed. Purpose To retrospectively assess the utility of ADC values in predicting prostate cancer aggressiveness, compared to the TRUS-guided prostate biopsy Gleason score (GS). Material and Methods The DW images of 54 patients with biopsy-proven prostate cancer were obtained using 1.5-T magnetic resonance (MR). The mean ADC values of cancerous areas and biopsy GS were correlated with prostatectomy GS and D’Amico clinical risk scores, respectively. Meanwhile, the utility of ADC values in identifying high-grade prostate cancer (with Gleason 4 and/or 5 components in prostatectomy) in patients with a biopsy GS ≤ 3 + 3 = 6 was also evaluated. Results A significant negative correlation was found between mean ADC values of cancerous areas and the prostatectomy GS ( P < 0.001) and D’Amico clinical risk scores ( P < 0.001). No significant correlation was found between biopsy GS and prostatectomy GS ( P = 0.140) and D’Amico clinical risk scores ( P = 0.342). Patients harboring Gleason 4 and/or 5 components in prostatectomy had significantly lower ADC values than those harboring no Gleason 4 and/or 5 components ( P = 0.004). Conclusion The ADC values of cancerous areas in the prostate are a better indicator than the biopsy GS in predicting prostate cancer aggressiveness. Moreover, the use of ADC values can help identify the presence of high-grade tumor in patients with a Gleason score ≤ 3 + 3 = 6 during biopsy.

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