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| 001 | 167183 | ||
| 005 | 20240229133532.0 | ||
| 024 | 7 | _ | |a 10.1016/j.ejrad.2021.109538 |2 doi |
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| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Wang, Xianfeng |0 P:(DE-He78)aec14a1077ed145f9ebe9de1d50905b0 |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Comparison of single-scanner single-protocol quantitative ADC measurements to ADC ratios to detect clinically significant prostate cancer. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2021 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1692790952_30742 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #EA:E010#LA:E010# |
| 520 | _ | _ | |a Mean ADC has high predictive value for the presence of clinically significant prostate cancer (sPC). Measurement variability is introduced by different scanners, protocols, intra-and inter-patient variation. Internal calibration by ADC ratios can address such fluctuations however can potentially lower the biological value of quantitative ADC determination by being sensitive to deviations in reference tissue signal.To better understand the predictive value of quantitative ADC measurements in comparison to internal reference ratios when measured in a single scanner, single protocol setup.284 consecutive patients who underwent 3 T MRI on a single scanner followed by MRI-transrectal ultrasound fusion biopsy were included. A board-certified radiologist retrospectively reviewed all MRIs blinded to clinical information and placed regions of interest (ROI) on all focal lesions and the following reference regions: normal-appearing peripheral zone (PZNL) and transition zone (TZNL), the urinary bladder (BLA), and right and left internal obturator muscle (RIOM, LIOM). ROI-based mean ADC and ADC ratios to the reference regions were compared regarding their ability to predict the aggressiveness of prostate cancer. Spearman's rank correlation coefficient was used to estimate the correlation between ADC parameters, Gleason score (GS) and ADC ratios. The primary endpoint was presence of sPC, defined as a GS ≥ 3 + 4. Univariable and multivariable logistic regression models were constructed to predict sPC. Receiver operating characteristics curves (ROC) were used for visualization; DeLong test was used to evaluate the differences of the area under the curve (AUC). Bias-corrected AUC values and corresponding 95 %-CI were calculated using bootstrapping with 100 bootstrap samples.After exclusion of patients who received prior treatment, 259 patients were included in the final cohort of which 220 harbored 351 MR lesions. Mean ADC and ADC ratios demonstrated a negative correlation with the GS. Mean ADC had the strongest correlation with ρ of -0.34, followed by ADCratioPZNL (ρ=-0.32). All ADC parameters except ADCratioLIOM (p = 0.07) were associated with sPC p<0.05). Mean ADC and ADCratioPZNL had the highest ROC AUC of all parameters (0.68). Multivariable models with mean ADC improve predictive performance.A highly standardized single-scanner mean ADC measurement could not be improved upon using any of the single ADC ratio parameters or combinations of these parameters in predicting the aggressiveness of prostate cancer. |
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| 650 | _ | 7 | |a Apparent diffusion coefficient |2 Other |
| 650 | _ | 7 | |a Gleason score |2 Other |
| 650 | _ | 7 | |a Multiparametric MRI |2 Other |
| 650 | _ | 7 | |a Prostate cancer |2 Other |
| 700 | 1 | _ | |a Hielscher, Thomas |0 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f |b 1 |u dkfz |
| 700 | 1 | _ | |a Radtke, Jan Philipp |0 P:(DE-He78)79897f8897ff77676549d9895258a0f2 |b 2 |u dkfz |
| 700 | 1 | _ | |a Görtz, Magdalena |b 3 |
| 700 | 1 | _ | |a Schütz, Viktoria |b 4 |
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| 700 | 1 | _ | |a Schwab, Constantin |b 7 |
| 700 | 1 | _ | |a Stenzinger, Albrecht |b 8 |
| 700 | 1 | _ | |a Hohenfellner, Markus |b 9 |
| 700 | 1 | _ | |a Schlemmer, Heinz-Peter |0 P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec |b 10 |u dkfz |
| 700 | 1 | _ | |a Bonekamp, David |0 P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93 |b 11 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1016/j.ejrad.2021.109538 |g Vol. 136, p. 109538 - |0 PERI:(DE-600)2005350-2 |p 109538 |t European journal of radiology |v 136 |y 2021 |x 0720-048X |
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