Prostate MRI for Improving Personalized Risk Prediction of Incontinence and Surgical Planning: The Role of Membranous Urethral Length Measurements and the Use of 3D Models

Boellaard, Thierry N.; Hagens, Marinus J.; Veerman, Hans; Yakar, Derya; Mertens, Laura S.; Heijmink, Stijn W. T. P. J.; Poel, Henk G. van der; Leeuwen, Pim J. van; Schoots, Ivo G.; Haan, Margriet C van Dijk-de

doi:10.3390/life13030830

Cited by 3 publications

(3 citation statements)

References 61 publications

(96 reference statements)

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“…We acknowledge that MRI measurement might have moderate interrater agreement which is a bias for the generalization of our results based on a single rater [ 39 , 44 ], an issue which may soon be solved by automated MRI measurements [ 45 ]. We attempted to mitigate this limitation by following a standardized protocol for midsagittal MUL measurements [ 20 , 21 ] and as such we believe it does not affect our conclusions [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Membranous urethral length is the single independent predictor of urinary continence recovery at 12 months following Retzius-sparing robot-assisted radical prostatectomy

Fonseca,

Moraes-Fontes,

Sousa

et al. 2024

J Robotic Surg

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The influence of anatomical parameters on urinary continence (UC) after Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains uncharted. Our objective was to evaluate their association with UC at 3, 6 and 12 months post-operatively. Data from patients who underwent RS-RARP were prospectively collected. Continence was defined as no pad use. Anatomic variables were measured on preoperative magnetic resonance imaging (MRI). Regression analyses were performed to identify predictors of UC at each time point. We included 158 patients with a median age of 60 years, most of whom had a localized tumor (≤ cT2). On multivariate analyses, at 3 months post-surgery, urinary incontinence (UI) rises with age, odds ratio (OR) 1.07 [95% confidence interval (CI) 1.004−1.142] and with prostate volume (PV), OR 1.029 (95% CI 1.006−1.052); it reduces with longer membranous urethral length (MUL), OR 0.875 (95% CI 0.780−0.983) and with higher membranous urethral volume (MUV), OR 0.299 (95% CI 0.121−0.737). At 6 months, UI rises with PV, OR 1.033 (95% CI 1.011−1.056) and decreases with MUV, OR 0.1504 (95% CI 0.050−0.444). Significantly, at 12 months post-surgery, the only predictor of UI is MUL, OR 0.830 (95% CI 0.706−0.975), establishing a threshold associated with a risk of UI of 5% (MUL > 15 mm) in opposition to a risk of 25% (MUL < 10 mm). This single institutional study requires external validation. To our knowledge, this is the first prospective cohort study supporting MUL as the single independent predictor of UC at 12 months post-surgery. By establishing MUL thresholds, we enable precise patient counseling.

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Section: Discussionmentioning

confidence: 99%

Membranous urethral length is the single independent predictor of urinary continence recovery at 12 months following Retzius-sparing robot-assisted radical prostatectomy

Fonseca,

Moraes-Fontes,

Sousa

et al. 2024

J Robotic Surg

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“…The methodology presented using 3D Slicer software together with 3D models could also be used in planning treatment of other conditions, e.g., in patients with high-riding dislocation developmental dysplasia of the hip [35,39] and even in cases where custom-made implants are used, as software for segmentation of bone structures [40]. Furthermore, the use of these techniques is not limited to pelvic surgery, but could be applied to spinal surgery for the purpose of planning pedicle insertion and osteotomy procedures in cases of spinal deformity [41], craniofacial surgery to prepare custom-made titanium mandibular protheses [42], as well as cancer treatment and the use of MRI-based three-dimensional models, e.g., planning of radical prostatectomy [43].…”

Section: Discussionmentioning

confidence: 99%

Optimization of Revision Hip Arthroplasty Workflow by Means of Detailed Pre-Surgical Planning Using Computed Tomography Data, Open-Source Software and Three-Dimensional-Printed Models

et al. 2023

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Background. In revision hip arthroplasty (RHA), establishing the center of rotation (COR) can be technically challenging due to the acetabular bone destruction that is usually present, particularly in severe cases such as Paprosky type II and III defects. The aim of this study was to demonstrate the use of open-source medical image reconstruction software and low-cost 3D anatomical models in pre-surgical planning of RHA. Methods. A total of 10 patients, underwent RHA and were included in the study. Computed tomography (CT) scans were performed for all cases, before surgery and approximately 1 week after the procedure. The reconstruction of CT data, 3D virtual planning of the COR and positioning of acetabular cups, including their inclination and anteversion angles, was carried out using the free open source software platform 3D Slicer. In addition, anatomical models of the pelvis were built on a desktop 3D printer from polylactic acid (PLA). Preoperative and postoperative reconstructed imaging data were compared for each patient, and the position of the acetabular cups as well as the COR were evaluated for each case. Results. Analysis of the pre- and post-op center of rotation position data indicated statistically insignificant differences for the location of the COR on the X-axis (1.5 mm, t = 0.5741, p = 0.5868) with a fairly strong correlation of the results (r = −0.672, p = 0.0982), whilst for the location of the COR in the Y and Z-axes, there was statistical dependence (Y axis, 4.7 mm, t = 3.168 and p = 0.0194; Z axis, 1.9 mm, t = 1.887 and p = 0.1081). A strong correlation for both axes was also observed (Y and Z) (Y-axis, r = 0.9438 and p = 0.0014; Z-axis, r = 0.8829 and p = 0.0084). Analysis of inclination angle values showed a statistically insignificant difference between mean values (3.9 degrees, t = 1.111, p = 0.3092) and a moderate correlation was found between mean values (r = −0.4042, p = 0.3685). Analysis of the anteversion angle showed a statistically insignificant difference between mean values (1.9 degrees, t = 0.8671, p = 0.4192), while a moderate correlation between mean values was found (r = −0.4782, p = 0.2777). Conclusions. Three-dimensional reconstruction software, together with low-cost anatomical models, are very effective tools for pre-surgical planning, which have great potential use in orthopedic surgery, particularly RHA. In up and in- and up and out-type defects, it is essential to establish a new COR and to identify three support points within the revision acetabulum in order to correctly position acetabular cups.

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“…Despite this improvement, Boellaard et al [ 8 ] emphasized the need for artificial intelligence (AI) to further improve interobserver agreement through automated measurements of pelvic parameters. AI models can be used to segment relevant structures and improve the consistency and efficiency of radiologists on prostate MRI [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy

van den Berg,

Spaans,

Wessels

et al. 2024

Eur Radiol Exp

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Background Pelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans. Methods Preoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland–Altman plot. Results The DL models achieved median Dice similarity coefficient (DSC) values of 0.85–0.97 for coronal structures and 0.87–0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28–0.83) to 0.91 (95% CI 0.84–0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42–0.87) to 0.86 (95% CI 0.75–0.92) and for the levator ani muscle from 0.40 (95% CI 0.05–0.66) to 0.61 (95% CI 0.31–0.78). Conclusions DL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans. Relevance statement The implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models. Key points • DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists. • Membranous urethral length measurement improved from substantial to almost perfect agreement. • Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models. Graphical Abstract

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Prostate MRI for Improving Personalized Risk Prediction of Incontinence and Surgical Planning: The Role of Membranous Urethral Length Measurements and the Use of 3D Models

Cited by 3 publications

References 61 publications

Membranous urethral length is the single independent predictor of urinary continence recovery at 12 months following Retzius-sparing robot-assisted radical prostatectomy

Membranous urethral length is the single independent predictor of urinary continence recovery at 12 months following Retzius-sparing robot-assisted radical prostatectomy

Optimization of Revision Hip Arthroplasty Workflow by Means of Detailed Pre-Surgical Planning Using Computed Tomography Data, Open-Source Software and Three-Dimensional-Printed Models

Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy

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