The mode of delivery does not influence the occurrence of post-partum perianal disease flares in patients with inflammatory bowel disease.

INTRODUCTION: Perianal disease occurs in up to 34% of inflammatory bowel disease (IBD) patients. An estimated 25% of women will become pregnant after the initial diagnosis, thus introducing the dilemma of whether mode of delivery affects perianal disease. The aim of our study was to analyze whether a cesarean section (C-section) or vaginal delivery influence perianal involvement. We hypothesized the delivery route would not alter post-partum perianal manifestations in the setting of previously healed perianal disease. METHODS: All consecutive eligible IBD female patients between 1997 and 2022 who delivered were included. Prior perianal involvement, perianal flare after delivery and delivery method were noted. RESULTS: We identified 190 patients with IBD who had a total of 322 deliveries; 169 (52%) were vaginal and 153 (48%) were by C-section. Nineteen women (10%) experienced 21/322 (6%) post-partum perianal flares. Independent predictors were previous abdominal surgery for IBD (OR, 2.7; 95% CI, 1-7.2; p = 0.042), ileocolonic involvement (OR, 3.3; 95% CI, 1.1-9.4; p = 0.030), previous perianal disease (OR, 22; 95% CI, 7-69; p < 0.001), active perianal disease (OR, 96; 95% CI, 21-446; p < 0.001) and biologic (OR, 4.4; 95% CI,1.4-13.6; p < 0.011) or antibiotic (OR, 19.6; 95% CI, 7-54; p < 0.001) treatment. Negative association was found for vaginal delivery (OR, 0.19; 95% CI, 0.06-0.61; p < 0.005). Number of post-partum flares was higher in the C-section group [17 (11%) vs. 4 (2%), p = 0.002]. CONCLUSIONS: Delivery by C-section section was not protective of ongoing perianal disease activity post-delivery, but should be recommended for women with active perianal involvement.

Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac.

A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCTBD1); (2) volumes for sCTBD1plus chest wall and ipsilateral breast (sCTBD2); (3) volumes for sCTBD2plus ribs (sCTBD3); and a deep learning-generated sCT (sCTDL) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCTBD1/sCTBD2/sCTBD3/sCTDLrespectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTVGTVwas 0.15/0.00/0.00/-0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCTDL. Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCTBD2showed the optimal number of bulk-density levels for a bulk-density approach.

From keyhole to sandwich: change in laparoscopic repair of parastomal hernias at a single centre.

BACKGROUND: Nearly 50% of patients with an ostomy will develop a parastomal hernia (PSH). Its repair remains a surgical challenge. Both laparoscopic "modified Sugarbaker" (SB) and Keyhole (KH) repair are currently in use, frequently with unsatisfactory results.''Sandwich Repair'' (SR) may be an alternative to reduce recurrence rates. We present the change of our technique from KH to SR. METHODS: We collected data from all consecutive laparoscopic PSH repairs at our institution from 2004 until now (from 2004 to 2013 treated with KH, from 2014 with SR) and compared the results of the two groups. Primary endpoint was recurrence rate at 1 year. Secondary outcomes were operative time, PO length of hospital stay (LOS), and short and long-term complications. RESULTS: 13 patients underwent SR. Main changes in surgical technique concerned primary defect closure, no stay sutures, use of glue for first mesh fixation, and partial lateral covering of the underlying mesh with a peritoneal flap. Early postoperative course after SR was uneventful and no recurrence at 1 year was recorded. In the KH group (19 patients), short-term complications occurred in two cases (10%), with one parietal hematoma and one case of intensive pain; we had four recurrences at 1 year (21%). LOS was shorter in the SR group (mean 4 days vs 6, p = 0.004). The KH group had 2 (10%) occurrences of chronic seroma and one bowel perforation (5%), while the SR group had one (8%) occurrence of chronic pain. Median follow-up was 26 months (range 13-78) for the SR group and 47 months (12-105) for the KH group. CONCLUSION: SR is safe and effective in expert hands and provides promising preliminary results.

A deep learning method for image-based subject-specific local SAR assessment.

PURPOSE: Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning-based method for image-based subject-specific local SAR assessment. We propose to train a convolutional neural network to learn a "surrogate SAR model" to map the relation between subject-specific B1+ maps and the corresponding local SAR. METHOD: Our database of 23 subject-specific models with an 8-transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex B1+ maps and local SAR distributions were used to train a conditional generative adversarial network. Extra penalization for local SAR underestimation errors was included in the loss function. In silico and in vivo validation were performed. RESULTS: In silico cross-validation shows a good qualitative and quantitative match between predicted and ground-truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations. CONCLUSION: The proposed deep learning method allows online image-based subject-specific local SAR assessment. It greatly reduces the uncertainty in current state-of-the-art SAR assessment methods, reducing the time in the examination protocol by almost 25%.

Fast contour propagation for MR-guided prostate radiotherapy using convolutional neural networks.

PURPOSE: To quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)-guided prostate external-beam radiotherapy. METHODS: Five prostate cancer patients underwent 20 fractions of image-guided external-beam radiotherapy on a 1.5 T MR-Linac system. For each patient, a pretreatment T2-weighted three-dimensional (3D) MR imaging (MRI) scan was used to delineate the clinical target volume (CTV) contours. The same scan was repeated during each fraction, with the CTV contour being manually adapted if necessary. A convolutional neural network (CNN) was trained for combined image registration and contour propagation. The network estimated the propagated contour and a deformation field between the two input images. The training set consisted of a synthetically generated ground truth of randomly deformed images and prostate segmentations. We performed a leave-one-out cross-validation on the five patients and propagated the prostate segmentations from the pretreatment to the fraction scans. Three variants of the CNN, aimed at investigating supervision based on optimizing segmentation overlap, optimizing the registration, and a combination of the two were compared to results of the open-source deformable registration software package Elastix. RESULTS: The neural networks trained on segmentation overlap or the combined objective achieved significantly better Hausdorff distances between predicted and ground truth contours than Elastix, at the much faster registration speed of 0.5 s. The CNN variant trained to optimize both the prostate overlap and deformation field, and the variant trained to only maximize the prostate overlap, produced the best propagation results. CONCLUSIONS: A CNN trained on maximizing prostate overlap and minimizing registration errors provides a fast and accurate method for deformable contour propagation for prostate MR-guided radiotherapy.

Soft-tissue prostate intrafraction motion tracking in 3D cine-MR for MR-guided radiotherapy.

To develop a method to automatically determine intrafraction motion of the prostate based on soft tissue contrast on 3D cine-magnetic resonance (MR) images with high spatial and temporal resolution. Twenty-nine patients who underwent prostate stereotactic body radiotherapy (SBRT), with four implanted cylindrical gold fiducial markers (FMs), had cine-MR imaging sessions after each of five weekly fractions. Each cine-MR session consisted of 55 sequentially obtained 3D data sets ('dynamics') and was acquired over an 11 s period, covering a total of 10 min. The prostate was delineated on the first dynamic of every dataset and this delineation was used as the starting position for the soft tissue tracking (SST). Each subsequent dynamic was rigidly aligned to the first dynamic, based on the contrast of the prostate. The obtained translation and rotation describes the intrafraction motion of the prostate. The algorithm was applied to 6270 dynamics over 114 scans of 29 patients and the results were validated by comparing to previously obtained fiducial marker tracking data of the same dataset. Our proposed tracking method was also retro-perspectively applied to cine-MR images acquired during MR-guided radiotherapy of our first prostate patient treated on the MR-Linac. The difference in the 3D translation results between the soft tissue and marker tracking was below 1 mm for 98.2% of the time. The mean translation at 10 min were X: 0.0 [Formula: see text] 0.8 mm, Y: 1.0 [Formula: see text] 1.8 mm and Z: [Formula: see text] mm. The mean rotation results at 10 min were X: [Formula: see text], Y: 0.1 [Formula: see text] 0.6° and Z: 0.0 [Formula: see text] 0.7°. A fast, robust and accurate SST algorithm was developed which obviates the need for FMs during MR-guided prostate radiotherapy. To our knowledge, this is the first data using full 3D cine-MR images for real-time soft tissue prostate tracking, which is validated against previously obtained marker tracking data.

Magnetic Resonance Imaging only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer.

Magnetic resonance imaging (MRI) is often combined with computed tomography (CT) in prostate radiotherapy to optimise delineation of the target and organs-at-risk (OAR) while maintaining accurate dose calculation. Such a dual-modality workflow requires two separate imaging sessions, and it has some fundamental and logistical drawbacks. Due to the availability of new MRI hardware and software solutions, CT examinations can be omitted for prostate radiotherapy simulations. All information for treatment planning, including electron density maps and bony anatomy, can nowadays be obtained with MRI. Such an MRI-only simulation workflow reduces delineation ambiguities, eases planning logistics, and improves patient comfort; however, careful validation of the complete MRI-only workflow is warranted. The first institutes are now adopting this MRI-only workflow for prostate radiotherapy. In this article, we will review technology and workflow requirements for an MRI-only prostate simulation workflow.

[Preparing children and their parents for surgical interventions in a reconstructive plastic surgery division]. / La preparazione all'intervento chirurgico dei bambini degenti e dei loro genitori in una divisione di chirurgia plastica ricostruttiva.

Fear of the unknown and the misunderstood increases the anxiety of patients of all ages who are going to have operations; in children fear is usually heightened by their fantasies. It is useful therefore to offer them accurate information in a way they can understand. Parents, too, can react better to their anxieties if they clearly know about the procedures to be done. In order to prepare the child patient as well as his parents for surgery, specific programs have been developed in a plastic surgical division.