The delineation of the clinical target volume (CTV) is a crucial, laborious and subjective step in cervical cancer radiotherapy. The aim of this study was to propose and evaluate a novel end-to-end convolutional neural network (CNN) for fully automatic and accurate CTV in cervical cancer. A total of 237 computed tomography (CT) scans of patients with locally advanced cervical cancer were collected and evaluated. A novel 2.5D CNN network, called DpnUNet, was developed for CTV delineation and further applied for CTV and organ-at-risk (OAR) delineation simultaneously. Comprehensive comparisons and experiments were performed. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD) and subjective evaluation were used to assess the performance of this method. The mean DSC and 95HD values were 0.86 and 5.34mm for the delineated CTVs. The clinical experts' subjective assessments showed that 90% of the predicted contours were acceptable for clinical usage. The mean DSC and 95HD values were 0.91 and 4.05mm for bladder, 0.85 and 2.16mm for bone marrow, 0.90 and 1.27mm for left femoral head, 0.90 and 1.51mm for right femoral head, 0.82 and 4.29mm for rectum, 0.85 and 4.35mm for bowel bag, 0.82 and 4.96mm for spinal cord respectively. The average delineation time for one patient's CT images was within 15 seconds. The experimental results demonstrate that the CTV and OARs delineated for cervical cancer by DpnUNet was in close agreement with the ground truth. DpnUNet could significantly reduce the radiation oncologists' contouring time.

Intracavitary and/or interstitial brachytherapy is an integral component of the management of patients with central pelvic-recurrent cervical cancer after primary hysterectomy, and is typically delivered using conventional applicators. We investigated the efficacy and safety of three-dimensional (3D)-printed, customizable applicators for those patients. Twenty-six patients were treated with combination external beam radiotherapy and brachytherapy. Patients with lesions ≤1 and >1 cm before brachytherapy were treated with intracavitary and interstitial brachytherapy, respectively. Dosimetric plans were compared between the vaginal cylinder and 3D-printed applicator for the first 9 patients. Outcomes and treatment-related complications were also investigated. The median tumor size before brachytherapy was 0.81 cm. Intracavitary, interstitial, and combined interstitial-intracavitary brachytherapy were performed in 22 (85%), 3 (11%), and 1 (4%) of the patients, respectively. The clinical target volume (CTV) coverage goal was achieved with all 3D-printed plans but failed with three single-channel cylinder plans (33.3%). Owing to 3D-printed transvaginal applicator guidance, there was no need to adjust the needle position after implantation. The mean CTV dose for all patients was 71 ± 8.2 Gy; all met the dose constraints to the organs at risk, but 1 (4%) had a rectal D_2cc overdose. The 2-year local control, progression-free survival, and overall survival rates were 87.8%, 71.0%, and 91.6%, respectively. Four patients (21%) developed early grade 3-4 hematological toxicities and 1 (4%) developed a late grade 3 adverse event. High-quality intracavitary and/or interstitial brachytherapy can be achieved using a 3D-printed applicator and yields favorable outcomes with acceptable toxicity.

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system’s robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system’s potential for real-world application.

Introduction To compare dosimetric parameters of multichannel cylinder (MCC) and 3-dimensional (3D)-printed plans for high-dose-rate vaginal cuff brachytherapy (VBT) and to explore vaginal morphologies after hysterectomy. Material and methods Forty-eight patients with gynecological cancer...

Purpose: To propose a novel deep-learning-based auto-segmentation model for CTV delineation in cervical cancer and to evaluate whether it can perform comparably well to manual delineation by a three-stage multicenter evaluation framework.Methods: An adversarial deep-learning-based auto-segmentation model was trained and configured for cervical cancer CTV contouring using CT data from 237 patients. Then CT scans of additional 20 consecutive patients with locally advanced cervical cancer were collected to perform a three-stage multicenter randomized controlled evaluation involving nine oncologists from six medical centers. This evaluation system is a combination of objective performance metrics, radiation oncologist assessment, and finally the head-to-head Turing imitation test. Accuracy and effectiveness were evaluated step by step. The intra-observer consistency of each oncologist was also tested.Results: In stage-1 evaluation, the mean DSC and the 95HD value of the proposed model were 0.88 and 3.46 mm, respectively. In stage-2, the oncologist grading evaluation showed the majority of AI contours were comparable to the GT contours. The average CTV scores for AI and GT were 2.68 vs. 2.71 in week 0 (P = .206), and 2.62 vs. 2.63 in week 2 (P = .552), with no significant statistical differences. In stage-3, the Turing imitation test showed that the percentage of AI contours, which were judged to be better than GT contours by ≥5 oncologists, was 60.0% in week 0 and 42.5% in week 2. Most oncologists demonstrated good consistency between the 2 weeks (P > 0.05).Conclusions: The tested AI model was demonstrated to be accurate and comparable to the manual CTV segmentation in cervical cancer patients when assessed by our three-stage evaluation framework.