Efficacy of High-Resolution Preoperative 3D Reconstructions for Lesion Localization in Oncological Colorectal Surgery—First Pilot Study

Developing a phantom for simulating robotic-assisted complete mesocolic excision using 3D printing and medical imaging

BACKGROUND

Robotic-assisted complete mesocolic excision is an advanced procedure mainly because of the great variability in anatomy. Phantoms can be used for simulation-based training and assessment of competency when learning new surgical procedures. However, no phantoms for robotic complete mesocolic excision have previously been described. This study aimed to develop an anatomically true-to-life phantom, which can be used for training with a robotic system situated in the clinical setting and can be used for the assessment of surgical competency.

METHODS

Established pathology and surgical assessment tools for complete mesocolic excision and specimens were used for the phantom development. Each assessment item was translated into an engineering development task and evaluated for relevance. Anatomical realism was obtained by extracting relevant organs from preoperative patient scans and 3D printing casting moulds for each organ. Each element of the phantom was evaluated by two experienced complete mesocolic excision surgeons without influencing each other's answers and their feedback was used in an iterative process of prototype development and testing.

RESULTS

It was possible to integrate 35 out of 48 procedure-specific items from the surgical assessment tool and all elements from the pathological evaluation tool. By adding fluorophores to the mesocolic tissue, we developed an easy way to assess the integrity of the mesocolon using ultraviolet light. The phantom was built using silicone, is easy to store, and can be used in robotic systems designated for patient procedures as it does not contain animal-derived parts.

CONCLUSIONS

The newly developed phantom could be used for training and competency assessment for robotic-assisted complete mesocolic excision surgery in a simulated setting.

The use of mixed reality in the preoperative planning of colorectal surgery: Preliminary experience with a narrative review

New advanced technologies have recently been developed and preliminarily applied to surgery, including virtual reality (VR), augmented reality (AR) and mixed reality (MR). We retrospectively review all colorectal cases in which we used holographic 3D reconstruction from February 2020 to December 2022. This innovative approach was used to identify vascular anomalies, pinpoint tumor locations, evaluate infiltration into neighboring organs and devise surgical plans for both training and educating trainee assistants. We have also provided a state-of-the-art analysis, briefly highlighting what has been stated by the scientific literature to date. VR facilitates training and anatomical assessments, while AR enhances training and laparoscopic performance evaluations. MR, powered by HoloLens, enriches anatomic recognition, navigation, and visualization. Successful implementation was observed in 10 colorectal cancer cases, showcasing the effectiveness of MR in improving preoperative planning and its intraoperative application. This technology holds significant promise for advancing colorectal surgery by elevating safety and reliability standards.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm³ (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.