Incidence of colorectal cancer (CRC) is on rise. While approximately 70% of all CRC cases are sporadic in nature, 20%-25% have familial aggregation and only < 5% is hereditary in origin. Identification of individuals with hereditary predilection for CRC is critical, as it has an impact on their overall surgical management including surgical timing, approach & technique and determines the role of prophylactic surgery and outcome. This review highlights the concept of hereditary CRC, provides insight into its molecular basis, possibility of its application into clinical practice and emphasizes the current treatment strategies with surgical management, based on the available international guidelines.

Urethral injuries (UIs) are significant complications pertaining to transanal total mesorectal excision (TaTME). It is important for surgeons to identify the prostate during TaTME to prevent UI occurrence; intraoperative image navigation could be considered useful in this regard. This study aims at developing a deep learning model for real-time automatic prostate segmentation based on intraoperative video during TaTME. The proposed model's performance has been evaluated.

This was a single-institution retrospective feasibility study. Semantic segmentation of the prostate area was performed using a convolutional neural network (CNN)-based approach. DeepLab v3 plus was utilized as the CNN model for the semantic segmentation task. The Dice coefficient (DC), which is calculated based on the overlapping area between the ground truth and predicted area, was utilized as an evaluation metric for the proposed model.

Five hundred prostate images were randomly extracted from 17 TaTME videos, and the prostate area was manually annotated on each image. Fivefold cross-validation tests were performed, and as observed, the average DC value equaled 0.71 ± 0.04, the maximum value being 0.77. Additionally, the model operated at 11 fps, which provides acceptable real-time performance.

To the best of the authors' knowledge, this is the first effort toward realization of computer-assisted TaTME, and results obtained in this study suggest that the proposed deep learning model can be utilized for real-time automatic prostate segmentation. In future endeavors, the accuracy and performance of the proposed model will be improved to enable its use in practical applications, and its capability to reduce UI risks during TaTME will be verified.

The aim of this study was to demonstrate highlighting of the urethra during surgery through the use of two different methods: a new near-infrared fluorophore IRDye800BK, and indocyanine green (ICG) mixed with silicone.

Male cadavers from the department of anatomy at the University of Oxford were used to visualise the urethra during near-infrared fluorescence excitation. To assess IRDye800BK, a perineal incision was utilised after infiltrating the urethra directly with an IRDye800BK solution mixed with Instillagel. ICG-silicone was assessed when the urethra was purposely exposed as part of a simulated transanal total mesorectal dissection. ICG was previously mixed with ethanol and silicone and left to set in a Foley catheter. Fluorescence was visualised using an in-house manufactured fluorescence-enabled laparoscopic system.

IRDye800BK demonstrated excellent penetration and visualisation of the urethra under fluorescence at an estimated tissue depth of 2 cm. An ICG-silicone catheter demonstrated excellent fluorescence without leaving any residual solution behind in the urethra after its removal.

The newly described ICG-silicone method opens up the possibility of new technologies in this area of fluorescence guided surgery. IRDye800BK is a promising alternative to ICG in visualising the urethra using fluorescence imaging. Its greater depth of penetration may allow earlier detection of the urethra during surgery and prevent wrong plane surgery sooner.