AIMC Topic: Rectal Neoplasms

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SP rTaTME: initial clinical experience with single-port robotic transanal total mesorectal excision (SP rTaTME).

Techniques in coloproctology
BACKGROUND: The technical difficulty and steep learning curve of transanal total mesorectal excision (taTME) has limited widespread adoption. The single-port (SP) daVinci robot is designed to facilitate single-incision and natural-orifice translumina...

Comparison of the short-term operative, Oncological, and Functional Outcomes between two types of robot-assisted total mesorectal excision for rectal cancer: Da Vinci versus Micro Hand S surgical robot.

The international journal of medical robotics + computer assisted surgery : MRCAS
OBJECTIVE: This study aimed to evaluate the difference of two various robotic technology applied in R- Total mesorectal excision (TME).

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Current medical science
Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly different from colon cancer in terms of treatment, prognosis, and metastasis. Recently, the differential...

Robotic versus laparoscopic total mesorectal excision for rectal cancer: a meta-analysis of long-term survival and urogenital functional outcomes.

Minerva gastroenterology
INTRODUCTION: Robotic surgical technology has been widely introduced and applied in various fields of surgery. The aim of this study was to analyze long-term oncological and urogenital functional outcomes following laparoscopic/robotic total mesorect...

Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.

Surgical endoscopy
BACKGROUND: Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require in...

Predicting treatment response from longitudinal images using multi-task deep learning.

Nature communications
Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simul...

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Radiology
Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring. Purpose To report the development and initial patient study of an imaging system compri...