AIMC Topic: Rectal Neoplasms

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Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.

World journal of surgical oncology
BACKGROUND: The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that ...

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

Journal of medical radiation sciences
INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accur...

Imaging segmentation mechanism for rectal tumors using improved U-Net.

BMC medical imaging
OBJECTIVE: In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic segmentation of rectal tumors from an MRI is a great challenge. There are two main shortcomings in ...

Long-term outcomes of robot-assisted laparoscopic surgery versus conventional laparoscopic surgery for rectal cancer: single-center, retrospective, propensity score analyses.

Journal of robotic surgery
Although the short-term outcomes of robot-assisted laparoscopic surgery (RALS) for rectal cancer are well known, the long-term oncologic outcomes of RALS compared with those of conventional laparoscopic surgery (CLS) are not clear. This study aimed t...

Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds.

Techniques in coloproctology
BACKGROUND: Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewe...

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

La Radiologia medica
OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a me...

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

ANZ journal of surgery
BACKGROUND: The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized...

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

BMC cancer
PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed...