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Rectal Neoplasms

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The effect of the re-segmentation method on improving the performance of rectal cancer image segmentation models.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patientâs lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with...

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...

Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Radiology. Artificial intelligence
Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic ...

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...

Robot-assisted low anterior resection in a patient with rectal cancer who had a urinary reservoir: A case report.

Asian journal of endoscopic surgery
Undergoing another surgery after a previous abdominal procedure can sometimes result in significant abdominal adhesions. We present a case of robot-assisted low anterior resection in a patient with rectal cancer who had a urinary reservoir. A 65-year...

Robot-assisted laparoscopic surgery for rectal cancer in a patient with a horseshoe kidney: A case report.

Asian journal of endoscopic surgery
A 52-year-old, Japanese man presented to the hospital with a complaint of anal bleeding, and detailed examination resulted in a diagnosis of locally advanced rectal cancer. The patient underwent total neoadjuvant therapy followed by short-course radi...