Colon Cancer

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.

Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and mi...

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.

The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empirical assessment, a certain amount of subjectivity is unavoidable. Furthermore, the reliance on external contrast agents such as hematoxylin and eosin (...

Development and validation of a deep learning-based algorithm for colonoscopy quality assessment.

BACKGROUND: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant ...

Robot-assisted magnetic capsule endoscopy; navigating colorectal inclinations.

To investigate the interaction of a robot assisted magnetically driven wireless capsule endoscope (WCE) with colonic tissue, as it traverses the colo...

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model.

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tis...

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps.

OBJECTIVES: To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer.

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed w...

Single-Port Retzius-Sparing Robot-Assisted Radical Prostatectomy: Feasibility and Early Outcomes.

To determine safety and feasibility of single-port Retzius-sparing robot-assisted radical prostatectomy (SP-rsRARP) using the da Vinci SP (Intuitive ...

Robot-assisted Thoracoscopic Lobectomy of T4 Lung Cancer.

A 79-year-old male former smoker presented with a T4 (>7 cm) adenocarcinoma of the right upper lobe. The patient was staged at clinical T4N0M0 and und...

Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being ...

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and hi...

Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important t...

AI-based carcinoma detection and classification using histopathological images: A systematic review.

Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cance...

The learning curve to attain surgical competency in robotic colorectal surgery.

INTRODUCTION: The aim of the study was to assess the robotic colorectal surgery (RCS) learning curve of an experienced surgeon.

Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models.

Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the...

SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images.

Automated synthesis of histology images has several potential applications including the development of data-efficient deep learning algorithms. In th...

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). Th...

Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer.

BACKGROUND AND PURPOSE: The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and va...

Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video).

BACKGROUND AND AIMS: The optical diagnosis of colorectal cancer (CRC) invasion depth with white light (WL) and image-enhanced endoscopy (IEE) remains ...

The incidence of cardiovascular thrombotic complications after laparoscopic resection in colorectal cancer in Japanese hospitals: A large-scale clinical study.

AIM: The aim of this retrospective study was to investigate the incidence of cardiovascular thrombotic complications after laparoscopic resection in c...