Colon Cancer

Boundary Constraint Network With Cross Layer Feature Integration for Polyp Segmentation.

Clinically, proper polyp localization in endoscopy images plays a vital role in the follow-up treatment (e.g., surgical planning). Deep convolutional neural networks (CNNs) provide a favoured prospect for automatic polyp segmentation and evade the limitations of visual inspection, e.g., subjectivity and overwork. However, most existing CNNs-based methods often provide unsatisfactory segmentation p...

Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach.

Numerous studies have been conducted to elucidate the relation of tumor proximity to cancer prognosis and treatment efficacy in colorectal cancer. However, the molecular pathways and prognoses of left- and right-sided colorectal cancers are different, and this difference has not been fully investigated at the genomic level. In this study, a set of data science approaches, including six feature sel...

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples.

The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard dia...

Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning Approaches.

Histopathological identification of tumor tissue is one of the routine pathological diagnoses for pathologists. Recently, computational pathology has ...

Double-Balanced Loss for Imbalanced Colorectal Lesion Classification.

Colorectal cancer has a high incidence rate in all countries around the world, and the survival rate of patients is improved by early detection. With ...

The involvement of gut microbiota in the anti-tumor effect of carnosic acid via IL-17 suppression in colorectal cancer.

Colorectal cancer (CRC) is a malignant tumor that threatens human health worldwide. Disturbance of the gut microbiota caused by various external facto...

Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer.

BACKGROUND: The treatment strategies for colorectal cancer (CRC) must ensure a radical cure of cancer and prevent over/under treatment. Biopsy specime...

Transformer-based unsupervised contrastive learning for histopathological image classification.

A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large an...

The Use of Deep Learning-Based Computer Diagnostic Algorithm for Detection of Lymph Node Metastases of Gastric Adenocarcinoma.

The diversifying modalities of treatment for gastric cancer raise urgent demands for the rapid and precise diagnosis of metastases in regional lymph ...

Automated histological classification for digital pathology images of colonoscopy specimen via deep learning.

Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validat...

Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer.

BACKGROUND: When endoscopically resected specimens of early colorectal cancer (CRC) show high-risk features, surgery should be performed based on curr...

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung a...

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.

BACKGROUND: High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with ...

Consensus-led recommendations defining practical principles of achieving optimal surgical outcomes in robotic colorectal surgery in the Asia-Pacific region.

Recent innovations within the field of robotic surgery have particular relevance to colorectal surgery. Although a robotic approach has been associate...

Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.

INTRODUCTION: Computer-aided detection (CADe) helps increase colonoscopic polyp detection. However, little is known about other performance metrics li...

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.

PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence...

HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening.

Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. ...

Predictors of operative difficulty in robotic low anterior resection for rectal cancer.

AIM: This study evaluates the relationship of tumour and anatomical features with operative difficulty in robotic low anterior resection performed by ...

Warning from artificial intelligence against inaccurate polyp size estimation.

Robotic right colectomy with robotic-sewn anastomosis: a pilot case series.

The background of this study is to assess the feasibility, clinical utility and safety of intra-corporeal robotic-sewn anastomosis (ICrA) in completel...