Colon Cancer

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images.

We developed a deep learning framework to accurately predict the lymph node status of patients with ...

Comment on "ChatGPT Answers Common Patient Questions About Colonoscopy".

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer.

BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor...

ASO Author Reflections: Could the Application of Machine Learning Enhance the Accuracy of Prognosis Estimation Using Serum Inflammatory Markers in Colorectal Cancer Patients?

Deep learning for the prediction of the chemotherapy response of metastatic colorectal cancer: comparing and combining H&E staining histopathology and infrared spectral histopathology.

Colorectal cancer is a global public health problem with one of the highest death rates. It is the s...

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning.

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobser...

Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images.

PURPOSE: To evaluate the role of quantitative features of intranodular vessels based on deep learnin...

Machine learning and biological evaluation-based identification of a potential MMP-9 inhibitor, effective against ovarian cancer cells SKOV3.

MMP-9, also known as gelatinase B, is a zinc-metalloproteinase family protein that plays a key role ...

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunothe...

Screening of the shared pathogenic genes of ulcerative colitis and colorectal cancer by integrated bioinformatics analysis.

Ulcerative colitis (UC) is one of the high-risk pathogenic factors for colorectal cancer (CRC). Howe...

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lo...

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in...

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitate...

Feasibility of on-demand robotics in colorectal surgery: first cases.

INTRODUCTION: The key benefits of robotics are improved precision and control, thanks to fully artic...

Fovea-UNet: detection and segmentation of lymph node metastases in colorectal cancer with deep learning.

BACKGROUND: Colorectal cancer is one of the most serious malignant tumors, and lymph node metastasis...

Evaluating staging laparoscopy indications for pancreatic cancer based on resectability classification and treatment strategies for patients with positive peritoneal washing cytology.

INTRODUCTION: The prognosis of pancreatic ductal adenocarcinoma (PDAC) in patients with positive per...

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is ...

Efficacy of robot-assisted vs. laparoscopy surgery in the treatment of colorectal cancer: A systematic review and meta-analysis.

PURPOSE: To compare the efficacy of robot-assisted colorectal surgery (RACS) vs. laparoscopic-assist...

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) t...

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, an...

Editorial for "Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study".