AIMC Topic: Colorectal Neoplasms

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Clinical actionability of triaging DNA mismatch repair deficient colorectal cancer from biopsy samples using deep learning.

EBioMedicine
BACKGROUND: We aimed to develop a deep learning (DL) model to predict DNA mismatch repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained whole-slide images (WSIs) and assess its clinical applicability.

Application Effect of Robot-Assisted Laparoscopy in Hepatectomy for Colorectal Cancer Patients with Liver Metastases.

Computational and mathematical methods in medicine
OBJECTIVE: Application effect of Leonardo's robot-assisted laparoscopy in hepatectomy for colorectal cancer patients with liver metastases.

Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer.

Applied immunohistochemistry & molecular morphology : AIMM
Colorectal cancer (CRC) is the third most common cancer worldwide. Although clinical outcome varies among patients diagnosed within the same TNM stage it is the cornerstone in treatment decisions as well as follow-up programmes. Tumor-infiltrating ly...

Robotic and laparoscopic surgical procedures for colorectal cancer.

Journal of robotic surgery
The study aims to investigate perioperative indices and immediate outcomes of laparoscopic and robotic surgical interventions in colorectal cancer patients. The study included 163 patients [90 (55.2%) females and 73 (44.8%) males, aged 67.46 ± 6.72 y...

ZhenQi FuZheng formula inhibits the growth of colorectal tumors by modulating intestinal microflora-mediated immune function.

Aging
Zhenqi Fuzheng formula (ZQFZ), of which the main ingredients are and , has immune system regulatory functions and potential anti-tumor bioactivity. The inhibition of colorectal tumor growth by ZQFZ was analyzed in inflammatory cells and B6/JGpt-/Gpt...

A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images.

Computers in biology and medicine
A clinically comparable Convolutional Neural Network framework-based technique for performing automated classification of cancer grades and tissue structures in hematoxylin and eosin-stained colon histopathological images is proposed in this paper. I...

Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage. However, outcomes are varied even among patients receiving similar treatments. We aimed to develop...

Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning.

Contrast media & molecular imaging
This study aimed to explore the value of abdominal computerized tomography (CT) three-dimensional reconstruction using the dense residual single-axis super-resolution algorithm in the diagnosis of nonperitonealized colorectal cancer (CC). 103 patient...

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

Scientific reports
High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from gene...

Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes.

International journal of cancer
Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC ...