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Colorectal Neoplasms

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Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of pa...

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Journal of imaging informatics in medicine
Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In th...

Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence.

Clinica chimica acta; international journal of clinical chemistry
Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-...

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning.

BMC cancer
BACKGROUND: Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

Validation of artificial intelligence-based bowel preparation assessment in screening colonoscopy (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Accurate bowel preparation assessment is essential for determining colonoscopy screening intervals. Patients with suboptimal bowel preparation are at a high risk of missing >5 mm adenomas and should undergo an early repeat colono...

Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital patho...

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.

European journal of radiology
OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning a...

Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?

PharmacoEconomics
OBJECTIVES: Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemother...

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weig...

A probabilistic knowledge graph for target identification.

PLoS computational biology
Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone....