AIMC Topic: Neoplasm Metastasis

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Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials.

JCO clinical cancer informatics
PURPOSE: Cetuximab (CET), targeting the epidermal growth factor receptor, is a systemic treatment option for patients with colorectal cancer. One known predictive factor for CET efficacy is the presence of CET-related rash; other putative toxicity fa...

MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers.

BMC medical informatics and decision making
BACKGROUND: Analysis of Electronic Health Records (EHRs) is crucial in real-world evidence (RWE), especially in oncology, as it provides valuable insights into the complex nature of the disease. The implementation of advanced techniques for automated...

Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients.

Scientific reports
Muscle-Invasive Bladder Cancer (MIBC) is a more aggressive disease than non-muscle-invasive bladder cancer (NMIBC), with greater chances of metastasis. We sought to develop machine learning (ML) models to predict metastasis and prognosis in MIBC pati...

A machine learning tool for identifying metastatic colorectal cancer in primary care.

Scandinavian journal of primary health care
BACKGROUND: Detection of colorectal cancer (CRC) is mainly achieved by clinical assessment. As new treatments become available for metastatic CRC (MCRC), it is important to accurately identify these patients.

The Extent to Which Artificial Intelligence Can Help Fulfill Metastatic Breast Cancer Patient Healthcare Needs: A Mixed-Methods Study.

Current oncology (Toronto, Ont.)
The Artificial Intelligence Patient Librarian (AIPL) was designed to meet the psychosocial and supportive care needs of Metastatic Breast Cancer (MBC) patients with HR+/HER2- subtypes. AIPL provides conversational patient education, answers user ques...

Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer.

Oncogene
CDK4/6 inhibitors in combination with endocrine therapy are widely used to treat HR+/HER2- metastatic breast cancer leading to improved progression-free survival (PFS) compared to single agent endocrine therapy. Over 300 patients receiving standard-o...

Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.

PloS one
More than 90% of deaths due to breast cancer (BC) are due to metastasis-related complications, with invasive ductal carcinoma (IDC) of the breast being the most common pathologic type of breast cancer and highly susceptible to metastasis to distant o...

A machine learning-based investigation of integrin expression patterns in cancer and metastasis.

Scientific reports
Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, a comprehensive understanding of these roles has not been achieved due to the complex relationships between ...

Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Scientific reports
Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed scar tissue. Artificial intelligence (AI) technologies can extract a wide range of tumor features from large datasets helping in oncological decision-ma...

M-NET: Transforming Single Nucleotide Variations Into Patient Feature Images for the Prediction of Prostate Cancer Metastasis and Identification of Significant Pathways.

IEEE journal of biomedical and health informatics
High-performance prediction of prostate cancer metastasis based on single nucleotide variations remains a challenge. Therefore, we developed a novel biologically informed deep learning framework, named M-NET, for the prediction of prostate cancer met...