AIMC Topic: Biomarkers, Tumor

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Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.

Analytical and bioanalytical chemistry
As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge...

Artificial intelligence for assisted HER2 immunohistochemistry evaluation of breast cancer: A systematic review and meta-analysis.

Pathology, research and practice
Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted treatment options. Nevertheless, pathologists' assessments of HER2 status are less objective than automated, computer-based evaluations. Artificial Intell...

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

Frontiers in immunology
INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients ...

Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

Histopathology
AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (...

Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers.

Clinical pharmacology and therapeutics
Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment...

Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.

Cancer science
The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarker...

Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas.

Cancer research and treatment
PURPOSE: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer.

International immunopharmacology
OBJECTIVE: Bladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the current...

The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized Y glass microspheres SIRT: a preliminary machine learning study.

European journal of nuclear medicine and molecular imaging
BACKGROUND: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes...