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Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.
Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response h…
FULLTEXT: Machine learning -
Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning.
Increasing evidence suggests that Graves' disease (GD) may increase the risk of thyroid cancer (THCA), but diagnostic biomarkers associated with it remain underexplored. To address this issue, we analyzed the Gene Expression Omnibus (GEO) a…
FULLTEXT: Machine learning -
Genetic relationships between the gut microbiota and prostate cancer: Mendelian randomization combined with bioinformatics analysis.
[BACKGROUND] Prostate cancer (PCa) is a leading cause of male cancer-related death globally. While the gut microbiota is linked to PCa, its genetic association remains unclear. [METHODS] We screened genetic instruments related to the gut m…
FULLTEXT: Machine learning -
Development and validation of machine learning prognostic models for overall survival in non-surgical prostate cancer patients with bone metastases.
[OBJECTIVE] To construct and interpret a machine learning model for predicting overall survival in nonsurgical prostate cancer with bone metastases (PCBM). [METHODS] Data from 3,378 SEER database patients were utilized to develop machine l…
FULLTEXT: Machine learning -
Neutrophil extracellular trap-related genes in PTCL: identification, prognosis and drug interaction prediction via bioinformatics-machine learning.
[OBJECTIVE] This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). [METHODS] Differentially expressed N…
FULLTEXT: Machine learning -
A vision-language model-based approach for lung cancer diagnosis using lossless 3D CT images: evaluation of GPT-4.1 and GPT-4o for patient-level malignancy assessment.
[PURPOSE] Large vision-language models (VLMs), such as GPT-4.1 and GPT-4o, have shown strong potential in medical tasks. However, their application in lossless 3D medical image analysis is still underexplored. This study proposes a GPT-base…
FULLTEXT: Machine learning -
Predicting lung cancer survival with attention-based CT slices combination.
Accurate prognosis of Non-Small Cell Lung Cancer (NSCLC) is crucial for enhancing patient care and treatment outcomes. Despite the advancements in deep learning, the task of overall survival prediction in NSCLC has not fully leveraged these…
FULLTEXT: Machine learning -
Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.
[BACKGROUND] Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But …
FULLTEXT: Machine learning -
A data fusion deep learning approach for accurate organelle-based classification of cancer cells.
[PURPOSE] Microscopy-based cancer cell classification traditionally relies on cell-based morphological features, while subcellular organelle organization remains underutilized. Existing machine learning methods often require manual preproce…
FULLTEXT: Machine learning -
Artificial intelligence-assisted FTIR spectroscopy for hormone receptor subtyping in formalin-fixed breast Cancer tissues.
[BACKGROUND] Determination of estrogen receptor (ER) and progesterone receptor (PR) status is critical for breast cancer subtyping and guiding endocrine therapy. Although immunohistochemistry (IHC) remains the diagnostic gold standard, it i…
FULLTEXT: Machine learning -
Emerging frontiers in cervical cancer diagnostics: Recent innovations in biosensors for HPV detection.
Cervical cancer (CC) is recognized as a significant health concern impacting females worldwide. The pathogenicity of CC is associated with human papillomavirus (HPV). Timely diagnosis, therefore, becomes imperative for reducing mortality an…
FULLTEXT: Machine learning -
High confidence Raman spectroscopy of tumor biomarker proteins through experimental and theoretical cross-validation.
Cancer represents a significant challenge to people's health and safety. Tumor biomarker detection plays a vital role in the precise diagnosis of cancer and finds widespread applications in cancer screening and pathological diagnosis. Exist…
FULLTEXT: Machine learning -
Smart graphene-enhanced ceramic material refractive index sensor simulation design developed for highly sensitive breast Cancer detection optimized with machine learning.
Based on the surface plasmon resonance (SPR) technique, the proposed biosensor is investigated as an SPR-based sensing platform for detecting breast cancer cells, specifically MCF-7 and MDA-MB-231 cells. Developed biosensor features an octa…
FULLTEXT: Machine learning -
An advanced diagnostic framework for discriminating lung cancer tissue subtypes via the synergy of fourier transform infrared spectroscopy and random forest.
Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniques are often time-consuming and heavily dependent on the operator's subjective judgment an…
FULLTEXT: Machine learning -
Integration of deep learning and radiomic features from multiplex immunohistochemistry images for reproducible Multi-Outcome prediction in a Multi-Center study of colorectal cancer.
[OBJECTIVE] To develop and validate a robust, multimodal machine learning framework integrating radiomic and deep learning features from multiplex immunohistochemistry (mIHC) images for comprehensive outcome prediction in colorectal cancer …
FULLTEXT: Machine learning -
Artificial intelligence in biomaterials for oral oncology.
Oral cancer and oral potentially malignant disorders (OPMDs) remain a significant challenge in diagnosis and therapy, primarily due to inherent limitations in early detection, targeted treatment, and postoperative rehabilitation. Convention…
FULLTEXT: Machine learning -
Artificial intelligence in clinical oncology: Multimodal integration and translational development.
Artificial intelligence (AI) is rapidly reshaping clinical oncology, as cancer care increasingly relies on integrating heterogeneous data streams spanning radiology, digital pathology, genomics, and longitudinal electronic health records. H…
FULLTEXT: Machine learning -
'See' through the surface: surface-derived three-dimensional AI-driven real-time imaging solution for intra-treatment image guidance.
Respiratory motion is a long-standing challenge for lung stereotactic body radiotherapy (SBRT), particularly for centrally located lung tumors where increased toxicity demands more precise motion management during treatment. Current two-dim…
FULLTEXT: Machine learning -
Rapid detection of drug-resistant leukemia cell using an optofluidic chip and machine learning.
[BACKGROUND] Rapid detection of drug-resistant leukemia played a crucial role in formulating appropriate treatment plans for patients and improving their prognosis. In this research, an integrated optofluidic platform was developed to detec…
FULLTEXT: Machine learning -
Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1.
[BACKGROUND] Non-small cell lung cancer (NSCLC) is a common type of lung cancer with poor prognosis and high mortality in advanced stages. Although secreted phosphoprotein 1 (SPP1) is associated with the progression of NSCLC, its specific m…
FULLTEXT: Machine learning