Oncology/Hematology

Revolutionizing early detection of TNBC: The rise of non-invasive biomarkers and next-gen diagnostic strategies.

Triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer (BC), which accounts for 10-20 % of BC incidences, and its early diagnosis is pivotal to curing the disease. Tissue biopsies, surgical biopsies, and needle biopsies, currently the preferred methods for acquiring tumor molecular characterization, are invasive and ineffective in capturing the core driving factors of ...

Hitting the Bull's AI: Artificial Intelligence-derived Imaging Features and their Association with Outcomes in CT-guided Lung Biopsy, a Retrospective Study.

PURPOSE: This study aims to evaluate whether quantitative imaging features analyzed by an artificial intelligence (AI) tool are associated with success rate, histopathological results, and complication risks of CT-guided lung biopsies. METHODS: A retrospective study was conducted on 120 CT-guided biopsies of suspicious pulmonary lesions with pathology reports. Associations between technical succes...

Unraveling the molecular mechanisms of air pollution-induced lung adenocarcinoma through machine learning, network toxicology, and molecular docking.

OBJECTIVE: Airborne environmental contaminants are established carcinogens. This investigation elucidates the mechanistic contributions to pulmonary a...

Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.

The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not full...

AI Clinical Decision Tools in Multidisciplinary Team Discussions for Colorectal Cancer.

Multidisciplinary team (MDT) discussions have become a cornerstone of colorectal cancer (CRC) management, integrating the expertise of surgeons, oncol...

Deep-learning/transfer-learning based Overall Survival prediction conditional on Progression-Free Interval with TCGA RNA-seq expression and KEGG-pathways.

We developed a novel deep-learning network that integrated Progression-Free Interval (PFI) as a condition based on transfer learning and fine tuning, ...

Survey on cancer patients' attitudes towards AI and data protection: A cross-sectional study from an Italian cancer center.

Background Artificial Intelligence (AI) is increasingly integrated into oncology, offering opportunities to improve diagnostics, treatment planning, a...

Artificial Intelligence as a Prognostic Tool in Cardiac Amyloidosis: A Review.

Cardiac amyloidosis (CA) poses a significant prognostic challenge due to its varied presentations and frequent delays in identification. While traditi...

Machine Learning-Based Multi-Omics Integration for Identification of Hepatocellular Carcinoma Biomarkers in an Egyptian Cohort.

Hepatocellular carcinoma (HCC) ranks among the most common causes of cancer-related deaths globally. The high incidence of HCC is largely linked to ch...

Artificial Intelligence for the Treatment and Management of Colorectal Liver Metastases.

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Prognosis is significantly worsened in patients with colorectal liv...

Pectin from Phyllanthus emblica L.: Structure elucidation and synergistic in vitro anti-stomach adenocarcinoma effects with 5-fluorouracil.

In recent years, polysaccharides with potential anticancer activity have attracted widespread attention. In this study, a homogeneous polysaccharide f...

Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for...

Machine learning-assisted prognosis of multiple myeloma side population cells via SRGs and OCLR stemness index.

BACKGROUND AND OBJECTIVE: Relapse in Multiple Myeloma, driven by therapy-resistant cancer stem cells, necessitates the development of more specific an...

Multi-cancer framework with cancer-aware attention and adversarial mutual-information minimization for whole slide image classification.

Whole Slide Images (WSIs) are crucial in modern pathology, offering high-resolution data for accurate diagnosis, treatment planning, and research. Dee...

Dermoscopy-guided high-frequency ultrasound: Principles and applications in dermatology.

Conventional skin imaging modalities are often bulky, expensive, and impractical for routine dermatology practice. There is a need for a portable, mul...

Label-free gold nanostar-based SERS with machine learning: A platform for detecting endometrial cancer-associated polyamine metabolites.

The occurrence and progression of endometrial cancer are closely associated with metabolic reprogramming, in which polyamine metabolites play a critic...

A Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer.

BACKGROUND: Prostate cancer remains a leading cause of cancer mortality, yet current risk stratification systems inadequately integrate multidimension...

Decoding Senescence-Driven Heterogeneity in Early-Onset Colorectal Cancer for Prognostic and Therapeutic Stratification.

Early-onset colorectal cancer (EOCRC), diagnosed in patients under 50, is particularly aggressive, yet lacks targeted therapeutic strategies. This stu...

Innovative molecular diagnostic strategy for salivary gland lesions based on ATR-FTIR spectroscopy coupled with machine learning.

OBJECTIVE: This study aimed to identify the biochemical signatures that distinguish pleomorphic adenoma (PA) and mucoepidermoid carcinoma (MEC) from n...

Polyploid giant cancer cells: A novel target in future cancer therapy.

Under intense therapeutic stress-including chemotherapy, radiotherapy, and targeted therapies-tumor cells can undergo ploidy reprogramming to generate...