Oncology/Hematology

A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis.

Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid prolifer...

CRISPR-Cas12a/Cas13a in cancer molecular diagnosis.

Cancer remains a leading cause of global mortality, with early diagnosis being pivotal for improving...

The utility of large language models in oncological multidisciplinary team meetings: A systematic review.

Large language models (LLMs) have emerged in recent years as innovative artificial intelligence syst...

Artificial intelligence for accurate tumor size assessment and non-invasive adenocarcinoma prediction in small-sized lung cancer.

INTRODUCTION: Accurate preoperative imaging is essential for improving the treatment of small lung c...

The dual role of the crosstalk between autophagy and ferroptosis in lung cancer treatment: Advances in mechanisms and therapeutic strategies (Review).

The interaction between autophagy and ferroptosis has resulted in the identification of novel approa...

Treatment of pulmonary fibrosis: From disease mechanisms to future novel therapies (Review).

Pulmonary fibrosis (PF) is a progressive and fatal interstitial lung disease characterized by irreve...

Nitenpyram promotes breast cancer progression via PARP1: Integration of multi-omics analyses with experimental validation.

Nitenpyram (NIT) is an insecticide used primarily for flea control in pets, especially cats and dogs...

Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.

Multiplexed Microenvironment-Responsive Probes Enable Rapid Glioblastoma Cell Line Analysis.

Accurate distinguishing the phenotype of glioblastoma (GBM) cell lines remains challenging in clinic...

Improving MRI-based differentiation of brain metastasis, glioblastoma, and primary CNS lymphoma using deep learning.

Clinical development of molecular residual disease (MRD) and multi-cancer early detection (MCED) using liquid biopsy multiomics with artificial intelligence (AI).

BACKGROUND: Early detection of cancer and precise recurrence monitoring remain major unmet needs in ...

SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.

Traditional spatial transcriptomics methods typically rely on the direct relationship between spatia...

The effect of neoadjuvant therapy on radiological, surgical, and pathological results in nonmetastatic breast cancer: A retrospective observational study.

The aim of the study was to evaluate the concordance between radiological imaging modalities and pat...

The DNA-Based Disease Diagnosis Model with Strand Displacement Reaction.

MicroRNAs (miRNAs) serve as crucial biomarkers in disease diagnosis. Although silicon-based electron...

Development of a Classifier for Metabolic Subtypes of Nasopharyngeal Carcinoma to Guide Personalized Immunotherapy Strategies: Biomarker Analysis of the Phase III CONTINUUM and DIPPER Trials.

PURPOSE: Personalized immunotherapy strategies are urgently needed for patients with locoregionally ...

Radiomics-based ultrasOund Model for differentiating Uterine Sarcomas from leiomyomas (ROMUS): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer (MITO) study.

OBJECTIVE: To develop machine-learning models that incorporate clinical information and radiomics fe...

Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment.

Breast cancer remains a leading global health concern in women, while screening is still limited by ...

Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-r...

Cell-free DNA methylome and fragmentome analysis for relapse monitoring of Ewing sarcoma.

Liquid biopsies and cell-free DNA (cfDNA) offer minimally invasive methods for the diagnosis and mon...

A confidence-based, artificial intelligence pathology model for diagnosis of intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICCA) is a rare but highly lethal adenocarcinoma arisin...

Integrated analysis of therapeutic strategies and prognostic factors in advanced lung adenocarcinoma: Retrospective study with emphasis on gene assays, multimodality treatment approaches and predictive machine learning models.

Patients with advanced lung adenocarcinoma have a range of treatment options, including targeted the...