Oncology/Hematology

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-related death worldwide, yet current prognostic stratification is hindered by tumor heterogeneity. Here, we developed a deep learning radiomics model (DLRM), optimized through systematic evaluation of ten machine learning algorithms across 117 combinations, using venous-phase computed tomography (CT)...

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 monitoring of Ewing Sarcoma (EwS). EwS have a low tumour mutational burden and their detection with plasma cfDNA is challenging. We hypothesised that analysing the cfDNA methylome and fragmentome could enhance sensitivity for detecting EwS and identifying disease recurrence. Using T7-MBD-seq, we conduc...

Climate predictors of child undernutrition: Insights from a machine learning model.

BACKGROUND: Climate variability is increasingly recognized as a driver of child undernutrition, yet the non-linear relationships between specific clim...

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

BACKGROUND: Intrahepatic cholangiocarcinoma (ICCA) is a rare but highly lethal adenocarcinoma arising within the hepatic parenchyma. Diagnosis present...

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 therapy and gene assay-guided chemotherapy. The aim o...

Context-Aware adaptive normalization LSTM (CAAN-LSTM) for immunotherapy decision support in cancer clinical data analysis.

BACKGROUND: Clinical decision-making for cancer immunotherapy is challenged by the heterogeneous and often incomplete nature of patient time-series da...

Leveraging Kappa-Lambda Signatures in a Multi-Stage Machine Learning Pipeline for B-Cell Lymphoma Detection by Flow Cytometry.

Flow cytometry immunophenotyping is essential for diagnosing B-cell lymphomas, but manual interpretation of high-dimensional data remains subjective, ...

BCLC classification and AI-based image quantification: What is meant to be will come together - but how and when?: BCLC and AI-based image quantification.

The Barcelona Clinic Liver Cancer (BCLC) classification has been the mainstay for prognostic assessment and initial treatment selection in hepatocellu...

From Antigen to Atlas: A Multi-omics, Single-Cell Pipeline for Discovering Safe and Effective CAR Targets in Ovarian Cancer.

Chimeric antigen receptor (CAR) T-cell therapy has achieved remarkable success in hematologic malignancies but continues to face significant barriers ...

Artificial-intelligence-guided autophagy modulation and nanomedicine design for precision photodynamic cancer therapy.

Cancer remains a major cause of death worldwide and current therapies are often limited by toxicity, resistance and poor tumor selectivity. Photodynam...

Unraveling the connection between PFOA and bladder cancer: A study integrating network toxicology, molecular docking, and experimental validation.

PFOA, an environmental pollutant linked to bladder cancer, has unclear molecular mechanisms. Integrating transcriptomic data with network toxicology a...

Advances in the application of photodynamic diagnosis in Skin Tumors.

Photodynamic Diagnosis (PDD) is a non-invasive imaging technique. It relies on a photosensitizer that, when activated by a specific light source, caus...

Machine Learning-Enhanced Microfluidic Impedance Platform for Rare Cell Analysis.

Rare cells, despite constituting only a small fraction of the population, play a critical role in health and disease. For instance, a minor subset of ...

Artificial Intelligence for T classification of TNM breast cancer in MRI imaging: Enabling Precision in Treatment Decisions.

The integration of artificial intelligence (AI) into breast cancer management presents transformative potential for both diagnosis and treatment plann...

Validation of an AI Method for Automated Lymphoma Metabolic Tumor Volume Segmentation Using a Public Benchmark PET/CT Dataset.

The aim of this study was to evaluate the performance of an artificial intelligence (AI)-based method for automated segmentation of total metabolic tu...

CLWD: a Chinese histopathology dataset for lung adenocarcinoma subtype classification.

Effective diagnosis and treatment of lung adenocarcinoma depends on accurate typing, subtyping, and grading. Herein, we present the CLWD dataset, a va...

Prediction of Ki-67 expression in invasive breast cancer with dual-modality radiomics.

Ki-67 expression, a critical biomarker for tumor aggressiveness and proliferation in invasive breast cancer, is traditionally assessed via invasive bi...

Association of surgical resection with survival in retroperitoneal leiomyosarcoma based on SEER propensity score matching and machine-learning models.

Retroperitoneal leiomyosarcoma (RLS) is a rare and aggressive subtype of soft tissue sarcoma with limited population-level evidence guiding surgical d...

Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions.

Dynamic contrast-enhanced (DCE) breast MRI is a highly sensitive modality for detecting breast cancer, but its limited specificity often leads to fals...

Automatic Hepatic Steatosis Quantification using Low-Dose CT with deep learning-based noise reduction and CT Fat Fraction Analysis Software.

OBJECTIVES: To evaluate the accuracy of CT-derived fat fraction (CDFF) software for quantifying hepatic steatosis at various radiation doses, using MR...