Oncology/Hematology

A new era beckons in asthma: enhanced models of care, disease remission, artificial intelligence and consumer participation.

The landscape of asthma management is undergoing significant transformation. This change is driven by several factors: deeper understanding of asthma pathophysiology, appreciation of the long-term harm of oral corticosteroids, biological treatments that dramatically improve patient outcomes with asthma remission becoming a realistic goal, and a focus on patient-centred care combined with the use o...

Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma.

The objective of the study is to develop and validate a multiparametric MRI (mpMRI)-based model that integrated with habitat-based radiomics, deep transfer learning (DTL), and quantitative parameters for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC). This retrospective study collected clinical, pathological, and mpMRI data from patients with con...

A multimodal prediction framework for colorectal cancer peritoneal metastasis: CT-based tumor and adipose tissue analysis.

Accurate preoperative assessment of CT-occult peritoneal metastasis (PM) in colorectal cancer is critical for clinical decision-making. This study dem...

An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.

The diagnosis of prostate cancer rests on the histopathological evaluation of prostate needle core biopsy specimens (NCBS) supplemented by immunohisto...

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulati...

Biomarker-driven and AI-assisted nanomedicine for breast cancer: advancing precision drug delivery from bench to bedside.

INTRODUCTION: Breast cancer remains the most prevalent malignancy among women worldwide, with its pronounced molecular heterogeneity demanding therape...

Development and internal validation of an explainable machine learning model for predicting textbook outcome after free flap reconstruction in oral cancer.

OBJECTIVE: To develop and internally validate an explainable machine learning model for predicting textbook outcome (TO) after free flap reconstructio...

Cross-species integration of single-cell data reveals conserved pathology-associated cell populations across animal models and human samples.

Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular heterogeneity, but integrating data across species remains challe...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

Cytoreductive nephrectomy improves survival in metastatic papillary renal cell carcinoma: a population-based study with machine learning validation and landmark analysis.

BACKGROUND: The role of cytoreductive nephrectomy (CN) in metastatic papillary renal cell carcinoma (pRCC) remains poorly defined, as most CN evidence...

A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.

Immune checkpoint inhibitors, particularly antibodies targeting programmed cell death 1 (PD-1), are increasingly used for advanced hepatocellular carc...

An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.

BACKGROUND: SERPING1, which encodes the C1 inhibitor (C1-INH) of the complement system, and plays a key regulator in regulating inflammatory responses...

Machine learning based prediction of recurrence in oral tongue cancer: a systematic review with quantitative synthesis.

PURPOSE: Oral tongue squamous cell carcinoma (OTSCC) is characterized by aggressive local invasion and a high risk of cervical nodal metastasis and mo...

An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.

Acute Lymphoblastic Leukemia (ALL) is one of the most aggressive hematological malignancies, and its early diagnosis remains challenging due to non-sp...

Deep learning-based cross-modal MR-CT registration for brain metastases radiotherapy with multi-scale feature refinement and brainstem guidance.

Accurate multimodal deformable registration between magnetic resonance (MR) and computed tomography (CT) images is essential for precise target deline...

Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.

The integration of multiomics technologies with artificial intelligence (AI) has become a transformative force in modern precision medicine, particula...

Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugs.

Cancer patients are particularly susceptible to Drug-Drug Interactions (DDIs) due to frequent polypharmacy in oncology care. Co-administered drugs can...

Prospective germline exome and machine learning-based risk score identify predictive and prognostic biomarkers of immunotherapy outcomes in advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, a su...

Translating lineage-resolved single-cell programs to bulk clinical prognosis: Adversarial generative learning reveals lineage-informed immune - stromal - epithelial risk signatures.

BACKGROUND: Breast cancer transcriptional programs span malignant epithelial cells, and the tumor microenvironment (TME), yet tumor-normal contrasts a...

Robust end-to-end stratification of amyotrophic lateral sclerosis patients via recurrent variational autoencoder and consensus clustering.

OBJECTIVE: This study aims to develop a data-driven methodology for stratifying Amyotrophic Lateral Sclerosis (ALS) patients based on longitudinal dis...