Oncology/Hematology

Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders.

Oral potentially malignant disorders (OPMDs) such as leukoplakia and erythroplakia may harbor dysplasia or progress to oral squamous cell carcinoma (OSCC), yet visual inspection alone is unreliable for risk assessment. Cytology offers a minimally invasive adjunct, but conventional approaches depend on manual feature extraction and subjective review. Herein we report a deep learning (DL) object det...

Evaluation of Machine Learning and Statistical Models for Predicting Long Term Gastrostomy Tube Dependency in Patients Undergoing Major Oral Cavity Cancer Surgery With Free Flap Reconstruction.

BACKGROUND: Swallowing dysfunction and long-term gastrostomy tube dependence are common morbidities after oral cavity cancer resection with free flap reconstruction, highlighting the need for preoperative risk prediction. METHODS: We performed a multicenter retrospective pooled cohort study of patients with non-metastatic oral cavity cancer undergoing free flap reconstruction. Firth's penalized lo...

Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors.

Automated measurements of anisokaryosis in canine cutaneous mast cell tumors (ccMCTs) have been shown to be predictive of survival, but questions rema...

Integrated bulk and single-cell transcriptomic analysis reveals the impact of lactate-related genes on prognosis and immune characteristics in HNSCC.

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis, accompanied by metabolic reprogramming and tum...

Reframing Response in Neoadjuvant Chemotherapy for Ovarian Cancer: Integrating Molecular and Functional Biomarkers.

BACKGROUND: Neoadjuvant chemotherapy (NACT) is an established treatment strategy for advanced epithelial ovarian cancer, particularly for patients wit...

Machine learning and multi-omic empowered risk stratification and therapeutic framework targeting nucleotide metabolism and mast cell for stomach adenocarcinoma patients.

BACKGROUND: Mast cell and nucleotide metabolism(NM) encodes the Stomach adenocarcinoma (STAD) progression and tumor immune microenvironment(TME) heter...

Pretreatment multiparametric MRI-based habitat radiomics and 2.5D deep learning for predicting early complete response to induction chemoimmunotherapy in locally advanced nasopharyngeal carcinoma.

OBJECTIVE: To develop and externally validate a pretreatment multiparametric MRI-based combined imaging model integrating conventional radiomics, habi...

Integration of inflammatory and nutritional biomarkers with machine learning enhances prediction of progesterone response in fertility-preserving endometrial carcinoma management.

BACKGROUND: Endometrial carcinoma (EC) and atypical endometrial hyperplasia (AEH) increasingly affect young women, posing challenges for fertility pre...

Precision therapeutics and innovative clinical trial design in neurodegenerative diseases.

Neurodegenerative diseases are biologically heterogeneous disorders characterized by progressive neuronal dysfunction, overlapping molecular pathologi...

Unraveling Bone-Skin Crosstalk Enables miRNA Nanoformulation for Cutaneous Neurovascular Reconstruction in Diabetic Mice.

The clinical intractability of diabetic foot ulcers stems from a profound uncoupling of cutaneous neurovascular networks, rendering standard metabolic...

Cyclin-dependent kinases as signaling integrators in cancer: Structural evolution, functional plasticity, and drug discovery.

Cyclin-dependent kinases (CDKs), traditionally recognized for their pivotal role in cell cycle control, have emerged as versatile regulators orchestra...

Specimen quality shapes the actionable genomic landscape in comprehensive cancer genomic profiling.

Comprehensive genomic profiling (CGP) is widely used to identify actionable alterations and guide precision oncology, yet only a minority of tested pa...

Ensemble machine learning for predicting breast cancer recurrence and mortality using clinical and hemogram data.

Accurate prediction of breast cancer recurrence remains difficult because prognosis varies significantly across molecular subtypes. This underscores t...

Deep learning for preoperative MRI-based endometrial cancer staging prediction.

Endometrial carcinoma ranks among the most common malignancies of the female reproductive system. Accurate early-stage staging is essential for devisi...

AI in predicting the macrotrabecular-massive subtype of HCC and informing treatment selection: a multi-center and prospective validation study.

The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is associated with aggressive biology and poor prognosis. We aimed to deve...

Geometric-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics.

Recent advances in spatially resolved transcriptomics have enabled large-scale measurement of gene expression while preserving spatial context, facili...

Advancing genomics and integration of multi-omics for precision oncology using quantum machine learning.

Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretat...

Integrative single-cell and spatial transcriptomic analysis reveals a lactate-driven crosstalk between NFATc4⁺ tumor cells and SPP1⁺ macrophages in glioblastoma.

BACKGROUND: Glioblastoma (GBM) remains a lethal brain tumor with limited therapeutic options. Metabolic reprogramming, particularly lactate metabolism...

TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics.

Transposable elements (TEs) represent an abundant and important source of HLA-presented antigens, but their immunopeptidomic characterization remains ...

Who Is Responsible When AI Gets Cancer Information Wrong? Implications for Patient Education.

Generative artificial intelligence (AI) tools are increasingly being used by patients seeking cancer-related information, creating new opportunities f...