Oncology/Hematology

Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features.

BACKGROUND: Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast cancer. Although sentinel lymph node biopsy (SLNB) is less invasive than axillary lymph node dissection, it still exposes node-negative patients to unnecessary surgical risks. Most existing models predicting lymph node status in breast cancer rely on a limited ...

Development of multiphasic CT-based delta-radiomics model for predicting postoperative recurrence risk in bladder cancer.

BACKGROUND: This study aimed to develop a CT-based delta-radiomics model for personalized prediction of postoperative prognosis in bladder cancer patients and identification of differentially expressed genes associated with tumour recurrence. MATERIALS AND METHODS: This retrospective study included 316 patients with bladder cancer from Wuhan Union Hospital who underwent preoperative unenhanced and...

A multimodal deep learning classifier for prediction of HER2-low expression in triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is an aggressive molecular subtype. With the new definition of HER2-low status and the availability o...

Dismantling the TGF-β Axis: A Critical Transition from Occupancy-based Kinase Inhibitors to Event-driven Degraders.

The TGF-β signaling pathway plays a dual role in cancer, acting as both a tumor suppressor and a potent driver of metastasis and immune evasion. Tradi...

LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images.

Breast cancer remains a significant health concern for women worldwide. Ultrasound imaging is widely adopted for screening due to its non-invasive and...

GAN-Augmented Radiomics and Machine Learning for Post-Therapy GBM Progression Assessment.

OBJECTIVE: Differentiating true progression (TP) from pseudoprogression (PsP) in post-therapy of glioblastoma multiforme (GBM) remains a critical chal...

Image sharpening improves the visibility of reference points on low-dose lateral cephalometric radiographs: a phantom study.

This phantom study aimed to determine whether a novel non-artificial intelligence image-sharpening approach improves the visibility of reference point...

Supporting patient understanding of cervical and ovarian cancer: how well does AI perform?

BACKGROUND: As the public increasingly interacts with artificial intelligence (AI) chatbots, we compared the answers from five AI chatbots to standard...

Head and Neck Free Flap Reconstruction: Current Landscape and Emerging Technologies.

OBJECTIVE: To synthesize contemporary developments in head and neck oncologic free flap reconstruction, with emphasis on perioperative physiologic opt...

Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer.

BACKGROUND AND PURPOSE: While intravesical Bacillus Calmette-Guérin Vaccine (BCG) instillation remains standard adjuvant therapy for high-grade non-mu...

Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer and Their Caregivers.

OBJECTIVE: This study evaluated dyadic endocrine interdependence among cancer patients and their spousal caregivers using a machine learning (ML) fram...

A morphology-guided ViT-CNN approach for the detection of lung and colon cancer using H&E-stained biopsy images.

The accurate categorisation of histopathological images is crucial for the reliable identification of morphologically similar cancers, including lung ...

Artificial intelligence-based prediction of claudin 18.2 expression and immune phenotype from routine histology to guide treatment decisions in patients with gastric cancer.

BACKGROUND: First-line treatment of gastric cancer is evolving with the integration of immune checkpoint inhibitors (ICIs) and targeted agents, compli...

Expression characteristics analysis, establishment of prognostic model and screening of key regulators based on selenium protein-related genes in rectal adenocarcinoma.

BACKGROUND: Selenium is an essential trace element in the human body and plays a significant role in multiple cancers. This study aims to comprehensiv...

Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.

BACKGROUND: KRAS-mutant lung adenocarcinoma (LUAD) is associated with aggressive phenotypes and therapy resistance, which highlights an urgent need to...

Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.

Quantitative systems pharmacology (QSP) models support model-informed drug development but are computationally expensive, limiting workflows requiring...

Optical Spectral Fingerprinting Enables Sensitive Detection of Anthracycline Chemotherapeutics in Synthetic Clinical Biofluids.

Anthracycline chemotherapeutics are common chemotherapeutics that have substantial toxicities. There is substantial interpatient pharmacokinetic varia...

Precision lung cancer classification using convolutional neural networks with enhanced image pre-processing and model optimization.

Among various types of cancers, lung cancer causes the highest mortality globally, necessitating prompt diagnosis for effective treatment. Traditional...

A compact texture frequency spatial gating network for oral cancer classification from clinical oral images.

Image-level classification of oral-cavity photographs remains challenging because oral-cancer appearances are heterogeneous and many public datasets l...

Multiscale systems modelling of communication networks in brain metastasis.

Brain metastasis remains a major clinical challenge because intracranial progression and therapeutic resistance arise not only from tumour-intrinsic p...