Breast Cancer

Next-generation precision oncology in microsatellite stable colorectal cancer: integrated targeting beyond RAS and RAF.

The therapeutic landscape of colorectal cancer (CRC) has evolved with the identification of molecular subtypes, including mismatch repair-deficient/microsatellite instability-high, POLE mutations, RAS/BRAF alterations, and HER2 amplification, enabling use of precision therapies and immune checkpoint inhibitors for selected populations. However, most microsatellite-stable (MSS) tumors remain resist...

A Multimodal Deep Learning Model for Preoperative Prediction of Postoperative Complications in Gastric Cancer.

BACKGROUND: Clavien-Dindo (CD) grade ≥II complications occur in roughly one in five patients after curative gastrectomy for gastric cancer and independently shorten overall survival (OS) by compromising adjuvant therapy delivery. Current nutritional, inflammatory and global surgical risk scores identify only a minority of affected patients. We developed and validated a multi-modal deep learning fr...

Deep Learning Predicts Imminent Tumor Progression in Advanced Pancreatic Adenocarcinoma Using Serial CT Scans During Chemotherapy.

Advanced pancreatic ductal adenocarcinoma (PDAC) often progresses rapidly during chemotherapy despite initial assessments of stable disease or partial...

A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.

BACKGROUND: Accurate delineation of target volumes and organs at risk (OAR) is essential in radiotherapy planning for cervical cancer. Deep learning (...

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...

CMTM3 links inflammation-related transcriptional dysregulation to immune and therapeutic stratification in gastric cancer.

Gastric cancer (GC) shows strong biological heterogeneity and frequent disruption of inflammatory and metabolic programs, which affect tumor progressi...

The Promise of Machine Learning-Guided Adjuvant Chemotherapy for Resected Gallbladder Cancer.

ViT-MultiRAGNet: A scalable and reliable retrieval-augmented Vision Transformer framework for memory-guided feature fusion multi-modal mammogram classification.

BACKGROUND: Diagnosing breast cancer with mammography continues to be an incidentally challenging process because of the numerous different ways of ac...

Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.

OBJECTIVE: Develop a multimodal fusion model combining MRI radiomics and deep learning (DL) to predict pathologic complete response (pCR) in breast ca...

A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.

BACKGROUND AND OBJECTIVES: Myxopapillary ependymomas (MPE) and intradural lumbosacral schwannomas may be challenging to distinguish based on presentin...

Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, he...

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of exist...

Treatment-specific CT Radiomics Models To Predict Response To Neoadjuvant Therapy And Explore Individualized Treatment Selection In Gastric Cancer.

RATIONALE AND OBJECTIVES: To develop and compare general and treatment-specific radiomics models based on pretreatment computed tomography (CT) for pr...

Explainable hourly global solar radiation forecasting using a CNN-BiLSTM model with temperature scaled softmax attention.

Accurate global solar radiation (GSR) forecasting is vital for smart grids and resilient energy systems. However, the nonlinear and non-stationary nat...

Machine learning-based prediction of overall survival in non-metastatic renal cell carcinoma after radical nephrectomy.

Prognostic heterogeneity remains a challenge for non-metastatic renal cell carcinoma (RCC) patients following radical nephrectomy (RN). This study aim...

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...

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...

Topological imaging of single-cell CAF heterogeneity for predicting prognosis and adjuvant chemotherapy benefit in pancreatic cancer.

PURPOSE: Cancer-associated fibroblasts (CAFs) are central drivers of PDAC progression and therapeutic resistance, yet their preoperative clinical util...

Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.

BACKGROUND: Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by...