Oncology/Hematology

Real-time respiratory motion forecasting with online learning of recurrent neural networks for accurate targeting in externally guided radiotherapy.

BACKGROUND AND OBJECTIVE: In lung radiotherapy, infrared cameras can track reflective objects on the chest to estimate tumor motion due to breathing. However, treatment system latencies hinder radiation beam precision. Real-time recurrent learning (RTRL), the conventional online learning approach for training recurrent neural networks (RNNs), is a potential solution that can learn patterns within ...

Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis.

Understanding the interaction between T Cell Receptors (TCRs) and peptide-bound Major Histocompatibility Complexes (pMHCs) is crucial for comprehending immune responses and developing targeted immunotherapies. While recent machine learning (ML) models show remarkable success in predicting TCR-pMHC binding within training data, these models often fail to generalize to peptides outside their trainin...

Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm.

Breast cancer remains a major cause of mortality among women globally, driving the need for advanced therapeutic solutions. This study presents a nove...

Integration of Bulk and Single-Cell RNA Sequencing to Identify RNA Modifications-Related Prognostic Signature in Ovarian Cancer.

BACKGROUND: Ovarian cancer (OC), a common fatal malignancy in women, has a poor prognosis. RNA modifications are associated with the development of OC...

and are potentially important biomarkers for early diagnosis and treatment of esophageal squamous cell carcinoma: based on WGCNA and machine learning analysis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) does not have distinct and highly sensitive biomarkers, making its diagnosis difficult. Conseque...

Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment.

INTRODUCTION: Prostate adenocarcinoma (PRAD) is an extremely widespread site of urological malignancy and is the second most common male cancer in the...

BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers.

BACKGROUND AND OBJECTIVE: Accurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic me...

Classification of non-small cell lung cancer by histologic subtype using deep learning in public and private data sets of computed tomography images.

OBJECTIVE: To develop a deep learning system to classify non-small cell lung cancer (NSCLC) by histologic subtype-adenocarcinoma or squamous cell carc...

Transformer model based on Sonazoid contrast-enhanced ultrasound for microvascular invasion prediction in hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) is strongly associated with the prognosis of patients with hepatocellular carcinoma (HCC).

Non-orthogonal kV imaging guided patient position verification in non-coplanar radiation therapy with dataset-free implicit neural representation.

BACKGROUND: Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, p...

Semiautomated segmentation of breast tumor on automatic breast ultrasound image using a large-scale model with customized modules.

To verify the capability of the Segment Anything Model for medical images in 3D (SAM-Med3D), tailored with low-rank adaptation (LoRA) strategies, in s...

Novel marker genes and small molecule drugs for radiotherapy resistance in cervical cancer identified based on single-cell multi-omics analysis.

Radiotherapy is the cornerstone of treatment for cervical cancer, yet the variability of patient response demands a deeper understanding of the molecu...

Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study.

BACKGROUND: In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node...

Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma.

BACKGROUND: Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endomet...

The Role of Machine Learning to Detect Occult Neck Lymph Node Metastases in Early-Stage (T1-T2/N0) Oral Cavity Carcinomas.

OBJECTIVE: Oral cavity carcinomas (OCCs) represent roughly 50% of all head and neck cancers. The risk of occult neck metastases for early-stage OCCs r...

Data-Driven Defragmentation: Achieving Value-Based Sarcoma and Rare Cancer Care Through Integrated Care Pathway Mapping.

Sarcomas, a rare and complex group of cancers, require multidisciplinary care across multiple healthcare settings, often leading to delays, redundant ...

Association of [F]-FDG PET/CT-Derived Radiomic Features with Clinical Outcomes and Genomic Profiles in Patients with Chronic Lymphocytic Leukemia.

The role of PET/CT imaging in chronic lymphoproliferative syndromes (CLL) is debated. This study examines the potential of PET/CT radiomics in predic...

Use of Large Language Models in Clinical Cancer Research.

Artificial intelligence (AI) is increasingly being applied to clinical cancer research, driving precision oncology objectives by gathering clinical da...

Study on the relationship between vaginal dose and radiation-induced vaginal injury following cervical cancer radiotherapy, and model development.

OBJECTIVE: This study investigates the relationship between vaginal radiation dose and radiation-induced vaginal injury in cervical cancer patients, w...

Revolutionizing precision oncology: the role of artificial intelligence in personalized pediatric cancer care.

Artificial intelligence (AI) has recently garnered significant public attention. Among the various fields where AI can be applied, medicine stands out...