Oncology/Hematology

MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images.

Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its heterogeneous nature and the need to harness the complementary information from multiple nuclei sources in histopathology images. To tackle this critic...

Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images.

Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with patient prognosis and response to immunotherapy, making TLS detection in whole-slide pathological images (WSIs) crucial for clinical decision-making. Alt...

Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, with Gleason grading critical for prognosis and treatment decisions. Mach...

POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning.

Long non-coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis and therapy. Although there are many computationa...

Deep learning and iterative image reconstruction for head CT: Impact on image quality and radiation dose reduction-Comparative study.

This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image ...

Construction and Multi-dimensional Validation of a Lactylation-Related Signature for Glioblastoma Multiforme Prognostic and Therapeutic Purposes.

Glioblastoma multiforme, one of the most malignant types of brain tumor, heavily relies on glycolytic pathways and is significantly influenced by immu...

Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches.

Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellul...

Mechanisms underlying obesity-malignancy connection: a systematic narrative review.

The association between obesity and cancer risk carries substantial public health ramifications as obesity promotes cancer advancement via many cellul...

Shining the Beam on the Next Generation: A Program Evaluation of a National Workshop Focusing on Medical Student Engagement in Radiation Oncology.

The rising cancer incidence has increased demand for radiation oncologists, surpassing current staffing expansion estimates. Enhancing radiation oncol...

SSC-SleepNet: A Siamese-Based Automatic Sleep Staging Model with Improved N1 Sleep Detection.

Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly an...

Single-cell multimodal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) constitutes over 80% of lung cancer cases and remains a leading cause of cancer-related mortality worldwide. Despit...

Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis.

BACKGROUND: Defining optimal adjuvant therapeutic strategies for older adult patients with breast cancer remains a challenge, given that this populati...

Ovarian Cancer Screening: Recommendations and Future Prospects.

Ovarian cancer remains a significant cause of mortality among women, largely due to challenges in early detection. Current screening strategies, inclu...

Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors.

Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appe...

Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma.

Accurate prediction of prognosis and postoperative radiotherapy response is critical for personalized treatment in head and neck squamous cell carcino...

End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study.

OBJECTIVES: Pancreatic cancer treatment plans involving surgery and/or chemotherapy are highly dependent on disease stage. However, current staging sy...

Process management method in head and neck cancer patient care.

Management of patients with head and neck cancer (HNC) is a complex process that involves extensive knowledge on (at least) the discipline of surgery,...

Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality, and tumor heterogeneity could result in diverse progno...

Machine learning explores the prognostic and immuno-oncological impact of mitochondrial unfolded protein response in CESC.

BACKGROUND: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) pose significant global health challenges. While the mitochondrial...

Evaluation of a deep-learning segmentation model for patients with colorectal cancer liver metastases (COALA) in the radiological workflow.

OBJECTIVES: For patients with colorectal liver metastases (CRLM), total tumor volume (TTV) is prognostic. A deep-learning segmentation model for CRLM ...