Oncology/Hematology

Predicting the risk of ibrutinib in combination with R-ICE in patients with relapsed or refractory DLBCL using explainable machine learning algorithms.

Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) poses significant therapeutic challenges due to heterogeneous patient outcomes. This study aimed to evaluate the efficacy of the ibrutinib plus R-ICE regimen and to leverage explainable machine learning models (ML) for predicting treatment risks and outcomes. Retrospective data from 28 patients treated between March 2019 and July 2022 we...

Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC), a cancer characterized by substantial immune infiltration, exhibits limited sensitivity to conventional radiochemotherapy. Although immunotherapy has shown efficacy in some patients, its applicability is not universally effective. Studies have indicated that programmed cell death (PCD) can modulate the activity of immune cells and participate in the regula...

Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA ...

Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images.

The detection, segmentation, and differentiation of benign and malignant nuclei from the histopathology images is a challenging task for the early dia...

Prediction of one-year recurrence among breast cancer patients undergone surgery using artificial intelligence-based algorithms: a retrospective study on prognostic factors.

BACKGROUND AND AIM: Breast cancer is highly prevalent, with an increasing trend in women globally. Although the survival of breast cancer is relativel...

Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.

INTRODUCTION: Cervical cancer, which includes squamous cell carcinoma and adenocarcinoma, is a leading cause of cancer-related deaths globally, partic...

ADGSyn: Dual-Stream Learning for Efficient Anticancer Drug Synergy Prediction

Drug combinations play a critical role in cancer therapy by significantly enhancing treatment efficacy and overcoming drug resistance. However, the ...

SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours

Accurate tumour segmentation is vital for various targeted diagnostic and therapeutic procedures for cancer, e.g., planning biopsies or tumour ablat...

[Integrated diagnosis and treatment of peritoneal metastasis in gastric cancer].

The high incidence and mortality rates of gastric cancer pose a significant burden on human health and public health systems. Peritoneal metastasis is...

[Clinical value of medical imaging artificial intelligence in the diagnosis and treatment of peritoneal metastasis in gastrointestinal cancers].

Peritoneal metastasis is a key factor in the poor prognosis of advanced gastrointestinal cancer patients. Traditional radiological diagnostic faces ch...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study ai...

Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images.

In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to th...

An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images

Accurate identification of breast cancer types plays a critical role in guiding treatment decisions and improving patient outcomes. This paper prese...

Deep Learning for Breast Cancer Detection: Comparative Analysis of ConvNeXT and EfficientNet

Breast cancer is the most commonly occurring cancer worldwide. This cancer caused 670,000 deaths globally in 2022, as reported by the WHO. Yet since...

Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study.

BACKGROUND: Developing an accurate predictive model for palliative care phases is crucial for improving cancer patient management, enabling healthcare...

Promptable cancer segmentation using minimal expert-curated data

Automated segmentation of cancer on medical images can aid targeted diagnostic and therapeutic procedures. However, its adoption is limited by the h...

Pixels to Prognosis: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures Across Multicentre NSCLC Data

Purpose: To evaluate the impact of harmonization and multi-region CT image feature integration on survival prediction in non-small cell lung cancer ...

DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer

Background: Accurate assessment of metastatic burden in axillary lymph nodes is crucial for guiding breast cancer treatment decisions, yet conventio...

Identification of molecular subtypes and a prognostic signature based on machine learning and purine metabolism-related genes in breast cancer.

Breast cancer (BC), one of the most prevalent malignant tumors worldwide, lacks efficacious diagnostic biomarkers and therapeutic targets. This study ...

Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT.

Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncert...