Oncology/Hematology

Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.

Urothelial carcinoma, predominantly appearing as non-muscle-invasive papillary urothelial carcinoma (NMIPUC), exhibits wide clinical variability. Accurate pathological staging and grading are essential for effective risk stratification and treatment decisions. Advancements in artificial intelligence (AI) open new opportunities to improve predictive models; however, their generalizability across di...

A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.

Accurate International Society of Urological Pathology (ISUP)-grade classification of renal cell carcinoma (RCC) is challenging due to subtle histopathological variations and the limitations of manual review. Existing deep learning models often rely on complex attention mechanisms that increase computational cost and hinder deployment. This study introduces a lightweight ResNet50V2-ECA framework t...

Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.

Early analysis is a necessity for the more effective treatment of cancers. In gynaecological cancers, like endometrial, ovarian, and cervical cancers,...

Integrative single-cell and spatial transcriptomics with machine learning identify a Luminal-inflam malignant program and reveal an RPN1-PERK UPR vulnerability in triple-negative breast cancer.

Triple-negative breast cancer is marked by extensive cellular heterogeneity and limited availability of actionable targeted treatments, which contribu...

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogeneity limits therapeutic benefit to specific patient...

Swin UNETR-based prediction of [68Ga]Ga-FAPI-46 PET/CT dose rate maps in cancer patients: quantitative comparison with [18F]FDG PET/CT.

BACKGROUND: Fibroblast activation protein (FAP) is a promising theranostic target due to its high stroma expression in numerous malignancies. This stu...

Deep learning based individualized cross-platform molecular subtype classification of B-lineage acute lymphoblastic leukemia.

Molecular subtypes of B-cell acute lymphoblastic leukemia (B-ALL) are essential in modern clinical treatment. However, the fast emerging subtypes and ...

Caregiver Preferences for AI-Supported Telemedicine in Pediatric Palliative Care: A Discrete Choice Experiment.

BackgroundAsynchronous telemedicine may support home-based pediatric palliative care (PPC) by improving access to professional guidance and reducing c...

Exploring artificial intelligence in point-of-care and standard breast ultrasound: a paired reader study.

BackgroundBreast cancer diagnoses are limited in low- and middle-income settings due to lack of medical resources. In these settings, point-of-care ul...

Prediction of early death in peripheral T-cell lymphoma-NOS patients based on machine learning.

Peripheral T-cell lymphoma-not otherwise specified (PTCL-NOS) is a highly aggressive and heterogeneous lymphoma subtype with a poor prognosis. This st...

Identifying the relationship between exosome genes and breast cancer risk using bioinformatics and machine learning methods.

BACKGROUND: Breast cancer (BC) is the most prevalent cancer among women globally, with a high mortality rate. The treatment and prevention of this dis...

Machine learning for predicting liver metastasis in colorectal cancer.

AIM: To evaluate the performance of machine learning models in predicting liver metastasis in colorectal cancer (CRC) patients using the SEER database...

Identifying PLAU as a shared pathogenic gene in type 2 diabetes and bladder urothelial carcinoma through integrated transcriptomic analysis and machine learning for diagnostic and therapeutic value.

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogen...

Development and validation of a machine learning-based model for predicting delirium risk in postoperative brain tumor patients in the Intensive Care Unit.

OBJECTIVE: This study aimed to clarify the incidence and influencing factors of delirium in ICU patients after brain tumor surgery, construct and vali...

Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images.

Breast cancer detection remains a significant challenge in medical diagnostics. Traditional diagnostic methods are time-consuming, unable to detect co...

Prompt-directed ambient artificial intelligence for automated multidisciplinary tumor board documentation.

BACKGROUND: Tumor board documentation is challenging due to complexity of multidisciplinary input. Ambient artificial intelligence (AI) for medical di...

Quantification of Histotripsy Dosage Using Machine Learning-Enhanced Ultrasound Imaging Analysis: Correlation with Histological Outcomes.

RATIONALE AND OBJECTIVES: Histotripsy is a noninvasive ultrasound therapy that mechanically disrupts target tissue through controlled acoustic cavitat...

A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and accurate histopathological classification is essential for ti...

Differentiating malignancy from liver parenchyma in Ex-Vivo OCT images using anomaly detection.

Primary liver cancer and colorectal liver metastases (CRLM) pose significant challenges, because of limited early diagnosis and the reliance on time-c...

AI-assisted extraction of opportunistic findings from oncologic CT reports using large language models.

Opportunistic findings at imaging (iOFs), such as osteoporosis, liver steatosis, or coronary artery calcifications, are clinically relevant abnormalit...