Oncology/Hematology

[The Digital Lab: Practical digital workflow and integration of AI for routine in pathology, through the example of prostate cancer].

Digital pathology is a major technological revolution for pathology. It modernizes routine practices and paves the way for the integration of artificial intelligence (AI) solutions for diagnostic and research purposes. At Rennes University Hospital, digital pathology has been routinely deployed since 2020, and an AI solution for the detection of prostate adenocarcinoma (Galen® Prostate, Ibex) has ...

Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation.

Objective. Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prostate cancer radiation therapy treatment planning and targeted dose escalation. However, DIL segmentation remains challenging due to small datasets, institutional bias, and variable imaging protocols. Although the segment anything model (SAM) has shown...

Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools.

PURPOSE: To differentiate benign and malignant breast masses by extracting radiomic features from low-energy and recombined contrast-enhanced mammogra...

A Protein-Centric Strategy Coupled with Match-Between-Run Glycoproteomics Enables Discovery of Robust Site-Specific Glycan Biomarkers for Hepatocellular Carcinoma.

Dysregulated protein glycosylation is a hallmark of cancer, and systematic investigation of glycosylation patterns is crucial for identifying biomarke...

Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.

Mitotic figure counting is an established measure of cell proliferation that is included in grading systems. We developed a deep learning method for m...

Association of Circulating T Cell and Tumor Microenvironment Profiles with Immune Checkpoint Blockade Outcome in Sarcoma.

PURPOSE: Immune checkpoint blockade (ICB) benefits only a subset of sarcoma patients. Biomarkers of response and resistance are needed to help guide p...

Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.

PURPOSE: Cancer survivors often experience long-term consequences affecting their Health-Related Quality of Life (HRQoL). Sociodemographic factors, cl...

Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer.

IMPORTANCE: Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from rout...

Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol.

BACKGROUND: Psychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals ...

Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.

BACKGROUND: Artificial intelligence (AI) has shown increasing potential in lung cancer imaging, particularly in detection, staging, prognosis, and rec...

Large-Scale Histological Image Dataset with Metadata for Colorectal Cancer Microenvironment.

The pronounced heterogeneity of the tumor microenvironment (TME) in colorectal cancer (CRC) presents major obstacles in accurately predicting patient ...

Multimodal activity-affinity assay of ADAM-10 extracellular vesicles in untreated plasma reveals metastatic stage of colorectal cancer.

Metalloproteinases (MPs) such as a-disintegrin and metalloproteinase-10 (ADAM-10) are key drivers of extracellular matrix remodeling during tumor prog...

A hypergraph-based model for tumor prognosis using local and global information fusion on H&E-stained histology images.

Prognostic variables play a critical role in guiding clinical treatment decisions for cancer patients. However, extracting prognostic information from...

Determination of Modified Waldenström Staging in Legg-Calvé-Perthes Disease Using Deep Learning.

BACKGROUND: Age and disease stage are critical factors in guiding management in Legg-Calvé-Perthes disease (LCPD). The modified Waldenström system is ...

Can Artificial Intelligence Be Used to Predict Response in Rectal Cancer? Current Evidence and Future Possibilities.

Artificial intelligence (AI) offers a promising solution to the long-standing challenge of accurately predicting treatment response in rectal cancer. ...

Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical integration.

Personalizing radiotherapy dose in breast cancer remains a major unmet need, as current treatment paradigms rely on uniform prescriptions that overloo...

Lactylation-based machine algorithm combined with multi-omics analysis to predict prognosis in cervical cancer.

Although lactylation has been investigated in cancer biology, its mechanistic role in cervical cancer remains unclear. This study integrated RNA-seque...

Machine learning-based prediction of long-term prognosis in cervical adenocarcinoma: a retrospective cohort study.

TCF21-WNT5A axis drives metastasis of colorectal cancer via stromal-tumor cell communication.

INTRODUCTION: The third most prevalent form of cancer, colorectal cancer (CRC), is associated with a high mortality rate due to colorectal liver metas...

Unraveling immune evasion in the tumor microenvironment: Mechanisms, therapeutic strategies, and future directions in cancer immunotherapy.

The immune evasion that is encouraged by the tumor microenvironment (TME) is a key factor in the failure of cancer immunotherapies. This review addres...