Oncology/Hematology

Deep Learning-Based Multimodal Fusion of Ultrasound, Cytology, and Clinical Features to Distinguish Follicular Thyroid Carcinoma from Adenoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: Preoperative differentiation between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challenging due to overlapping cytological and ultrasonographic features. This study aimed to develop and validate a multimodal deep learning model integrating ultrasound images, fine-needle aspiration cytology (FNAC) images, and clinical features for preop...

STC2-mediated calcium homeostasis dysregulation in the osteosarcoma microenvironment: Insights from single-cell sequencing and machine learning.

BACKGROUND: Osteosarcoma (OS) is an aggressive bone malignancy with a poor prognosis. Dysregulated calcium homeostasis may contribute to OS progression. METHODS: Various machine learning algorithms were integrated into multiple model combinations to identify key prognostic genes. Single-cell expression profiling was conducted to assess STC2 expression across different cell types within the OS micr...

Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.

Accurate neoantigen prediction is central to the design of personalized cancer immunotherapy. The immune recognition of neoantigens is a multi-step pr...

Imaging Immune Heterogeneity in Nasopharyngeal Carcinoma: A Multidimensional Radiomics Perspective.

RATIONALE AND OBJECTIVES: Nasopharyngeal carcinoma (NPC) is characterized by a distinctive virologic and immunologic profile, in which Epstein-Barr vi...

Multimodal large language models in brain tumor imaging: clinical applications and future perspectives.

The use of multimodal data is essential for the precise diagnosis and treatment of brain tumors. In this context, multimodal data encompass multiseque...

Multiclass Classification of Renal Tumor Subtypes: Addressing Diagnostic Challenges Using a Texture-Informed Deep Hybrid CNN-Transformer.

Accurate histopathological classification of renal cell carcinoma (RCC), along with its distinction from benign mimickers, is essential for precision ...

Exploration of the roles of SSR2 in hepatocellular carcinogenesis based on single-cell transcriptomics and spatial transcriptomics.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common types of cancer globally. However, HCC features poor prognosis due to complex pat...

Integrative multi-omics analysis identifies PAICS as a key regulator of epithelial-mesenchymal transition in breast cancer metastasis.

BACKGROUND: Breast cancer metastasis remains a major clinical challenge due to its complex molecular mechanisms, highlighting the need to identify key...

Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review.

Glycans are complex carbohydrates that are integral to cancer progression and tumor biology. Conventional methods face challenges due to the structura...

Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study.

BACKGROUND: Prognostic information is essential for decision-making in breast cancer management. In recent years, trials and clinical practice have em...

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Compu...

The role of AI in oncology: present applications and future horizons.

Artificial intelligence (AI) offers a powerful means to accelerate precision oncology by individualizing care in an era of rapidly evolving treatment ...

Development and validation of a multimodal AI-agent system for prognosis analysis of bladder urothelial carcinoma.

Precise survival risk stratification for bladder urothelial carcinoma (BUC) remains a clinical challenge. We developed and validated a multimodal AI a...

Bioimage analysis for multiplexed FUCCI acquisitions powered by deep learning.

The FUCCI sensor fluorescently labels cell cycle phases, which is essential to assess normal and abnormal cell-cycle progression in physiological and ...

Quantum dot-DNA microsphere aptamer biosensor with AI-assisted structural modeling for rapid detection of the lung cancer biomarker USE1.

BACKGROUND: Lung cancer remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need for rapid, accurate, and affordabl...

AI-based classification of pediatric sagittal skeletal patterns using multi-view facial photographs.

OBJECTIVE: The increasing global demand to assess pediatric skeletal malocclusions poses a growing challenge, as current screening methods are depende...

Breast area affects the performance of a commercial artificial intelligence algorithm assessment of negative digital breast tomosynthesis exams.

OBJECTIVE: To understand whether cancer-neutral image attributes (breast area and number of slices) impact an AI algorithm assessment of negative digi...

Evaluation of the impact of glycolysis-related gene signatures on prognosis and therapeutic targeting in lung adenocarcinoma.

Abnormal glycolysis is one of the hallmarks of cancer and plays a significant role in its progression. This study investigates the association between...

Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?

PURPOSE: In this prospective cross-over study, the precision of manual correction of the clinical target volume (CTV) during online-adaptive radiother...

Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment.

BACKGROUND: Accurate prediction of early recurrence (ER) after radical resection remains a critical challenge in pancreatic ductal adenocarcinoma (PDA...