Oncology/Hematology

Potential of artificial intelligence for radiation dose reduction in computed tomography -A scoping review.

INTRODUCTION: Artificial intelligence (AI) is now transforming medical imaging, with extensive ramifications for nearly every aspect of diagnostic imaging, including computed tomography (CT). This current work aims to review, evaluate, and summarise the role of AI in radiation dose optimisation across three fundamental domains in CT: patient positioning, scan range determination, and image reconst...

UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion

Background: Brain tumor segmentation has a significant impact on the diagnosis and treatment of brain tumors. Accurate brain tumor segmentation remains challenging due to their irregular shapes, vague boundaries, and high variability. Objective: We propose a brain tumor segmentation method that combines deep learning with prior knowledge derived from a region-growing algorithm. Methods: The prop...

STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis

We propose a multimodal spatiotemporal graph neural network (STG) framework to predict colorectal cancer liver metastasis (CRLM) progression. Curren...

Nonparametric learning of covariate-based Markov jump processes using RKHS techniques

We propose a novel nonparametric approach for linking covariates to Continuous Time Markov Chains (CTMCs) using the mathematical framework of Reprod...

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models

We explore Generalizable Tumor Segmentation, aiming to train a single model for zero-shot tumor segmentation across diverse anatomical regions. Exis...

DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction

Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-q...

RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction

Cancer survival prediction using multi-modal medical imaging presents a critical challenge in oncology, mainly due to the vulnerability of deep lear...

CSASN: A Multitask Attention-Based Framework for Heterogeneous Thyroid Carcinoma Classification in Ultrasound Images

Heterogeneous morphological features and data imbalance pose significant challenges in rare thyroid carcinoma classification using ultrasound imagin...

A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans

Pancreatic cancer, characterized by its notable prevalence and mortality rates, demands accurate lesion delineation for effective diagnosis and ther...

Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer

This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pat...

Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging

Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity o...

MANIFEST: Multiomic Platform for Cancer Immunotherapy.

Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatmen...

TumorTwin: A python framework for patient-specific digital twins in oncology

Background: Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and t...

Automated segmenta-on of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023

Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magneti...

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning.

Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour bioma...

MMsurv: a multimodal multi-instance multi-cancer survival prediction model integrating pathological images, clinical information, and sequencing data.

Accurate prediction of patient survival rates in cancer treatment is essential for effective therapeutic planning. Unfortunately, current models often...

[Usage of artificial intelligence in the clinical practice of urologists in observations with renal parenchymal neoplasms].

OBJECTIVE: to assess the needs and attitudes of urologists regarding the use of technologies related to artificial intelligence, particularly the web ...

Leukaemia Stem Cells and Their Normal Stem Cell Counterparts Are Morphologically Distinguishable by Artificial Intelligence.

Leukaemia stem cells (LSCs) are a rare population among the bulk of leukaemia cells and are responsible for disease initiation, progression/relapse an...

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant...

[ARTIFICIAL INTELLIGENCE-ASSISTED LITERATURE REVIEW: A CASE STUDY IN FUMARATE HYDRATASE-DEFICIENT RENAL CELL CARCINOMA].

Fumarate hydratase-deficient renal cell carcinoma (FHdRCC) is a rare and aggressive form of kidney cancer that presents significant therapeutic challe...