Oncology/Hematology

From data to treatment plan: An AI-driven path for automated breast radiotherapy planning.

BACKGROUND: Breast cancer is one of the most prevalent malignancies in women, with radiotherapy (RT) playing a key role in its treatment. Advances in RT techniques, such as 3D conformal radiotherapy (3D-CRT) and intensity-modulated radiotherapy (IMRT), have improved dose precision and reduced side effects. However, RT modality selection and treatment planning remain manual, time-consuming, and sub...

Machine Learning Reveals the Association Between Gene Expression and Immune Infiltration in Colorectal Cancer: A Comprehensive Study From Single-Cell to Survival Analysis.

Colorectal cancer (CRC) is one of the most common causes of cancer mortality globally. Analysis of immune cell infiltration patterns in the tumour microenvironment (TME) is critical to treatment outcomes, but the molecular mechanisms which regulate this process are still poorly understood. We uniquely applied machine learning to single-cell RNA sequencing analysis to unravel the complex interactio...

Advanced Drug Delivery Strategies for Overcoming Biological Barriers: Tumor Microenvironment and Blood-Brain Barrier.

Therapeutic efficacy for malignancies and neurological disorders is fundamentally restricted by biological barriers, particularly the complex tumor mi...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Diagnostic and Technological Advances in Magnetic Resonance (Focusing on Imaging Technique and the Gadolinium-Based Contrast Media), Computed Tomography (Focusing on Photon Counting CT), and Ultrasound-State of the Art.

Magnetic resonance continues to evolve and advance as a critical imaging modality for disease diagnosis and monitoring. Hardware and software advances...

Lung-DDPM: Semantic Layout-Guided Diffusion Models for Thoracic CT Image Synthesis.

With the rapid development of artificial intelligence (AI), AI-assisted medical imaging analysis demonstrates remarkable performance in early lung can...

Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers.

PURPOSE: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic s...

Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications, Challenges, and Future Directions.

This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approa...

Adverse reactions of PD-1/PD-L1 inhibitors in cancer: FAERS database analysis and protocols to mitigate immune-related events in elderly patients and when using pembrolizumab and atezolizumab.

OBJECTIVE: This study aimed to characterize adverse drug reactions (ADRs) associated with programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) in...

Virtual Multi-Phase Contrast Enhanced Liver MRI Using Deep Learning for Evaluating Hepatocellular Carcinoma.

A deep learning (DL) model was developed to generate contrast-enhanced MRI (CE-MRI) at multiple enhancement phases (arterial, portal venous, transitio...

A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with Colorectal Cancer: A Comparative Study with Radiologists.

Purpose To evaluate the performance of a deep learning algorithm (DLA) for detecting liver metastases (LM) in patients with colorectal cancer (CRC) ac...

Cutting-Edge Technologies to Decode Tumor Microenvironment in Multiple Myeloma.

Multiple myeloma (MM) is recognized as a malignancy shaped by its complex tumor microenvironment (TME), which fuels disease progression and therapeuti...

Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.

Purpose To develop a deep learning-based, computer-aided diagnosis (CADx) model for preoperative classification of ovarian tumors (OTs) on CT scans an...

Region-Based Segmentation of Lymph Node Metastases in Whole-Slide Images of Colorectal Cancer: A Pilot Clinical Study.

BACKGROUND: Digital technologies and artificial intelligence (AI) are transforming medical diagnostics, particularly in pathology. This study presente...

A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.

Purpose To develop a self-supervised chest CT foundation model and evaluate its performance in lung cancer clinical tasks. Materials and Methods In th...

Impact of Annotation Level on Multisequence MRI Models for Preoperative Microvascular Invasion Prediction in Hepatocellular Carcinoma.

Purpose To evaluate the performance of deep learning models integrating multimodal data for predicting microvascular invasion (MVI) in hepatocellular ...

Machine Learning-Driven Prognostic Model Integrating Lymphocyte-to-C-Reactive Protein Ratio and TNM Staging in Gallbladder Cancer.

BACKGROUND: A comprehensive preoperative assessment of the patient's physical condition is crucial for predicting the prognosis of patients undergoing...

Cancer-associated fibroblasts are associated with CD8+ T cell depletion and poor prognosis in colorectal adenocarcinoma: a multi-omics and machine learning analysis.

Fibroblastic proliferation in various tumor microenvironments influences cancer survival through complex interactions with diverse immune responses. T...

Identification and Validation of cGAS-STING Pathway-Associated Predictive and Therapeutic Models for Esophageal Squamous Cell Cancer Patients via Artificial Intelligence and Multi-Omics.

BACKGROUND: Esophageal squamous cell cancer (ESCC) is a malignancy derived from the Esophagus, and dysregulation of the cGAS-STING pathway contributes...

Simulation-free spine palliative radiotherapy enabled by AI-adapted diagnostic CT.

BACKGROUND: Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is ...