Oncology/Hematology

Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.

PURPOSE: The histological grade of hepatocellular carcinoma (HCC) is an important factor associated with early tumor recurrence and prognosis after surgery. Developing a valuable tool to assess this grade is essential for treatment. This study aimed to evaluate the feasibility and efficacy of a deep learning-based three-dimensional super-resolution (SR) magnetic resonance imaging radiomics model f...

Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.

Cervical cancer stands out among various cancers due to its potential for prevention and eradication, mainly through vaccination and proactive screening measures. However, there are still a large number of women in low- and middle-income countries who need to undergo cervical cancer screening. Conventional cervical cancer screening approaches possess distinct benefits and drawbacks regarding sensi...

A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.

Medical imaging has become an essential tool in the diagnosis and treatment of various diseases, and provides critical insights through ultrasound, MR...

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acq...

Metabolic alterations driven by LDHA in CD8 + T cells promote immune evasion and therapy resistance in NSCLC.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Despite advancements in treatment, prognosis for patients wi...

Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination.

BACKGROUND: Lung cancer is a highly aggressive and lethal cancer requiring prognostic and predictive biomarkers for improving patient outcomes. Here, ...

Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.

Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore ...

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within ...

Recent advances of engineering cell membranes for nanomedicine delivery across the blood-brain barrier.

The blood-brain barrier (BBB) poses a major challenge to the effective delivery of therapeutic agents for the treatment of central nervous system (CNS...

Evaluating cell-free DNA integrity index as a non-invasive biomarker for neoadjuvant chemotherapy in colorectal cancer patients.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is gaining attention as a treatment for advanced colorectal cancer owing to its potential to improve surgic...

Dynamic abdominal MRI image generation using cGANs: A generalized model for various breathing patterns with extensive evaluation.

Organ motion is a limiting factor during the treatment of abdominal tumors. During abdominal interventions, medical images are acquired to provide gui...

Deep Learning Model Based on Dual-energy CT for Assessing Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma.

BACKGROUND: Accurate detection of lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC) is crucial for treatment planning. This study dev...

Corrigendum to 'CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy' [Acad Radiol 32/6 (2025) 3397-3409].

Microfluidic Biochip-Based Multiplexed Profiling of Small Extracellular Vesicles Proteins Integrated with Machine Learning for Early Disease Diagnosis.

Accurate early diagnosis is essential for preventing diseases and improving cure and survival rates. There are no reliable early-diagnosis biomarkers ...

Shape matters: inferring the motility of confluent cells from static images.

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the h...

From combination early detection to multicancer testing: shifting cancer care toward proactive prevention and interception.

Identifying the presence of tumors at a very early stage or deciphering the process underlying their development can enable the interception of pro-ma...

The evolving therapeutic landscape of Graves' disease in adults: present and future.

The therapeutic landscape of Graves' hyperthyroidism has been rapidly evolving in the last few years. There has been a shift worldwide towards antithy...

pyDOSEIA: A Python Package for Radiological Impact Assessment during Long-term or Accidental Atmospheric Releases.

pyDOSEIA is a Python package designed for meteorological data processing and radiological impact assessment in diverse scenarios, including nuclear an...

AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract associated with an increased risk of colorectal canc...

Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors.

Identifying materials with optimal optoelectronic properties for targeted applications represents both a critical need and a persistent challenge in o...