Oncology/Hematology

Multimodal ultrasound and artificial intelligence for characterization of thyroid nodules in Hashimoto's thyroiditis: current challenges and future perspectives.

Patients with Hashimoto's thyroiditis (HT) frequently present with concurrent nodular lesions such as nodular goiter and thyroid cancer (especially papillary thyroid carcinoma, PTC), and their risk of PTC is significantly higher than that of non-HT individuals. Patients with HT exhibit varying degrees of glandular fibrosis, leading to some areas having a "nodular-like" appearance. Some of these no...

Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.

CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However, recurrence or failure to achieve remission remains relatively frequent, underscoring the need for improved early risk stratification. OBJECTIVE: To develop and evaluate an interpretable machine learning model for predicting recurrence or nonremissi...

Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.

PURPOSE: Hypervascular pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) represent a classic diagnostic mimicry on contrast-...

Machine learning based classification of aggressive and malignant renal tumors from multimodal data.

This study aimed to develop and evaluate a machine learning pipeline using multiphase contrast-enhanced CT images and clinical data to classify renal ...

Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study.

This multicenter study aims to enhance the preoperative prediction of pathological invasiveness in clinical stage I lung adenocarcinoma (LUAD) by deve...

Transformer-based classification and interpretability of NR3C1 expression patterns in OSCC: Metabolic adaptation insights.

INTRODUCTION: Oral squamous cell carcinoma (OSCC) involves several oncogenic proteins for survival. Protein sequence classification is a fundamental c...

Generation of machine-learning derived cancer vulnerability indicator to determine the spatial burden of cancer outcomes.

BACKGROUND: Due to the difficulty of obtaining population-based individual-level data, ecological studies are often used to explore factors related to...

Predicting treatment response to neoadjuvant chemotherapy in locally advanced rectal cancer: A combined deep learning and machine learning approach utilizing longitudinal multi-sequence MRI.

PURPOSE: To develop and validate deep leaning-based machine learning models using longitudinal multi-sequence MRI for predicting treatment response of...

Driver gene mutations and clinical features predict bone metastasis risk in NSCLC: a logistic regression model.

Bone metastasis affects approximately 40% of patients with non-small cell lung cancer (NSCLC), significantly impacting patient survival and prognosis....

Non-invasive prediction of Ki-67 expression in gastric cancer using AI-based dual-energy CT: a multicenter study.

OBJECTIVE: To develop and validate a machine learning model based on quantitative parameters of dual-energy CT (DECT) virtual monoenergetic images (VM...

Biomarkers and advances in AML-MRC: from bench to bedside.

Acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) represents a high-risk subtype of AML, characterized by poor prognosis and limite...

The optimal conditioning intensity of stem cell transplantation for acute myeloid leukemia in complete remission.

UNLABELLED: This study aimed to identify patient groups in which myeloablative conditioning (MAC) or reduced-intensity conditioning (RIC) regimens ind...

Dual-Stream Deep Feature and Cell Phenotype Fusion Model for the Diagnosis of Myeloproliferative Neoplasms.

Diagnosing myeloproliferative neoplasms (MPNs) is challenging due to the nuanced and overlapping clinical manifestations of the various subtypes. Prec...

Privacy-preserving multimodal fusion for Alzheimer's staging: A federated vision transformer framework with explainable AI.

Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multim...

Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

In the oil industry, accurate flow rate determination and control in pipelines are critical for ensuring operational efficiency. However, most convent...

Non-invasive screening for ovarian cancer by combining serum SERS with interpretable machine learning models.

Early identification of malignant ovarian tumors is critical for informing treatment decisions and enhancing patients' quality of life. As the third m...

Top Ten Tips Palliative Care Clinicians Should Know About the Future of Artificial Intelligence.

Artificial intelligence (AI) is transforming health care and has implications for palliative care (PC) and serious illness communication (SIC). This a...

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide...

An Automated Hybrid Deep Learning-based Model for Breast Cancer Detection using Mammographic Images.

INTRODUCTION: Breast cancer is a disease in which abnormal breast cells grow uncontrollably and develop into a tumor. It is one of the most common can...

Neoadjuvant Radiation is Causally Linked to Increased Operative Time and Perioperative Blood Transfusion in Pancreatic Ductal Adenocarcinoma.

INTRODUCTION: Blood transfusion in patients undergoing surgical resection for pancreatic ductal adenocarcinoma (PDAC) is associated with worse outcome...