Latest AI and machine learning research in oncology/hematology for healthcare professionals.
The aim of this study is to establish a deep learning model for predicting the pathological grade of oral squamous cell carcinoma(OSCC) based on whole slide images (WSIs). The histopathological images of 257 patients with OSCC were downloaded from TCGA database and randomly divided into training set, validation set and test set at a ratio of 7:2:1. The CPTAC-OSCC dataset, which contains 165 pathol...
BACKGROUND: Cancer poses a significant health threat, causing millions of deaths annually. Although chemotherapy-based comprehensive therapies are common, their low accuracy and severe side effects limit effectiveness. Metal-organic frameworks (MOFs), with their superior biocompatibility and stability, show great promise for drug delivery and cancer treatment. This study aims to explore the potent...
BACKGROUND AND PURPOSE: Colorectal cancer (CRC) is the third most prevalent malignancy in the gastrointestinal tract and the second leading cause of c...
OBJECTIVES: To evaluate the predictive performance of artificial intelligence (AI) methods using pre-treatment PET-based imaging for outcome predictio...
Elucidating the anti-tumor role of tumor-draining lymph nodes (tdLNs) in patients could offer critical mechanistic insight and shift therapeutic strat...
Multispecific antibodies have redefined the immunotherapeutic landscape in hematologic malignancies. Bispecific antibodies (BsAbs), which redirect cyt...
Endometrial adenocarcinoma carries a bleak prognosis, and the molecular markers that evaluate the progression of endometrial adenocarcinoma to advance...
BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by high heterogeneity, and its molecular features and microenvironment complexity contribu...
PURPOSE: Investigate spleen diameter (d) and volume (v) relationship in patients with hematologic malignancies (HM) by determining volumetric threshol...
BACKGROUND: Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy re...
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Availab...
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide, with a 5-year survival ra...
OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) ra...
Cancer remains one of the most complex diseases faced by humanity, with over 200 distinct types, each characterized by unique molecular profiles that ...
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and ...
Long-range intercellular communication is essential for multicellular biological systems to regulate multiscale cell-cell interactions and maintain li...
This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for pot...
PURPOSE: To establish prediction models using Shapley Additive exPlanations (SHAP) and multiple machine learning (ML) algorithms to identify clinical ...
Cell-to-cell communication (CCC) is prominent for cell growth and development as well as tissue and organ formation. CCC inference can help us to deep...
PURPOSE: Lymph node metastasis (LNM) significantly affects prognosis and treatment strategies in non-small cell lung cancer (NSCLC). Current diagnosti...