Oncology/Hematology

Lung cancer segmentation Using the Att-U-Net Model on PET-CT Images.

INTRODUCTION: Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the need for early and accurate diagnosis. Precise segmentation of lung regions and tumors in imaging is critical for effective diagnosis and treatment. This study investigates the Attention U-Net (Att-U-Net) model for segmenting lung tissues and tumors in positron emission tomography-computed tomography...

Integrating Multi-Omics and Artificial Intelligence for Personalized Breast Cancer Management: A Guide to Clinicians.

Breast cancer's (BC) diverse nature and global impact demand tailored clinical strategies. Conventional screening methods often fall short in early detection and individualized risk assessment. By merging multi-omics technologies such as genomics, transcriptomics, proteomics, and metabolomics with artificial intelligence (AI), clinicians gain powerful tools to navigate this complexity. AI's abilit...

Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.

Wildfire smoke exposures are increasingly common, consisting of complex mixtures of gases and particulates known to cause diverse pulmonary health eff...

Co-repression of Yap1 and Sox9 Abrogates Established Cholangiocarcinoma by Eliminating Transcriptional Compensation.

BACKGROUND/AIMS: Intrahepatic cholangiocarcinoma (iCCA) represents an unmet clinical need due to its increasing incidence, aggressive biology, and lim...

A deep learning approach to assess transendothelial cell trafficking performance.

Transendothelial migration (TEM), is a complex, multistep process impacted by diseases like autoimmune disorders and cancer. Deciphering aspects of th...

Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining.

The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and prognosi...

PRET is a few-shot system for pan-cancer recognition without example training.

Pathological examination stands as the cornerstone in cancer diagnosis, impacting millions worldwide annually. With the shortage of pathologists globa...

Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid carcinoma.

RATIONALE AND OBJECTIVE: The status of cervical lymph node metastasis(LNM) in Papillary thyroid carcinoma(PTC) can affect the patient's treatment plan...

Prospectively screening for venous thromboembolism in patients with esophagectomy for cancer improves survival: The complexity of simplicity.

BACKGROUND: We implemented a prospective screening program to detect deep venous thromboembolism and define its prevalence in patients with esophageal...

Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from benign hyperplasia.

BACKGROUND AND OBJECTIVE: Differentiating T1-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) is challenging. This study aims to cons...

Predicting neoadjuvant therapy response in breast cancer from preoperative biopsy via spatial-semantic-differential learning and interpretable clinicopathological-guided fusion.

Predicting pathological complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer remains challenging due to high tumor heterogeneity and ...

Prediction of post-stroke brain swelling using biomechanical modelling and deep neural networks.

Malignant stroke is a life-threatening condition, with mortality rates reaching up to 80% among patients managed conservatively. Brain swelling volume...

Biological basis and clinical translation prospects of circulating cell-free DNA in precision management of breast cancer (Review).

Circulating cell-free DNA (cfDNA) has emerged as a valuable non-invasive biomarker that provides unique insights into the spatial and temporal heterog...

Cutting-edge advances in endocrine therapy for breast cancer (Review).

Endocrine therapy remains one of the primary treatment modalities for estrogen receptor (ER) positive breast cancer, serving a pivotal role in improvi...

Constructing a corpus of hematologic pathology notes for the fine-tuning of BERT models for named entity recognition.

BACKGROUND: Microscopic, immunologic, and chemical testing play a major role in the diagnostic process of hematologic cancer patients. Pathologists re...

Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.

Lung cancer (LC) is one of the leading causes of death globally. Early detection is essential for saving lives and ensuring effective treatment for pa...

Integrative Machine Learning and Network Pharmacology Approach to Uncover Phytochemical Therapeutics for Melanoma and Breast Cancer.

INTRODUCTION: Melanoma and Breast cancer are the leading cancers that cause thousands of deaths annually worldwide. Although several early identificat...

Clinical and immunological significance of FOXO1 as a biomarker of improved response to immune checkpoint plus tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma.

BACKGROUND: Predicting response to immune checkpoint inhibitor plus tyrosine kinase inhibitor (IO+TKI) therapy in metastatic renal cell carcinoma (mRC...

Gut microbial extracellular vesicles modulate the development of metabolic dysfunction-associated steatohepatitis through the gut-liver axis.

Metabolic dysfunction-associated steatohepatitis (MASH) represents a growing global health challenge due to its propensity to progress to irreversible...

Application of contrast-enhanced CT-based radiomics with machine learning to predict WHO/ISUP grading in clear cell renal cell carcinoma.