Oncology/Hematology

Research on Lung Cancer Classification Based on Multidimensional Hematological Indicators and Machine Learning Models.

Accurate subtyping of lung cancer is crucial for developing personalized treatment plans and improving patient outcomes. This study established machine learning models for lung cancer subtyping by integrating multidimensional hematological indicators, offering advantages such as non-invasiveness, repeatability, and the capability for dynamic disease monitoring. The study utilized data from 771 lun...

Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer.

Spatial cellular context is crucial in shaping intratumor heterogeneity. However, understanding how each tumor establishes its unique spatial landscape and what factors drive the landscape for tumor fitness remains significantly challenging. Here, we analyze over 2 million cells from 50 tumor biospecimens using spatial single-cell imaging and single-cell RNA sequencing. We develop a deep learning-...

A comparative analysis of data-driven models for breast cancer survival prediction.

Breast cancer is the most frequently diagnosed cancer among women and persists as a societal problem worldwide. It remains a leading cause of cancer a...

Lactate metabolism-driven tumor heterogeneity and molecular signatures in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive liver malignancy with limited therapeutic options and poor prognosis. Recent ...

Emerging trends and converging evidence in tumor evolution: A comprehensive review.

BACKGROUND: Tumor evolution is a spatiotemporal dynamic process orchestrated by the interplay of genetic mutations, epigenetic reprogramming, and bidi...

Optimal management of oligometastatic prostate cancer: current state and future directions.

PURPOSE: Oligometastatic prostate cancer (oligoPCa) represents a clinical state of limited metastatic spread in which metastasis-directed therapy (MDT...

A perturbed multilayer perceptron approach to predicting distant metastatic sites of cancer patients.

Cancer metastasis accounts for about 90% of cancer-related mortality, but is difficult to predict. In particular, distant metastasis is more difficult...

Artificial Intelligence Impact on Radiologist Performance in Breast Cancer Screening with Digital Breast Tomosynthesis.

PURPOSE: Assess impact of artificial intelligence (AI) on radiologists' detection of cancer on digital breast tomosynthesis (DBT) exams based on densi...

Immunotherapy in Triple-Negative Breast Cancer: From Molecular Mechanisms to Precision Medicine-Overcoming Resistance and Optimizing Clinical Outcomes.

Triple-negative breast cancer (TNBC) is the most violent type of breast cancer, in which estrogen receptors (ER), progesterone receptors (PR), and hum...

Moving Beyond Morphology: Toward a Morpho-Molecular Classification of Pleural Mesothelioma.

Despite recent advances in the treatment of pleural mesothelioma, it remains a challenging and heterogeneous disease, with limited options for patient...

AI decision support for increasing prostate biopsy efficiency: a retrospective multicentre, multiscanner study.

OBJECTIVES: To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS) for optimising prostate biopsy d...

Multidimensional analysis of postoperative nursing challenges in oral cancer surgery: a retrospective study of 245 patients and proposal of the PROTECT mode.

PURPOSE: The quality of postoperative care for oral tumors critically influences patient prognosis and quality of life; however, current nursing syste...

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis.

PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated int...

Distinction Between Benign and Borderline/Malignant Phyllodes Tumor in Breast Mammography and Ultrasound Based on Radiomics Methods.

RATIONALE AND OBJECTIVES: This study aims to evaluate whether radiomics methods used on breast mammography (MG) and ultrasound (US) could distinguish ...

Multiparametric MRI-Based Deep Learning and Radiomics for Predicting Progression-Free Survival Benefit in Patients with Hepatocellular Carcinoma Treated with Immunotherapy and Targeted Therapy Plus Transarterial Chemoembolization: A Bicentric Study.

RATIONALE AND OBJECTIVES: The non-invasive biomarkers for predicting progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) t...

Single-cell transcriptomic analysis and machine learning identify ATAD3A as a key gene that stabilizes mitochondrial-endoplasmic reticulum membranes, promoting bladder cancer progression.

BACKGROUND: Mitochondria-associated endoplasmic reticulum membranes (MAM) play a critical regulatory role in cancer, yet their function in bladder can...

Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.

Over recent years, several deep learning (DL) models have been presented to predict colorectal cancer (CRC) patient survival directly from haematoxyli...

CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction.

In cancer research, identifying cancer subtypes and evaluating prognosis are crucial for personalized diagnosis and treatment of cancer. With the adva...

Artificial intelligence-generated synthetic data for cancer research and clinical trials.

Synthetic data, generated through advanced artificial intelligence models, are gaining traction in healthcare research, particularly in high-stakes fi...

Artificial intelligence differentiates prefibrotic primary myelofibrosis with thrombocytosis from essential thrombocythemia using digitized bone marrow biopsy images.

Prefibrotic primary myelofibrosis (prePMF) and essential thrombocythemia (ET) are distinct myeloproliferative neoplasms (MPNs) with overlapping clinic...