Oncology/Hematology

Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.

BACKGROUND: Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain. OBJECTIVE: To explore the ability of machine learning to predict cancer postoperative complications and early recurrence. METHODS: From the creation of the database until October 4, 2024, we conducted a comprehensive search of PubMed, Web of Science (WoS), Embase, Scop...

Colorectal cancer sidedness: prognostic implications and the predictive role of artificial intelligence.

BACKGROUND: Colorectal cancer (CRC) is a biologically heterogeneous disease in which tumor sidedness has emerged as a relevant prognostic factor. Conventional TNM staging does not incorporate several clinically and biologically meaningful variables that may influence outcomes. In this context, artificial intelligence (AI) based approaches offer an opportunity to integrate complex clinicopathologic...

Assessing biomarkers for patients with breast cancer who are suited for adjuvant therapy.

INTRODUCTION: Biomarker-guided stratification is essential for optimizing adjuvant systemic therapy in early-stage breast cancer, requiring a balance ...

Transcriptionally informed nucleosome profiling of circulating cell-free DNA predicts breast cancer recurrence.

Cell-free DNA (cfDNA) offers a minimally invasive approach to capture genomic and epigenetic dynamics during cancer progression. We performed targeted...

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.

BACKGROUND: Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. OBJECTIVES: The purpose of this study was to as...

Precision Management of Autoimmune Ocular Complications: Th17 Mechanisms and Therapeutic Innovations.

Precision management of ocular complications in systemic autoimmune diseases, such as Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), Beh...

Advances in Digital Pathology and Artificial Intelligence in the Diagnosis of Myeloid Neoplasms.

Recent advances in digital pathology and artificial intelligence (AI) are transforming our ability to diagnose myeloid neoplasms, including acute myel...

Artificial Intelligence-Powered Cystoscopy Diagnostic Support System: Clinical Application of Multiarchitecture Deep Learning Models.

PURPOSE: Cystoscopy is a crucial diagnostic tool in urology for the detection and evaluation of bladder lesions. However, its diagnostic accuracy (ACC...

Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.

PURPOSE: For patients with oral cavity cancer (OCC) and oropharyngeal cancer (OPC), the time between initial diagnosis and start of treatment can have...

Up-regulation of SLC26A3 enhances oxaliplatin sensitivity in colorectal cancer.

The role of SLC26A3 in the sensitivity to oxaliplatin, a widely used chemotherapy drug for colorectal cancer (CRC), remains unclear. This study aimed ...

Development and Evaluation of Foundational Cancer Science Graduate Courses in the Evolving Landscape of Cancer Research.

Roswell Park is a free-standing Comprehensive Cancer Center in Buffalo, New York and serves as the academic home to a doctoral program in Cancer Scien...

Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.

BACKGROUND: Cardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, sup...

Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for spinal metastases.

PURPOSE: We aimed to develop and internally validate prediction models for one-month postoperative performance status (PS) after surgery for spinal me...

Review of systemic therapy in HCC with an approach to response assessment in clinical practice.

Systemic therapy for hepatocellular carcinoma (HCC) has undergone rapid transformation over the past decade, significantly expanding treatment options...

Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors.

PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, imp...

RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD), the predominant histological subtype of non-small cell lung cancer, remains a leading cause of cancer-related ...

Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.

Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can pr...

Machine learning-based prediction of hepatocellular carcinoma risk in steatotic liver disease: A nationwide cohort study.

BACKGROUND AND AIMS: Steatotic liver disease (SLD) has emerged as an important risk factor for hepatocellular carcinoma (HCC), often in the absence of...

Nanomedicine-based cancer immunotherapy: translational barriers, mechanistic strategies, and future perspectives.

Nanomedicine-based cancer immunotherapy integrates nanotechnology with immune modulation, representing a promising strategy to improve both the effica...

Machine-learning-model based tool for screening bone metastases from lung cancer patients in primary care practice.

PURPOSE: This study aimed to use the machine-learning methods to predict bone metastasis (BM) in patients with lung cancer. METHODS: This study includ...