Other Cancers

Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics.

OBJECTIVES: Identifying patients at risk of chemoresistant osteosarcoma enables risk-adapted management. This study aimed to predict chemoresistant osteosarcoma using baseline clinical and magnetic resonance (MRI)-derived radiomics features, with histological response as the reference standard and external validation included. MATERIALS AND METHODS: This retrospective single-center study included ...

Deep Learning Algorithm Based on Contrast-Enhanced Ultrasound Potentially Optimizes Treatment Strategies for Solitary Primary Hepatocellular Carcinoma.

OBJECTIVE: We aimed to propose a prognostic framework using a dual-branch Vision Transformer (ViT) deep learning (DL) architecture for stratifying recurrence risk in solitary primary hepatocellular carcinoma (HCC) patients undergoing surgical resection (SR) or thermal ablation (TA) and explore its potential value as a tool for aiding therapy-related discussions. METHODS: This two-center study enro...

Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.

Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present sub...

A temporal keyword co-occurrence network mining framework for detecting structural transitions in cancer biomarker research (2006-2023).

Biomarker research for cancer diagnosis and prognosis has rapidly expanded technologically and thematically, along with advancements in molecular diag...

Multicenter machine learning model for assessing the impact of malignancy on in-hospital mortality in heart failure patients: a clinical decision support system with interpretable artificial intelligence.

Heart failure (HF) and malignancy represent two major global health burdens that frequently coexist and lead to poor clinical outcomes. However, the s...

Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.

Prediction of patient-level drug response is critical for precision oncology but remains limited by the scarcity of clinical data. While machine learn...

AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases.

OBJECTIVES: This study evaluates the clinical utility of an artificial intelligence (AI)-driven volumetric approach for assessing treatment response i...

TFRC-mediated immune exclusion as a therapeutic vulnerability in cervical cancer: A comprehensive analysis based on the cancer immunoediting process.

BACKGROUND: Advanced and recurrent cervical cancer (CC) remains a clinical challenge due to limited therapeutic options and a poor 5-year survival rat...

Habitat analysis and other AI technologies for Ki-67 prediction in hepatocellular carcinoma: a multi-center study.

OBJECTIVES: To develop various artificial intelligence (AI) models including radiomics, deep transfer learning (DTL) and habitat analysis models, as w...

Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types.

BACKGROUND: Cancer is a complex set of diseases caused by the dysregulation of cell proliferation, differentiation, and apoptosis, ultimately leading ...

Multicenter validation and randomized crossover reader evaluation of deep learning-assisted tri-sequence three-dimensional MRI segmentation for hypopharyngeal tumor.

BACKGROUND AND PURPOSE: Accurate MRI-based target delineation for hypopharyngeal squamous cell carcinoma (HPSCC) is clinically important but expertise...

Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients.

BACKGROUND: Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment...

Prospects and Challenges of Personalized Immunotherapy in Breast Cancer Treatment and Long-term Cancer Management.

Breast cancer is highly heterogeneous,and different molecular subtypes exhibit significant variations in their response to immunotherapy.While signifi...

Unraveling the Multifunctional and Translational Paradigm of Nanoparticulate Systems against Colorectal Cancer.

Colorectal cancer (CRC) is a health burden due to its high mortality rate, recurrence rate, and drug resistance. The limitations of traditional method...

Novel biomarkers driving treatment selection in metastatic castration-sensitive prostate cancer: fundamentals of personalized clinical decision making.

INTRODUCTION: Prostate cancer is well known to be androgen-dependent, with growth directly relying on the androgen receptor signaling pathway. In fact...

Nanomedicine carrier-based combined antitumor strategy integrating phototherapy and immunotherapy.

INTRODUCTION: The combination of phototherapy (PTT/PDT) and immunotherapy holds promise for cancer treatment but is hindered by poor spatiotemporal co...

APTw CEST MRI in therapy-naive IDH-wildtype glioblastoma: insights into tumor heterogeneity and molecular subtypes.

PURPOSE: Advanced MRI techniques may provide non-invasive insight into the molecular heterogeneity of glioblastoma. Amide proton transfer-weighted (AP...

Genome-wide variation in cell-free DNA end motif entropy predicts immunotherapy response in head and neck cancer.

BACKGROUND: Minimally invasive biomarkers predicting immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinical ...

Temporally consistent longitudinal brain tumor segmentation using a temporal spatial transformer network.

Proper monitoring of tumor progression and evaluation of treatment responses highly depend on longitudinal brain tumor segmentation from MRI data. Cur...

Robust transcriptomic signatures of Alzheimer's disease progression: validated explainable AI approach.

The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer's disease (AD) progression and clinical translation. Current tr...