Other Cancers

An accurate, straightforward computer vision algorithm for optimal tumor-feeding visualization in cone-beam computed tomography hepatic arteriography: A preliminary study.

AIM: Although standardized 3D volume rendering techniques (VRT) and embolization guidance visualize and identify tumor-feeding arteries, current vessel tracking software lacks automatic angle recommendations. This forces an operator, e.g. an interventional radiologist, to leave an ongoing procedure to manually manipulate the system and find the best angle for each feeding vessel-requiring time-con...

Context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in renal cell carcinoma.

The invasiveness prediction in renal cell carcinoma (RCC) is of significant importance for the decision of clinical surgical plans and the patients' prognosis. Currently, besides invasive pathological assessment, it mainly relies on observation through computed tomography (CT) imaging. However, limitations of human vision and qualitative descriptions restrict the accuracy of the diagnosis of renal...

Hereditary syndromes and RCC: what radiologists need to know.

Hereditary renal cell carcinoma (RCC) accounts for approximately 5-8% of all renal cancers. This review provides a comprehensive overview of the seven...

Development and validation of a personalized web-based calculator of aggressive recurrence after surgery for early-stage hepatocellular carcinoma by machine learning.

BACKGROUND: Aggressive recurrence (AR) is an important factor affecting prognosis after surgery for hepatocellular carcinoma (HCC). This study aimed t...

Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.

BACKGROUND: Reliable tools for early prediction of treatment response to androgen deprivation therapy (ADT) plus novel androgen receptor pathway inhib...

Functional profiling of the chaperone systems interactome in breast cancer using experimental and machine-learning data.

Breast cancer heterogeneity stems from diverse molecular alterations, including proteostasis loss due to chaperone system dysfunction. However, the im...

AI-assisted literature screening: A hybrid approach using large language models and retrieval-augmented generation.

OBJECTIVE: Current systematic literature reviews largely rely on manual screening of articles retrieved through keyword search, which is time-consumin...

Artificial intelligence for TNM staging in NSCLC: a critical appraisal of segmentation utility in [1⁸F]FDG PET/CT.

PURPOSE: This study aims to investigate whether a diagnostic AI model can effectively support lesion detection and staging in non-small cell lung canc...

Explainable artificial intelligence for multi-modal cancer analysis: From genomics to immunology.

Cancer remains one of the most biologically intricate and heterogeneous diseases, with variability spanning genetic, epigenetic, metabolic, spatial, a...

Screening tools and strategies for early detection of oral cancer and potentially malignant disorders in rural and low-resource populations: A systematic review.

Oral cancer and its precursor lesions, known as oral potentially malignant disorders (OPMDs), present significant public health challenges due to high...

IDH Mutation Classification in Nonenhancing Gliomas: A Comparison of Habitat and Whole-Tumor Transfer Learning Strategies.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the diagnosis and management of nonenhancing gliomas, undersc...

Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study.

OBJECTIVES: To develop and evaluate the predictive efficacy of a combined model incorporating clinical parameters and PET-based radiomics signature (R...

Metabolic and epigenetic reprogramming in circulating tumor cells: Advances in liquid biopsy and cancer management.

Circulating tumor cells are key drivers of cancer metastasis and represent powerful biomarkers for real-time monitoring of disease progression through...

Molecular targeted therapies, omics, and AI based theranostics approaches for the treatment of prostate cancer.

Cancer is a disease that begins with genetic and epigenetic alterations occurring in specific cells, some of which can spread and migrate to other tis...

Integrating multiphase MRI surrogates to improve microvascular invasion detection and recurrence risk stratification in HCC.

BACKGROUND & AIMS: Microvascular invasion (MVI) is a key determinant of recurrence and poor outcomes in hepatocellular carcinoma (HCC), yet accurate p...

Lipidomic machine learning predictor for progression of gastric cancer.

BACKGROUND: Gastric cancer (GC) is a leading cause of global cancer mortality, characterized by aggressive progression and poor prognosis. The develop...

Integrated multi-omics analyses identified the H3K18la-based spatiotemporal characteristics and risk-stratified treatment strategy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, characterized by high mortality, recurrence, and metastasis. Despite a...

Drug Repurposing in Oncology: A Strategic Pathway to Unlocking New Therapeutic Potential.

The growing number of cancer cases and deaths highlights the urgent need for innovative treatment approaches. One technique that has lately been recog...

Regulators of homologous recombination deficiency identified by machine learning using somatic multi-omics data.

Homologous recombination deficiency (HRD) is a critical biomarker for guiding targeted therapies, yet the full range of somatic alterations driving HR...

Vibroacoustic detection of inclusions in an elastomeric tissue phantom using a multilayer perceptron classifier: A proof-of-concept study.

Accurate detection of tumor boundaries is critical for the success of oncologic surgical intervention. Traditionally, palpation can handover important...