Other Cancers

Evolving strategies in prostate cancer: Emerging approaches and unmet needs from the Bridging the Gaps in Prostate Cancer expert panel.

BACKGROUND: The expansion of treatment options for prostate cancer (PC) has improved disease-specific and overall survival outcomes but has also raised questions about the optimal level of treatment needed for patients based on their individual prognosis and accounting for potential toxicity, incorporating quality of life considerations. METHODS: A panel of experts met to discuss current controver...

Multimodal hybrid mamba classification model for tumor pathological grade prediction using magnetic resonance images.

Malignant tumors present a significant global health challenge, and accurate pathological grading is essential for personalized treatment. Traditional grading methods, which rely on invasive biopsies, are limited by tumor location. In contrast, magnetic resonance imaging (MRI) offers a non-invasive, high-resolution tool with multi-sequence MRI (e.g., T1, T2, T1C) enabling comprehensive tumor asses...

The emerging role of machine learning-based methods in cancer classification using microRNA.

Early detection and accurate classification of cancer are crucial to improving patient outcomes. Diagnosis and classification of tumors using conventi...

Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.

PURPOSE: Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detect...

Deep learning for histopathological diagnosis of esophageal squamous cell carcinoma in biopsies: A multicenter analysis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer mortality in Asia, where histopathological diagnosis of endoscopic bi...

Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) remains a major global health challenge due to its molecular heterogeneity, late diagnosis, and limited therapeutic opt...

Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches.

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dys...

CT-based multiple instance and ensemble learning for lymph node metastasis prediction in esophageal squamous cell carcinoma: a multicentre, retrospective study.

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrat...

Late distant recurrence prediction model in premenopausal women with ER-positive/HER 2-negative breast cancer: A multicenter retrospective study.

BACKGROUND: Late distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (...

Development of a Predictive Calculator for the Need for Abdominoperineal Resection after Chemoradiation Therapy in Anal Squamous Cell Carcinoma.

BACKGROUND: Chemoradiation therapy (CRT) is standard treatment for anal squamous cell carcinoma (ASCC). Abdominoperineal resection (APR) is usually re...

Beyond handcrafted radiomics in oncologic imaging: Innovations in deep, explainable, multi-site and multi-omics radiomics approaches.

Radiomics seeks to convert medical images into quantitative biomarkers capable of capturing tumor phenotype, microenvironment, and underlying biology....

Automating classification of treatment responses to combined targeted therapy and immunotherapy in HCC.

Tyrosine kinase inhibitor (TKI) combined with immunotherapy regimens are now widely used for treating advanced hepatocellular carcinoma (HCC), but the...

Radiomics analysis of MRI improves prediction of lymph node metastasis in laryngeal squamous cell carcinoma.

OBJECTIVE: To explore the role of multi-sequence magnetic resonance imaging (MRI) images in preoperative prediction of lymph node metastasis in laryng...

Potential mechanisms of Si-Wu-Tang against esophageal squamous cell carcinoma: A machine learning pharmacological study.

The purpose of this study is to explore the potential mechanism of Si-Wu-Tang (SWT) against esophageal squamous cell carcinoma (ESCC). Initially, 18 a...

Generative artificial intelligence for patient education material on gastric cancer prevention.

BACKGROUND:  This study assessed the effectiveness of large language models (LLMs) in generating lay summaries for patient education on the management...

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the ...

Interpretable machine learning based on intratumoral and peritumoral ultrasound radiomics for predicting central lymph node metastasis in papillary thyroid carcinoma.

OBJECTIVES: This retrospective and single-center study aimed to develop machine learning (ML) model integrating clinical features, ultrasound (US) fea...

eccDNA2Ca: an ensemble deep learning framework for interpretable prediction of cancer-associated extrachromosomal circular DNA.

BACKGROUND: Extrachromosomal circular DNA (eccDNA) is increasingly recognized as a critical driver of oncogene amplification, therapeutic resistance, ...

AI Co-Scientists Move to the Front Lines of Cancer Research.

Artificial intelligence systems are beginning to function as "co-scientists" in cancer research, generating drug candidates, prioritizing immunotherap...