Oncology/Hematology

Foundation Model and Radiomics-Based Quantitative Characterization of Perirenal Fat in Renal Cell Carcinoma Surgery.

RATIONALE AND OBJECTIVES: To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma.

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma characterization and treatment planning, particularly in the context of radiation therapy (RT). Magnetic resonance imaging (MRI) provides superior soft tissue ...

Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.

BACKGROUND: To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MR...

Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The growing adoption of Large Language Models (LLMs) in medicine has raised important questions about their potential utility for clinical...

Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.

Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on ma...

A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progress...

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic me...

Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis.

The task of predicting liver tumors is critical as part of medical image analysis and genomics area since diagnosis and prognosis are important in mak...

Artificial intelligence-assisted magnetic resonance lymphography for evaluation of micro- and macro-sentinel lymph node metastasis in breast cancer.

Contrast-enhanced magnetic resonance lymphography (CE-MRL) plays a crucial role in preoperative diagnostic for evaluating tumor metastatic sentinel ly...

Evaluating the prognostic significance of artificial intelligence-delineated gross tumor volume and prostate volume measurements for prostate radiotherapy.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI...

Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches.

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to ...

Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma.

BACKGROUND: We aim to predict outcomes of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC), a subtype of head and n...

Deep learning-based analysis of gross features for ovarian epithelial tumors classification: A tool to assist pathologists for frozen section sampling.

Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge thi...

Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation.

Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, whic...

Machine Learning and Mendelian Randomization Reveal a Tumor Immune Cell Profile for Predicting Bladder Cancer Risk and Immunotherapy Outcomes.

This study's objective was to develop predictive models for bladder cancer (BLCA) using tumor infiltrated immune cell (TIIC)-related genes. Multiple R...

Brain tumor segmentation with deep learning: Current approaches and future perspectives.

BACKGROUND: Accurate brain tumor segmentation from MRI images is critical in the medical industry, directly impacts the efficacy of diagnostic and tre...

HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images.

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning mod...

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and qual...

Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.

New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is s...

Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool.

Colorectal cancer (CRC) ranks as the third most common and second deadliest cancer worldwide. The immune system, particularly tertiary lymphoid struct...