Other Cancers

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This review summarizes key developments in imaging modalities, computational tools, and clinical applications, highlighting major breakthroughs in thoracic diseases-lung cancer, pulmonary nodules, interstitial lung disease (ILD), chronic obstructive pulmonary...

Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers.

PURPOSE: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic solid cancers. MATERIALS AND METHODS: A total of 231 patients with mediastinal lymphadenopathy were selected from the Mediastinal-Lymph-Node-SEG collection in The Cancer Imaging Archive, including 145 patients with hematologic malignancies (74 with ch...

Adverse reactions of PD-1/PD-L1 inhibitors in cancer: FAERS database analysis and protocols to mitigate immune-related events in elderly patients and when using pembrolizumab and atezolizumab.

OBJECTIVE: This study aimed to characterize adverse drug reactions (ADRs) associated with programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) in...

Virtual Multi-Phase Contrast Enhanced Liver MRI Using Deep Learning for Evaluating Hepatocellular Carcinoma.

A deep learning (DL) model was developed to generate contrast-enhanced MRI (CE-MRI) at multiple enhancement phases (arterial, portal venous, transitio...

Cutting-Edge Technologies to Decode Tumor Microenvironment in Multiple Myeloma.

Multiple myeloma (MM) is recognized as a malignancy shaped by its complex tumor microenvironment (TME), which fuels disease progression and therapeuti...

Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.

Purpose To develop a deep learning-based, computer-aided diagnosis (CADx) model for preoperative classification of ovarian tumors (OTs) on CT scans an...

Region-Based Segmentation of Lymph Node Metastases in Whole-Slide Images of Colorectal Cancer: A Pilot Clinical Study.

BACKGROUND: Digital technologies and artificial intelligence (AI) are transforming medical diagnostics, particularly in pathology. This study presente...

A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.

Purpose To develop a self-supervised chest CT foundation model and evaluate its performance in lung cancer clinical tasks. Materials and Methods In th...

Impact of Annotation Level on Multisequence MRI Models for Preoperative Microvascular Invasion Prediction in Hepatocellular Carcinoma.

Purpose To evaluate the performance of deep learning models integrating multimodal data for predicting microvascular invasion (MVI) in hepatocellular ...

Machine Learning-Driven Prognostic Model Integrating Lymphocyte-to-C-Reactive Protein Ratio and TNM Staging in Gallbladder Cancer.

BACKGROUND: A comprehensive preoperative assessment of the patient's physical condition is crucial for predicting the prognosis of patients undergoing...

Identification and Validation of cGAS-STING Pathway-Associated Predictive and Therapeutic Models for Esophageal Squamous Cell Cancer Patients via Artificial Intelligence and Multi-Omics.

BACKGROUND: Esophageal squamous cell cancer (ESCC) is a malignancy derived from the Esophagus, and dysregulation of the cGAS-STING pathway contributes...

Simulation-free spine palliative radiotherapy enabled by AI-adapted diagnostic CT.

BACKGROUND: Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is ...

Generalizability of deep learning-based dose conversion model in proton beam therapy.

BACKGROUND: In proton beam therapy (PBT), the analytical pencil beam (PB) algorithm involves dose uncertainties in inhomogeneous regions, making accur...

A voxel-wise uncertainty-guided framework for glioma segmentation using spherical projection-based U-Net and localized refinement.

BACKGROUND: Accurate segmentation of glioma subregions from multi-parametric MRI (MP-MRI) is critical for clinical management but remains challenging ...

ChatGPT, Gemini, and Claude in clinical and dermoscopic image analysis of basal cell carcinoma and its common mimickers: A comparative performance analysis.

Basal cell carcinoma (BCC) is the most common skin cancer. Off-the-shelf multimodal large language models are widely accessible, yet their performance...

Integrated Single-Cell and Bulk Transcriptome Analysis Reveals the Prognostic Significance and Immune Regulatory Mechanisms of Sialylation in Hepatocellular Carcinoma.

Liver Hepatocellular Carcinoma (LIHC) is a high-mortality primary liver cancer. Its treatment and prognosis are highly dependent on disease stage and ...

Single-nucleus multi-omics dissection of dysregulated trophoblast development and disrupted immune microenvironment in complete hydatidiform moles.

Hydatidiform mole (HM) is driven by aberrant trophoblast proliferation, disrupting embryonic development and leading to pregnancy loss with increased ...

Predicting non-alcoholic fatty liver disease (NAFLD) using machine learning algorithms: Evidence from a large-scale community cohort in Taiwan.

Closely associated with metabolic disorders, non-alcoholic fatty liver disease (NAFLD) substantially increases the risk of hepatocellular carcinoma. T...

[Albumin-myoestatosis gauge assisted by an artificial intelligence tool as a prognostic factor in patients with metastatic colorectal-cancer].

Objectives: to evaluate the prognostic role of the marker albumin-myosteatosis (MAM) in Caucasian patients with metastatic colorectal cancer. Material...

When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.

OBJECTIVES: Multidisciplinary tumor boards (MDTs) are critical for the personalized management of soft tissue sarcomas (STS), but they are limited by ...