Other Cancers

KLF7 as a biomarker for the pre-metastatic state promotes colorectal cancer liver metastasis via TGFβ autocrine signaling.

Although metastasis-initiating cells drive metastasis, only a certain subpopulation of these cells can successfully disseminate from the primary tumor to colonize metastatic sites. The identification and characterization of this subpopulation remain poorly studied. We designated this specific subpopulation as pre-metastatic state (PMS) cells. Identifying biomarkers and understanding the mechanisms...

Flavonoid-modulated JAK-STAT signaling mitigates malignant transformation and drug resistance in breast tumors: A clinically relevant 3PM-guided innovation.

BACKGROUND: Breast cancer (BC) treatment efficacy is often compromised by tumor cell plasticity and multidrug resistance of multi-factorial origin. Among emerging therapeutic agents, flavonoids - a structurally diverse group of naturally occurring polyphenols - have demonstrated a significant potential to modulate the Janus kinase/signal transducer and activator of transcription (JAK-STAT) signali...

Renal Cell Carcinoma subtyping: Learning from multi-resolution localization.

BACKGROUND AND OBJECTIVE: Renal Cell Carcinoma (RCC) is often diagnosed at advanced stages, limiting treatment options. Since prognosis depends on tum...

Explainable multimodal fusion for breast carcinoma diagnosis: A systematic review, open problems, and future directions.

Breast carcinoma (BC) remains one of the most common and lethal malignancies in women worldwide, making an early and accurate diagnosis a public healt...

Deep learning model using contrast-enhanced CT for predicting overall survival in oropharyngeal squamous cell carcinoma: a prospective multicenter study.

BACKGROUND: Accurate risk stratification for overall survival (OS) in patients with oropharyngeal squamous cell carcinoma (OPSCC) is critical for guid...

Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies.

INTRODUCTION AND OBJECTIVES: An AI model that performs well during training does not guarantee similar performance in clinical practice and should be ...

Assessment of deep learning reconstruction effects on detection and differentiation of liver metastasis from hepatic hemangioma in diffusion-weighted imaging.

PURPOSE: To evaluate and compare the performance of diffusion-weighted imaging (DWI) using compressed sensing (CS) and DWI using CS with model-based d...

Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does improved image quality translate to better resectability assessment?

PURPOSE: To evaluate whether deep learning-based combined noise reduction and contrast enhancement reconstruction (DLR) improves image quality and res...

Towards Precision Oncology: A New Predictive Machine Learning Model for Early Progression to Castration Resistant Prostate Cancer.

BACKGROUND AND OBJECTIVE: Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive, lethal state of prostate cancer, for which early p...

Multiperspective tumor heterogeneity metrics for preoperative prediction of IASLC grading in clinical stage IA lung adenocarcinomas: A multicenter study.

BACKGROUND AND OBJECTIVE: Accurate preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grades is crucial for...

A High-resolution dataset for AI-driven segmentation and analysis of drug-treated breast tumor spheroids.

BACKGROUND AND OBJECTIVE: Three-dimensional (3D) tumor spheroids are widely adopted in preclinical drug screening for their ability to mimic the compl...

A multi-task deep learning framework for intraoperative diagnosis of thyroid cancer metastasis using whole slide images.

BACKGROUND: Lymph node metastasis (LNM) is a critical prognostic indicator in papillary thyroid carcinoma (PTC), significantly influencing surgical de...

Temporal dynamics and predictive modeling of oral epithelial dysplasia features during carcinogenesis.

OBJECTIVE: To investigate the temporal evolution and predictive value of individual histopathological features of oral epithelial dysplasia (OED) duri...

BreAST-U²Net: A Twin-Stream U2Net with Attention-based Tumor Fusion for 2-D Tumor Segmentation in Automated Breast Ultrasound.

BACKGROUND: Automated breast ultrasound (ABUS) shows potential for breast cancer diagnosis but faces tumor segmentation challenges due to limited anno...

Comparison of glass-based and whole-slide image-based grade assignment for clear-cell and papillary renal cell carcinoma: a multi-cohort, international reproducibility study.

BACKGROUND AND AIMS: Histological grading of renal cell carcinoma (RCC) is an important part of diagnostic evaluation. Reproducibility of RCC grading ...

Malignant pleural mesothelioma classification and survival prediction with CT imaging using ResNet.

OBJECTIVES: This study aims to achieve accurate differentiation of malignant pleural mesothelioma (MPM) from metastatic pleural disease (MPD) and to p...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...

AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

PURPOSE: To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (11C)...

Predictive imaging in abdominal oncology: current trends and future directions.

While the concept of predictive imaging is not entirely new, advanced analytic tools such as radiomics and machine learning have laid the foundation f...

Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.

OBJECTIVE: This study aims to develop and validate an integrated multi-task framework for hepatocellular carcinoma analysis by combining deep learning...