Oncology/Hematology

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 health priority. Recent artificial intelligence (AI) research has increasingly explored multimodal fusion, with imaging, clinical records, histopathology, and genomic data to produce richer, more reliable predictions. In parallel, explainable AI (XAI) tec...

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 guiding personalized treatment and surveillance. A deep learning (DL) model was developed and validated to estimate OS in OPSCC patients based on contrast-enhanced computed tomography (CECT). MATERIALS AND METHODS: A total of 269 patients from three cent...

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...

Drug repurposing identifies novel Wee1 kinase inhibitors for triple negative breast cancer therapeutics.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options. Wee1 kinase, a critical regulator of th...

Accelerated Chemical Exchange Saturation Transfer Imaging With Deep Unrolling Networks and Synthetic Brain Tumor Datasets.

PURPOSE: To develop a model-based deep unrolling network for high-quality image reconstruction of accelerated multi-channel chemical exchange saturati...

Performance Assessment of a Deep Learning-based Algorithm for Ovarian Cancer Histotyping in an Independent Data Set.

Artificial intelligence diagnostic tools show promise for improving histotype classification in epithelial ovarian cancer but face challenges due to s...

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...

Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

OBJECTIVES: To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes...

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...

Advanced artificial intelligence combined with SERS platforms for diagnosis and therapeutic effects of cancer in clinical applications.

Surface-Enhanced Raman Spectroscopy (SERS) has become a valuable way to detect small amounts of molecules due to its high sensitivity. Nonetheless, ap...

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 ...

An interactive and explainable AI approach to improve human-machine teaming in cancer subtyping from digital cytopathology.

Algorithmic decision support is rapidly becoming a staple of personalized medicine, particularly for high-stakes recommendations such as cancer subtyp...

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...