Oncology/Hematology

Interpretable machine learning models for bladder cancer overall survival prediction development and external validation via SEER database and Chinese cohort analysis.

OBJECTIVE: We developed interpretable machine learning(ML) models to predict overall survival in bladder cancer patients. This approach aims to improve the interpretability and transparency of our modeling results. METHODS: We collected clinical and pathological information on bladder cancer patients from the SEER database, allocating it to training and validation sets in a 7:3 ratio. At the same ...

Preoperative prediction of tumor budding grade in rectal cancer by combining APT histogram analysis and ADC MRI.

OBJECTIVE: Tumor budding (TB) is a histopathological marker of aggressive behavior and poor prognosis in rectal cancer (RC), yet not reliably evaluated preoperatively. We assessed whether histogram features from amide proton transfer-weighted (APTw) imaging and apparent diffusion coefficient (ADC) maps could serve as noninvasive biomarkers for preoperative TB grade prediction. MATERIALS AND METHOD...

Targeting tumor-associated G-protein coupled receptors: beyond single-axis inhibition toward multidimensional regulation.

G protein-coupled receptors (GPCRs) serve as central hubs in tumor signal transduction and microenvironment regulation. However, their therapeutic exp...

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-la...

Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics.

OBJECTIVES: Identifying patients at risk of chemoresistant osteosarcoma enables risk-adapted management. This study aimed to predict chemoresistant os...

Comparative evaluation of treatment recommendations generated by generative AI and breast cancer specialists for advanced and recurrent breast cancer: a multidimensional assessment of evidence interpretation and clinical decision support.

BACKGROUND: Large language models (LLMs), a form of generative artificial intelligence (AI), are increasingly explored for clinical applications due t...

Explainable Deep Learning Framework for Multimodal Brain Tumor Classification via Neuroimaging Attribute Extraction.

Accurate grading of brain tumors from multiparametric MRI is a critical step in treatment planning, yet deep learning models trained for this task rem...

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.

BACKGROUND: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and ...

RADIANT: A fully configurable radiotherapy dose prediction framework.

Treatment planning is a multi-disciplinary effort that requires medical decision-making, specialized training, and access to specialized software. Rec...

Deep Learning Algorithm Based on Contrast-Enhanced Ultrasound Potentially Optimizes Treatment Strategies for Solitary Primary Hepatocellular Carcinoma.

OBJECTIVE: We aimed to propose a prognostic framework using a dual-branch Vision Transformer (ViT) deep learning (DL) architecture for stratifying rec...

Interpretable deep learning framework for mapping E3-substrate binding interfaces.

E3 ubiquitin ligases recognize substrates through specific interfaces. Accurate delineation of these interfaces is essential, as mutations disrupting ...

AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.

Breast cancer's genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial in...

Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.

Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present sub...

A neural network embedding the linear-quadratic model for improved prediction of cellular response to ion beam exposure.

Predicting biological responses to ionizing radiation is challenging due to the complex, multi-scale mechanisms involved. Traditional machine learning...

A temporal keyword co-occurrence network mining framework for detecting structural transitions in cancer biomarker research (2006-2023).

Biomarker research for cancer diagnosis and prognosis has rapidly expanded technologically and thematically, along with advancements in molecular diag...

Multicenter machine learning model for assessing the impact of malignancy on in-hospital mortality in heart failure patients: a clinical decision support system with interpretable artificial intelligence.

Heart failure (HF) and malignancy represent two major global health burdens that frequently coexist and lead to poor clinical outcomes. However, the s...

Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.

Prediction of patient-level drug response is critical for precision oncology but remains limited by the scarcity of clinical data. While machine learn...

Phenotypic AI-based design of cell-specific small molecule cytotoxics.

Artificial intelligence is transforming drug discovery by enabling exploration of vast chemical spaces and the design of new molecules with tailored p...

AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases.

OBJECTIVES: This study evaluates the clinical utility of an artificial intelligence (AI)-driven volumetric approach for assessing treatment response i...

Integrating multi-omics data for next-generation cancer research and precision medicine.

Multi-omics is the coordinated acquisition, integration, and interpretation of multiple datasets generated from diverse molecular layers of a biologic...