Oncology/Hematology

Prediction of Neoadjuvant Chemotherapy Efficacy for Locally Advanced Nasopharyngeal Carcinoma Using MRI-Based Deep Learning Features Combined with Vision Transformer.

RATIONALE AND OBJECTIVES: Predicting neoadjuvant chemotherapy (NACT) efficacy is vital for advanced nasopharyngeal carcinoma (LA-NPC) management. Existing models have limited generalizability. The combination of MRI-based deep learning features (DLF) and Vision Transformer (ViT) for this purpose remains unexplored. This study therefore aims to evaluate the value of multi-sequence MRI-based DLF com...

BESTDR Enables Bayesian Quantification of Mechanism-Specific Drug Responses.

UNLABELLED: Understanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. Traditional dose-response models rely on simplified metrics, limiting their ability to quantify parameters like cell division, death, and transition rates between cell states. To address these limitations, we developed Bayesian Estimation of S...

A Deep Learning Framework Integrating Tumor Microenvironmental Features Accurately Predicts Multiple Driver Gene Mutations in Lung Cancer Pathology Images.

UNLABELLED: Deep learning (DL) has the potential to enable the prediction of gene mutations directly from routine histopathology slides in lung cancer...

Integration of Short- and Long-Read RNA Sequencing Enables the Discovery of Circular RNAs.

UNLABELLED: Circular RNAs (circRNA) are associated with crucial hallmarks of tumorigenesis. Select circRNAs contain circular open reading frames (cORF...

Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.

UNLABELLED: Multiplexed imaging of tissues is an approach that holds promise for improving early detection, diagnosis, and treatment of cancer. In thi...

AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.

This article reports the results of the second iteration of the autoPET challenge on automated lesion segmentation in whole-body PET/CT, held in conju...

Deep Learning-Derived Sarcopenia Marker Predicts Benefit from Anti-EGFR Therapy in Patients with RAS Wild-type Metastatic Colorectal Cancer.

PURPOSE: The benefit of treatment intensification in metastatic colorectal cancer (mCRC) may be influenced by host-related factors that are not accoun...

Computational exploration and discovery of dual EGFR-CDK2 kinase inhibitors: AI-ML powered bioisosteric design, 3D QSAR, docking, DFT and ADMET analysis of novel phthalimide derivatives.

AI-ML approaches emerged as transformative technologies in cancer drug discovery by accelerating the target identification and lead optimization. EGFR...

Rapid Diagnosis of Bacteremia in Febrile Pediatric Oncology Patients via Host-Response Infrared Spectroscopy of Blood and Machine Learning.

Bacteremia is a life-threatening complication and a leading cause of sepsis and septic shock in patients. Conventional diagnostic methods, such as blo...

Deep learning-based identification and maturation assessment of the zygomaticomaxillary suture in cone-beam computed tomography images.

OBJECTIVE: This study addresses maxillary deficiency management by developing a deep learning-based system for zygomaticomaxillary suture assessment. ...

PSA-1DCNN: Multimodal Biomarker and Text Integration for Lung Cancer Diagnosis.

Integrating multimodal data, such as unstructured clinical narratives and quantitative blood biomarkers, remains a major challenge in modern healthcar...

A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases.

Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoper...

Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach.

The lower thermal behavior of solar-based thermal systems limits the contribution of solar systems to meet current energy demand of industries. The Fl...

An interpretable breast cancer risk stratification model via multi-omics integration: multi-method development and cross-cohort validation.

BACKGROUND: Breast cancer remains a formidable global health challenge, and the absence of suitable survival guidance models persists, with most exist...

Preoperative prediction of metachronous liver metastasis in colorectal cancer using a deep learning-based radiomics model with automatic segmentation: a multicenter study.

OBJECTIVES: To develop and validate an integrated clinical-radiomics nomogram predicting the risk of metachronous liver metastasis (MLM) in patients w...

Characterization of the Sequence of Cytopenia after Total-body Irradiation using Unsupervised Machine Learning.

Hematopoietic acute radiation syndrome (H-ARS) elicits multidimensional effects, as total-body irradiation (TBI) induced myelosuppression results in d...

Integrated scRNA-seq and bulk transcriptomics identify an amino acid metabolism-associated prognostic signature and highlight FUS as a potential driver in prostate cancer progression.

Prostate cancer (PCa) exhibits marked metabolic heterogeneity, yet the prognostic implications of amino acid metabolism remain insufficiently characte...

Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.

PURPOSE: Early-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting t...

Machine Learning-Driven Risk Stratification and Adjuvant Treatment Guidance in Oral Cavity Cancer.

PURPOSE: To develop and validate machine learning (ML) models for postoperative risk stratification in oral cavity squamous cell carcinoma (OCSCC) and...

From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.

Bone tumors such as osteosarcoma and Ewing sarcoma remain among the most challenging cancers to diagnose and monitor because of their biological heter...