Oncology/Hematology

Multiclass convolutional neural network vs. 96 dermatologists in skin lesion diagnosis, an international study.

BACKGROUND AND OBJECTIVES: Artificial intelligence was shown to improve diagnostic accuracy for skin cancer detection. While most clinically approved models provide binary "benign/malignant" classifications, multiclass predictions may offer greater clinical utility. Yet, comparisons between multiclass convolutional neural networks (CNNs) and dermatologists are scarce. METHODS: In an international ...

Integrating machine learning and pharmacogenomics for biomarker discovery, identification and prioritization of potential drug candidates in ovarian cancer.

Ovarian cancer remains a major global health concern and leading cause of mortality among women due to late diagnosis, therapeutic resistance, and limited predictive biomarkers for treatment response. There is an urgent need for integrative approaches to improve early detection and treatment outcomes. In this study, we integrated machine learning and pharmacogenomics to identify drug-sensitive bio...

Integrating Molecular Pathology, Tumor Microenvironment, and Novel Therapies to Overcome Resistance in Glioblastoma.

Glioblastoma multiforme (GBM) represents the most aggressive primary brain tumor in adults, characterized by significant heterogeneity, rapid progress...

An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.

BACKGROUND: Oxaliplatin (OXA) is widely used in the treatment of gastrointestinal malignancies such as colorectal cancer. However, oxaliplatin-induced...

Preoperative hypoalbuminemia predicts early complications, mortality, and discharge disposition following surgical resection of spinal metastases: a nationwide study.

PURPOSE: Patients undergoing surgery for spinal metastases often have limited physiologic reserve. Although hypoalbuminemia is a recognized risk marke...

Machine learning-based prediction of immune infiltration patterns in extrahepatic cholangiocarcinoma.

BACKGROUND: Immune cell infiltration patterns play important roles in shaping the tumor microenvironment of cholangiocarcinoma. However, efficient app...

Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

OBJECTIVE: To critically evaluate machine learning (ML) models developed for predicting radiation-induced oral mucositis (OM) in head and neck cancer ...

Deep Learning Predicts Mutations and Outcomes in Gastrointestinal Stromal Tumors from Whole-Slide Images.

Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase (KIT) and platelet-deri...

Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic pr...

Review Topic: Don't Cancer Doctors Offer Good-Quality Care?

We offer the perspectives of two veterans on the last quarter century of quality improvement efforts in oncology care. We believe that our colleagues ...

The atlas of abdominal organ remodeling in hepatocellular carcinoma patients: An artificial intelligence-based multicenter imaging study.

BACKGROUND: Morphological changes of abdominal organs in hepatocellular carcinoma (HCC) remain uncharacterized. This study aimed to automatically quan...

Radiological Assessment of Liver and Biliary Neoplasms.

Primary liver cancer, encompassing hepatocellular carcinoma and cholangiocarcinoma, represents a growing global health burden with significant morbidi...

Integrative machine learning and multi-omics analysis reveals ATIC as a promoter of hepatocellular carcinoma progression.

Autophagy plays a non-negligible role in the progression and immune regulation of hepatocellular carcinoma (HCC). An integrated analysis of the autoph...

Deep learning-based spatiotemporal estimation of lesion changes for patient-level assessment of breast cancer lung metastases on longitudinal CT.

Clinical management of breast cancer lung metastasis is challenging because of the complexity of dynamic lesion assessment. Traditional methods based ...

Integrated transcriptomics, network pharmacology and clinical expression validation reveal the prognostic significance of PANoptosis-related genes in cordycepin-treated lung adenocarcinoma.

BACKGROUND: Studies have shown that PANoptosis is increasingly involved in cancer and cancer treatment. The Cordycepin has also been found to be invol...

Physical activity level and its determinants in patients with cancer: a secondary analysis using a decision-tree approach.

PURPOSE: Low physical activity is associated with poorer outcomes in patients with cancer. Understanding the determinants of physical activity is esse...

Deep learning layer-specific collagen quantification correlates with activity and is associated with outcomes in Crohn's disease.

BACKGROUND & AIMS: Intestinal fibrosis remains a major unmet need in inflammatory bowel disease (IBD). Although collagen deposition is the defining fe...

Predicting time to local failure after gamma knife radiosurgery for melanoma brain metastases using survival machine learning.

BACKGROUND: Melanoma brain metastases treated with Gamma Knife radiosurgery show heterogeneous local control trajectories. Accurate prediction of time...

Integrative single-cell and spatial transcriptomics analysis reveals a baicalein-responsive 10-gene signature for non-small cell lung cancer.

BACKGROUND: Non‑small cell lung cancer (NSCLC) remains a leading cause of cancer‑related mortality worldwide. Baicalein, a natural flavonoid, has show...

Machine learning models predict the immunotherapy response in tumors on the basis of DNA methylation.

BACKGROUND: The epigenetic control of immune responses plays a crucial role in the development and progression of cancer. The need to identify biomark...