Oncology/Hematology

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary cla...

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

PURPOSE: Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patient...

Machine learning and new insights for breast cancer diagnosis.

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammograph...

Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

PURPOSE: Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hosp...

[Identification of Protein-Coding Gene Markers in Breast Invasive Carcinoma Based on Machine Learning].

Objective To screen out the biomarkers linked to prognosis of breast invasive carcinoma based on the analysis of transcriptome data by random forest (...

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological...

Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review.

Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker...

Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.

In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensiv...

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at dif...

A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.

Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The...

HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approach...

Improving drug response prediction via integrating gene relationships with deep learning.

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterog...

Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to ...

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarker...

Contrastive learning for enhancing feature extraction in anticancer peptides.

Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts ha...

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.

MOTIVATION: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scient...

Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT.

Purpose To compare the image quality and diagnostic capability in detecting malignant liver tumors of low-dose CT (LDCT, 33% dose) with deep learning-...

[Current status and prospects for the application of robot-assisted spine surgery].

Traditional spine surgery frequently encounters difficulties with inadequate surgical visualization and high risk.Robot-assisted spine surgery is quic...

Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rec...

A systematic review on deep learning-based automated cancer diagnosis models.

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated ...