Oncology/Hematology

Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data.

IMPORTANCE: Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of eligible Americans are screened. CXR-LC, an open-source deep learning tool that estimates lung cancer risk from existing chest radiograph images and commonly available electronic medical record (EMR) data, may enable automated identification of high-risk patients as a step to...

Epidemiological Features of Acute Myeloid Leukemia in Five Regions of the Republic of Kazakhstan: Population Study.

OBJECTIVE: The aim of the study was to assess the main epidemiological characteristics of AML (morbidity, survival, distribution by AML variants and age groups) in 5 regions participating in the study.

Efficient framework for brain tumor detection using different deep learning techniques.

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after t...

Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.

INTRODUCTION: Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are s...

A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is c...

Retzius-sparing vs. standard robot-assisted radical prostatectomy for clinically localised prostate cancer: a comparative study.

BACKGROUND: Retzius-sparing robot-assisted radical prostatectomy (RARP) is not yet universally accepted due to still limited functional data and some ...

Robot-assisted versus thoracolaparoscopic oesophagectomy for locally advanced oesophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy.

INTRODUCTION: Robot-assisted oesophagectomy (RAE) and thoracolaparoscopic oesophagectomy (TLE) are surgical techniques for the treatment of oesophagea...

Percutaneous Ablation Robot-Assisted Partial Nephrectomy for Completely Endophytic Renal Masses: A Multicenter Trifecta Analysis with a Minimum 3-Year Follow-Up.

To compare outcomes of robot-assisted partial nephrectomy (RAPN) and percutaneous tumor ablation (PTA) for completely endophytic renal masses. Data ...

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.

BACKGROUND: Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized d...

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning.

BACKGROUND: Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationsh...

Medial versus lateral camera port placement for robotic nephron-sparing surgery: evaluating surgical complications and outcomes.

As the growing popularity of robotic-assisted laparoscopic procedures for the treatment of renal cancer increases, there exists a variation in surgica...

X-ray dose profiles using artificial neural networks.

This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15...

Deep learning and multi-omics approach to predict drug responses in cancer.

BACKGROUND: Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing geneti...

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.

The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around th...

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic...

Prognostic features of upstaged pT3a renal tumors with fat invasion after robot-assisted partial nephrectomy: is it time for a new subclassification?

INTRODUCTION: The clinical management of pT3a pathologic-upstaged renal cell carcinoma (RCC) patients is actually controversial. Aim of this study was...

Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning.

PURPOSE: Develop a prediction model to determine the probability of no lymph node metastasis (pN0) in patients with colorectal cancer.

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders.

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorig...

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

BACKGROUND: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, ...

A Bibliometric Review: Brain Tumor Magnetic Resonance Imagings Using Different Convolutional Neural Network Architectures.

BACKGROUND: Numerous scientists and researchers have been developing advanced procedures and methods for diagnosing the kind and phase of a human tumo...