Oncology/Hematology

Can 3D artificial intelligence models outshine 2D ones in the detection of intracranial metastatic tumors on magnetic resonance images?

BACKGROUND: This study aimed to compare the prediction performance of two-dimensional (2D) and three-dimensional (3D) semantic segmentation models for intracranial metastatic tumors with a volume ≥ 0.3 mL.

[Robot-Assisted Partial Nephrectomy for Renal Cell Carcinoma Complicated by Leriche Syndrome : A Case Report].

A 71-year-old man was referred to our hospital for treatment of a 2 cm-sized right renal mass incidentally found by computed tomography (CT) and was diagnosed with right renal cell carcinoma cT1aN0M0. Contrast-enhanced CT revealed that the aorta was completely occluded below the inferior mesenteric artery origin, and Leriche syndrome was diagnosed. CT angiography showed several collateral arteries...

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

PURPOSE: Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomogr...

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the pa...

An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models.

MOTIVATION: Organization of the organoid models, imaged in 3D with a confocal microscope, is an essential morphometric index to assess responses to st...

[Clinical significance of the deep learning algorithm based on contrast-enhanced CT in the differential diagnosis of gastric gastrointestinal stromal tumors with a diameter ≤ 5 cm].

Contrast-enhanced CT is an important method of preoperative diagnosis and evaluation for the malignant potential of gastric submucosal tumor (SMT). I...

A deep autoencoder framework for discovery of metastable ensembles in biomacromolecules.

Biomacromolecules manifest dynamic conformational fluctuation and involve mutual interconversion among metastable states. A robust mapping of their co...

Robotics, imaging, and artificial intelligence in the catheterisation laboratory.

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional too...

Automatic contour segmentation of cervical cancer using artificial intelligence.

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to deline...

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disor...

Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.

Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into differe...

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for t...

DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to...

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studie...

Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions.

Renal-cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ...

Early Experience with Salvage Robotic-Assisted Radical Prostatectomy in Proton Beam Radiotherapy Failures.

AIMS: To assess the perioperative and short-term functional and oncologic outcomes of the salvage robot-assisted radical prostatectomy (sRARP), after ...

Extraperitoneal Single-Port Robot-Assisted Radical Prostatectomy.

Robot-assisted radical prostatectomy (RARP) is currently the standard minimally invasive procedure for the surgical management of localized prostate c...

Applying Machine Learning to Stem Cell Culture and Differentiation.

Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer...

Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology.

PURPOSE: Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general on...

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives.

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define th...