Oncology/Hematology

Younger tamoxifen-treated breast cancer patients also had higher risk of endometrial cancer and the risk could be reduced by sequenced aromatase inhibitor use: A population-based study in Taiwan.

OBJECTIVE: Previous Western studies reported that older (≥50 years) breast cancer survivors with tamoxifen treatment had higher risk of endometrial cancer. This study aims to disclose whether younger (<50 years) tamoxifen-treated breast cancer patients also had higher risk of endometrial cancer and to examine whether sequenced aromatase inhibitor (AI) use could reduce the risk.

Artificial Intelligence Systems Assisting Oncologists? Resist and Desist or Enlist and Coexist.

The use of artificial intelligence (AI) has become an integral part of patient care, but there are concerns about the impact of non‐human decision assistance on patient outcomes. This commentary focuses on how AI can assist oncologists and benefit patients.

Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.

BACKGROUND: We attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-...

Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.

PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed ...

Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks.

PURPOSE: To effectively grade hepatocellular carcinoma (HCC) based on deep features derived from diffusion weighted images (DWI) with multiple b-value...

Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach.

Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using ...

Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning.

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrat...

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer.

OBJECTIVE: Artificial neural network (ANN) technology has been developed for clinical use to analyze bone scintigraphy with metastatic bone tumors. It...

Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier.

Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not p...

MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy...

A preliminary study of using a deep convolution neural network to generate synthesized CT images based on CBCT for adaptive radiotherapy of nasopharyngeal carcinoma.

This study aims to utilize a deep convolutional neural network (DCNN) for synthesized CT image generation based on cone-beam CT (CBCT) and to apply th...

Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach.

Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative therapy for high-risk acute leukemia (AL), some patients still r...

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually ...

Improving palliative and end-of-life care with machine learning and routine data: a rapid review.

Improving end-of-life (EOL) care is a priority worldwide as this population experiences poor outcomes and accounts disproportionately for costs. In c...

The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.

According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree mal...

Image-Guided Robotic Radiosurgery for Treatment of Recurrent Grade II and III Meningiomas. A Single-Center Study.

OBJECTIVE: Stereotactic radiosurgery (SRS) has been increasingly applied for malignant meningiomas as an alternative to conventionally fractioned radi...

Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video).

BACKGROUND AND AIMS: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous...

Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT).

BACKGROUND AND PURPOSE: To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine ...

Characterization and potential antitumor effect of a heteropolysaccharide produced by the red alga .

Taking into account the rising trend of the incidence of cancers of various organs, effective therapies are urgently needed to control human malignanc...

Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.

Classification of benign-malignant lung nodules on chest CT is the most critical step in the early detection of lung cancer and prolongation of patien...