Oncology/Hematology

Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization.

The objective is to assess the performance of seven semiautomatic and two fully automatic segmentati...

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis.

OBJECTIVE: Ovarian cancer is a significant health issue with lasting impacts on the community. Despi...

A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.

Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malign...

Automatic sleep staging for the young and the old - Evaluating age bias in deep learning.

BACKGROUND: Various deep-learning systems have been proposed for automated sleep staging. Still, the...

Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods.

OBJECTIVE: To determine whether spinal metastatic lesions originated from lung cancer or from other ...

Early screening of cervical cancer based on tissue Raman spectroscopy combined with deep learning algorithms.

Cervical cancer is the most common reproductive malignancy in the female reproductive system. The in...

Development and validation of a deep learning survival model for cervical adenocarcinoma patients.

BACKGROUND: The aim was to develop a personalized survival prediction deep learning model for cervic...

Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging.

Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel siz...

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry.

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of ...

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations.

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies ...

Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.

This study aimed to determine the optimal radiographic conditions for detecting lesions on digital c...

A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma.

BACKGROUND: Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficu...

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers.

Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely dif...

Whole slide image-based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence.

OBJECTIVES: Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for de...

An artificial intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model.

The present work develops ANAKIN: an. ANAKIN is trained and tested over 513 cell survival experiment...

ChatGPT: Can a Natural Language Processing Tool Be Trusted for Radiation Oncology Use?

Recognition of types and frequency of incidental findings in cancer staging CT should be essential for future artificial intelligence development.

Artificial intelligence-powered software detected more than half of the liver metastases overlooked by radiologists on contrast-enhanced CT.

PURPOSE: To evaluate the sensitivity of artificial intelligence (AI)-powered software in detecting l...

Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning.

Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emergin...

A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.

Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the ...

Deep learning prediction of post-SBRT liver function changes and NTCP modeling in hepatocellular carcinoma based on DGAE-MRI.

BACKGROUND: Stereotactic body radiation therapy (SBRT) produces excellent local control for patients...