Oncology/Hematology

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer.

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while...

Radiomics based on F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study.

PURPOSE: Our study assessed the ability F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach.

Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR),...

Isolated Hepatic Metastasis of Prostate Cancer with Variable F-fluociclovine Uptake by PET/CT Imaging.

A 74-year-old man presented with rapid rising prostate-specific antigen (PSA) 2 years after treatment of prostate cancer with prostatectomy and salvag...

Predictors, Presentation, and Treatment Outcomes of Recurrent Hepatocellular Carcinoma After Liver Transplantation: A Large Single Center Experience.

BACKGROUND: Liver transplantation (LT) is an accepted therapeutic option for hepatocellular carcinoma (HCC) in patients with cirrhosis. Despite carefu...

Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.

This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical i...

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

BACKGROUND: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microe...

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field.

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous ...

A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer.

Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a S...

Deep segmentation networks predict survival of non-small cell lung cancer.

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide...

Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands.

Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands a...

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.

PURPOSE: To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating t...

Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transc...

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can aut...

A simple knowledge-based tool for stereotactic radiosurgery pre-planning.

We studied the dosimetry of single-isocenter treatment plans generated to treat a solitary intracranial lesion using linac-based stereotactic radiosur...

Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.

Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of arti...

The impact of patient clinical information on automated skin cancer detection.

Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automat...

Physical and Chemical Stability of Dexamethasone Sodium Phosphate in Intravenous Admixtures Used to Prevent Chemotherapy-Induced Nausea and Vomiting.

Dilute intravenous (IV) admixtures of dexamethasone sodium phosphate (DSP) are becoming increasingly used in antiemetic regimens to prevent chemother...

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

BACKGROUND: Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfort...

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.

With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in parti...