Oncology/Hematology

Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data.

Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: candidate segmentation, candidate classification, and semi-automatic correction of the classification result. T...

Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs.

Background A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT. Purpose To evaluate whether a deep learning (DL)-based CAD system can improve diagnostic yield for newly visible lung metastasis on chest radiographs in patients with cancer. Materials and Methods A regulatory-approved CAD system ...

Contemporary Pure Laparoscopic Robot-Assisted Laparoscopic Radical Nephrectomy: Is the Transition Worth It?

The proportion of robotic procedures continues to rise. The literature reinforces that robotic procedures take longer and are often more costly. We c...

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhi...

Graphene oxide and its nanocomposites with EDTA or chitosan induce apoptosis in MCF-7 human breast cancer.

To achieve the advanced anticancer activity of nanocomposites fabricated with graphene oxide (GO), a novel procedure was used during the fabrication o...

Chemotherapeutic potential of Cayratia trifolia L nhexane extract on A2780 cells.

It is of interest to report the chemotherapeutic (drug target based) potential of n-hexane Cayratia trifolia L. (C.trifolia) extract on A2780 cell lin...

Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists.

BACKGROUND: Differentiating benign from malignant renal tumors is important for selection of the most effective treatment.

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

BACKGROUND: Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a si...

Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

OBJECTIVES: To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sar...

Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

For medicines, the apparent membrane permeability coefficients (P) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient...

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative...

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The curren...

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecti...

Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate...

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based o...

A deep learning-based dual-omics prediction model for radiation pneumonitis.

PURPOSE: Radiation pneumonitis (RP) is the main source of toxicity in thoracic radiotherapy. This study proposed a deep learning-based dual-omics mode...

Artificial intelligence: The opinions of radiographers and radiation therapists in Ireland.

INTRODUCTION: Implementation of Artificial Intelligence (AI) into medical imaging is much debated. Diagnostic Radiographers (DRs) and Radiation Therap...

Towards personalized nutritional treatment for malnutrition using machine learning-based screening tools.

Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional therap...

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data.

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associat...

3D-PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN.

Pulmonary cancer is one of the most common malignancies worldwide. Accurate classification of its subtypes is required in differential diagnosis. Howe...