Oncology/Hematology

A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer.

BACKGROUND: Preoperative diagnoses of metastatic lymph nodes (LNs) by the most advanced deep learning technology of Faster Region-based Convolutional Neural Network (Faster R-CNN) have not yet been reported.

Robust edge-based biomarker discovery improves prediction of breast cancer metastasis.

BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metas...

Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines...

The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective.

As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern ana...

Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN.

In breast cancer, undetected lymph node metastases can spread to distal parts of the body for which the 5-year survival rate is only 27%, making accur...

Integration of AI and Machine Learning in Radiotherapy QA.

The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disc...

Characterizing CDK12-Mutated Prostate Cancers.

PURPOSE: Cyclin-dependent kinase 12 (CDK12) aberrations have been reported as a biomarker of response to immunotherapy for metastatic castration-resis...

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer v...

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lun...

Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic chang...

Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma.

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This s...

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection...

Operative and long-term oncological outcomes in patients undergoing robotic versus laparoscopic surgery for rectal cancer.

BACKGROUND: This study aimed to compare short- and long-term outcomes after robotic versus laparoscopic approach in patients undergoing curative surge...

Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.

BACKGROUND: Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic r...

2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma.

For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy...

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an importan...

Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics.

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatm...

Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients.

Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI...

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and ...

Identification of Latent Oncogenes with a Network Embedding Method and Random Forest.

Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers...