Oncology/Hematology

Deep Collocative Learning for Immunofixation Electrophoresis Image Analysis.

Immunofixation Electrophoresis (IFE) analysis is of great importance to the diagnosis of Multiple Myeloma, which is among the top-9 cancer killers in the United States, but has rarely been studied in the context of deep learning. Two possible reasons are: 1) the recognition of IFE patterns is dependent on the co-location of bands that forms a binary relation, different from the unary relation (vis...

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.

BACKGROUND: Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters.

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved.

OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology.

Long-term cancer survival prediction using multimodal deep learning.

The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal de...

Unsupervised water scene dehazing network using multiple scattering model.

In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are no...

The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape o...

Artificial Intelligence in Cancer Care: Legal and Regulatory Dimensions.

Considering that artificial intelligence (AI) technologies have the potential to change cancer care, this article discusses the AI features of which o...

Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer.

OBJECTIVE: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only s...

Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers.

Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate o...

Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical i...

Artificial intelligence in medical imaging: implications for patient radiation safety.

Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost...

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to ope...

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and ...

Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning.

BACKGROUND AND AIMS: The relationship between quantitative magnetic resonance imaging (MRI) imaging features and gene-expression signatures associated...

Robot assisted laser-interstitial thermal therapy with iSYS1 and Visualase: how I do it.

BACKGROUND: Laser-interstitial thermal therapy (LITT) is an ablative treatment based on a surgically implanted laser-emitting catheter to induce a foc...

Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis.

The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly ...

Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.

OBJECTIVE: This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis...

A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI.

PURPOSE: To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRC...

Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.

Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influ...

The predictive power of artificial intelligence on mediastinal lymphnode metastasis.

OBJECTIVE: The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence...