Oncology/Hematology

Other Cancers

Latest AI and machine learning research in other cancers for healthcare professionals.

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A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies.

PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to tre...

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a no...

Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in...

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.

BACKGROUND: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurre...

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters.

Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity...

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally a...

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

IMPORTANCE: Biological therapies have revolutionized inflammatory bowel disease management, but many...

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.

BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many ...

Effectiveness of ozonated saline in the treatment of VX2 tumors in rabbits.

To investigate the efficacy, safety, and associated mechanisms of injected ozonated saline in the t...

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation.

Automatic and precise segmentation and classification of tumor area in medical images is still a cha...

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.

The strategy of using existing drugs originally developed for one disease to treat other indications...

An ontology for representing hematologic malignancies: the cancer cell ontology.

BACKGROUND: Within the cancer domain, ontologies play an important role in the integration and annot...

Multimodal brain tumor image segmentation using WRN-PPNet.

Tumor segmentation is of great importance for diagnosis and prognosis of brain cancer in medical fie...

Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. Accordin...

Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy.

Numerous models have investigated cancer behavior by considering different factors in chemotherapy. ...

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosar...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical elem...

Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.

Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important fo...

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