Oncology/Hematology

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

MOTIVATION: Brain or central nervous system cancer is the tenth leading cause of death in men and women. Even though brain tumour is not considered as the primary cause of mortality worldwide, 40% of other types of cancer (such as lung or breast cancers) are transformed into brain tumours due to metastasis. Although the biopsy is considered as the gold standard for cancer diagnosis, it poses sever...

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally diffi...

Lomboaortic Lymphadenectomy in Gynecological Oncology: Laparotomy, Laparoscopy or Robot-Assisted Laparoscopy?

BACKGROUND: The outcomes of paraaortic lymphadenectomy were compared for the treatment of gynecological malignancies to identify the most appropriate ...

Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images.

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accurac...

Improvement of oral cancer screening quality and reach: The promise of artificial intelligence.

Oral cancer is easily detectable by physical (self) examination. However, many cases of oral cancer are detected late, which causes unnecessary morbid...

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular target...

Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of c...

A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

BACKGROUND: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be...

Anatomical pathology (human structural biopathology) in the era of "Big Data", digitalization, 5G and artificial intelligence: Evolution or Revolution?

A proposal of an updated system of the Organization of Scientific Biomedical Kowledge is presented, integrating the historical achievements in patholo...

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determ...

Developing knowledge-based planning for gynaecological and rectal cancers: a clinical validation of RapidPlan.

INTRODUCTION: To create and clinically validate knowledge-based planning (KBP) models for gynaecologic (GYN) and rectal cancer patients. Assessment of...

Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

Accurate estimation of reference evapotranspiration (ET) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation...

Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework.

PURPOSE: To accurately distinguish benign from malignant pulmonary nodules with CT based on partial structures of 3D U-Net integrated with Capsule Net...

Development of a machine learning-based multimode diagnosis system for lung cancer.

As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis...

Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model.

PURPOSE: To determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we develo...

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful eval...

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.

Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disru...

Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.

PURPOSE: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-ass...

A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.

PURPOSE: To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade...