Oncology/Hematology

Intelligent solution predictive networks for non-linear tumor-immune delayed model.

In this article, we analyze the dynamics of the non-linear tumor-immune delayed (TID) model illustrating the interaction among tumor cells and the immune system (cytotoxic T lymphocytes, T helper cells), where the delays portray the times required for molecule formation, cell growth, segregation, and transportation, among other factors by exploiting the knacks of soft computing paradigm utilizing ...

Using a deep learning neural network for the identification of malignant cells in effusion cytology material.

AIM: To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples.

Drug synergy model for malignant diseases using deep learning.

Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug...

Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development.

Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have deve...

Mastectomy with Reconstruction Including Robotic Endoscopic Surgery (MARRES): a prospective cohort study of the Korea Robot-Endoscopy Minimal Access Breast Surgery Study Group (KoREa-BSG) and Korean Breast Cancer Study Group (KBCSG).

BACKGROUND: Robotic nipple-sparing mastectomy (RNSM) has emerged as a new treatment option for breast cancer and risk-reducing mastectomy (RRM) for wo...

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The pu...

Deep learning-based body weight from scout images can be an alternative to actual body weight in CT radiation dose management.

PURPOSE: Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be me...

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.

Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of ...

Efficient evaluation of photodynamic therapy on tumor based on deep learning.

Photodynamic therapy (PDT) is a non-invasive treatment method for treating tumors. Under laser irradiation, photosensitizers in tumor tissues generate...

Predicting Neoadjuvant Chemotherapy Response and High-Grade Serous Ovarian Cancer From CT Images in Ovarian Cancer with Multitask Deep Learning: A Multicenter Study.

RATIONALE AND OBJECTIVES: Accurate prediction neoadjuvant chemotherapy (NACT) response in ovarian cancer (OC) is essential for personalized medicine. ...

Cross-dimensional transfer learning in medical image segmentation with deep learning.

Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision appli...

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography rei...

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images.

Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy...

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach.

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment sele...

TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging.

Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep le...

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overri...

Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis.

Multi-parametric MR image synthesis is an effective approach for several clinical applications where specific modalities may be unavailable to reach a...

Diagnostic ability of deep learning in detection of pancreatic tumour.

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effect...

PTPRC promoted CD8+ T cell mediated tumor immunity and drug sensitivity in breast cancer: based on pan-cancer analysis and artificial intelligence modeling of immunogenic cell death-based drug sensitivity stratification.

BACKGROUND: Immunogenic cell death (ICD) is a result of immune cell infiltration (ICI)-mediated cell death, which is also a novel acknowledgment to re...

Phytochemical Screening, HPTLC Fingerprinting and Evaluation of In Vitro Cytotoxic Activity of Stem Bark Extracts of O (L.) Vent. Against Human Cervical Cancer Cells.

a well-known traditional medicinal plant which is used to alleviate various kinds of diseases in Asia. The study aimed to identify bioactive compound...