Oncology/Hematology

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate dehydrogenase 1 (IDH1) mutations aids the diagnostic classification of these tumors and the prediction of their clinical outcomes. While histology continues to play a key role in frozen section diagnosis, as a diagnostic reference and as a method for monitoring disease progression, recent research has ...

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add value as a diagnostic aid in the evaluation of tissue samples involved by lymphoma. A common diagnostic question is the determination of chronic lymphocytic leukemia (CLL) progression to accelerated CLL (aCLL) or transformation to diffuse large B-cell ...

Complete Molar Pregnancies with a Coexisting Fetus: Pregnancy Outcomes and Review of Literature.

 The objective of the study was to review the obstetric outcomes of complete hydatidiform molar pregnancies with a coexisting fetus (CHMCF), a rare c...

BH-index: A predictive system based on serum biomarkers and ensemble learning for early colorectal cancer diagnosis in mass screening.

BACKGROUND AND OBJECTIVE: Colorectal cancer is one of the most common malignancies among the general population. Artificial Intelligence methodologies...

Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm.

Ex vivo confocal microscopy (EVCM) generates digitally colored purple-pink images similar to H&E without time-consuming tissue processing. It can be u...

CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation.

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately pre...

Development of S4A-BSA-Au NPs for enhanced anti-tumor therapy of canine breast cancer.

S4A ((1,2,3)-1,2-propanediol acetal-zeylenone) is one of the derivatives of zeylenone and exhibits superior cytotoxicity against the canine breast can...

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the manage...

Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm.

Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult t...

Deep learning-based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CE...

Environmentally Adaptive Shape-Morphing Microrobots for Localized Cancer Cell Treatment.

Microrobots have attracted considerable attention due to their extensive applications in microobject manipulation and targeted drug delivery. To reali...

Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address...

A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma.

Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO method...

Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.

PURPOSE: Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learni...

Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.

OBJECTIVES: Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatmen...

Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network.

Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 ...

The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.

Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patie...

Self-supervised driven consistency training for annotation efficient histopathology image analysis.

Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustiv...

Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models.

Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may in...

Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison.

Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize...