Oncology/Hematology

Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti-CTLA4 and anti-PD-L1 immune checkpoint inhib...

A new prognostic score for predicting survival in patients treated with robotic stereotactic radiotherapy for brain metastases.

The study aimed to analyze potential prognostic factors in patients treated with robotic radiosurgery for brain metastases irrespective of primary tumor location and create a simple prognostic score that can be used without a full diagnostic workup. A retrospective analysis of 142 patients with 1-9 brain metastases treated with stereotactic radiosurgery (1-4 fractions) was performed. Volumes of al...

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator.

Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technolog...

Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks.

The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-canc...

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network.

BACKGROUND: At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), m...

Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.

Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery...

BCHisto-Net: Breast histopathological image classification by global and local feature aggregation.

Breast cancer among women is the second most common cancer worldwide. Non-invasive techniques such as mammograms and ultrasound imaging are used to de...

Multimodal kidney-preserving approach in localised and locally advanced high-risk upper tract urothelial carcinoma.

OBJECTIVES: Multimodal kidney-preserving (MKP) strategies may be an option for patients with localised or locally advanced high-risk upper tract uroth...

Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection.

The information captured by the gist signal, which refers to radiologists' first impression arising from an initial global image processing, is poorly...

Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models.

Toll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the ...

CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer.

Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of the tumour region is critical for the doctors. But w...

Phytochemical profile, antioxidant, antiproliferative, and enzyme inhibition-docking analyses of Celep & Doğan.

Celep & Doğan is an endemic species of Turkey. To our knowledge, the number of studies on biological activities and phytochemical profiling of this p...

Development of monoclonal antibodies for quantification of bovine tumor necrosis factor-α.

The expression of the proinflammatory cytokine tumor necrosis factor-α (TNF-α) is associated with production losses in dairy cows and is a hallmark of...

Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals.

BACKGROUND: The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Mos...

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.

OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (infl...

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. Ther...

Comparative optimization of global solar radiation forecasting using machine learning and time series models.

The increasing use of solar energy as a source of renewable energy has led to increasing the interest in photovoltaic (PV) power outputs forecasting. ...

Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network.

To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images o...

Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking.

Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based method...

Exploring Molecular Descriptors and Fingerprints to Predict mTOR Kinase Inhibitors using Machine Learning Techniques.

Mammalian Target of Rapamycin (mTOR) is a Ser/Thr protein kinase, and its role is integral to the autophagy pathway in cancer. Targeting mTOR for ther...