Chemotherapy

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, which can subsequently be examined in patients that have been treated with the same drugs. These gene signatures typically contain elements of multiple...

Characterization of Nanoscale Organization of F-Actin in Morphologically Distinct Dendritic Spines Using Supervised Learning.

The cytoarchitecture of a neuron is very important in defining morphology and ultrastructure. Although there is a wealth of information on the molecular components that make and regulate these ultrastructures, there is a dearth of understanding of how these changes occur or how they affect neurons in health and disease. Recent advances in nanoscale imaging which resolve cellular structures at the ...

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.

BACKGROUND: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients t...

Prediction of Chemotherapy Response of Osteosarcoma Using Baseline F-FDG Textural Features Machine Learning Approaches with PCA.

PURPOSE: Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is...

Characterization and potential antitumor effect of a heteropolysaccharide produced by the red alga .

Taking into account the rising trend of the incidence of cancers of various organs, effective therapies are urgently needed to control human malignanc...

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to p...

Physicochemical stability of nefopam and nefopam/droperidol solutions in polypropylene syringes for intensive care units.

INTRODUCTION: Nefopam has been reported to be effective in postoperative pain control with an opioid-sparing effect, but the use of nefopam can lead t...

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients.

OBJECTIVES: The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on ...

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, possibly leading to the delay or suspension of t...

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 advanced rectal cancer (LARC) using radiomics and d...

Machine-learned target volume delineation of F-FDG PET images after one cycle of induction chemotherapy.

Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptak...

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. The subject of a controller design approach for th...

Qualification of a chemotherapy-compounding robot.

KIRO® Oncology (Kiro Grifols, Spain) is a robotic system for automated compounding of sterile injectable drugs including intravenous cytotoxic treatme...

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 osteosarcoma. This study reports the first fully automated...

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.

PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individ...

Robot-Assisted Simple Prostatectomy: Illustration of a Simplified Extraperitoneal Transcapsular Technique.

Robot-assisted simple prostatectomy (RASP) performed with the extraperitoneal (EP) technique (RASP-EP) minimizes the risk of bowel injury, particular...

Accurate intracellular and in vivo temperature sensing based on CuInS/ZnS QD micelles.

Accurate and sensitive temperature sensing is of great importance for studying biological activities in cells and tissues. Due to the complexity of th...

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

PURPOSE: To identify the role of radiomics texture features both within and outside the nodule in predicting time to progression (TTP) and overall su...

Reinforcement learning-based control of tumor growth under anti-angiogenic therapy.

BACKGROUND AND OBJECTIVES: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accor...

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

KEY POINTS: Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to ...