Chemotherapy

Cytotoxic and Apoptotic Properties of the Flavonoid-rich Ethyl Acetate Fraction of the Crude Methanol Leaf Extract of (Blume) Merr. (Myrtaceae) against MCF-7 Breast Cancer Cells.

INTRODUCTION: Breast cancer is the most common cancer among women in the Philippines and about 3 in every 100 Filipina will be diagnosed with breast cancer in their lifetime. There is a need to discover safe, yet inexpensive herbal extracts with potential cytotoxic properties as potential treatment modalities to treat breast cancer.

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.

SIGNIFICANCE: Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical ado...

Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning.

Liposome-based anticancer agents take advantage of the increased vascular permeability and transvascular pressure gradients for selective accumulation...

[Multistain deep learning as a prognostic and predictive biomarker in colorectal cancer].

The tumor immune microenvironment (TIME) plays a crucial prognostic and predictive role in solid malignancies such as colorectal cancer (CRC). Neverth...

Cytotoxic activity of ethyl acetate extract of on MCF7 and T47D breast cancer cells.

One of the most cancers in women that can be fatal is breast cancer. Radiation therapy, chemotherapy, or a combination of the two are often used to tr...

Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers...

Robotic retroperitoneal lymph node dissection for paratesticular rhabdomyosarcoma in adolescents: a case series.

Robotic assisted (RA) retroperitoneal lymph node dissection (RPLND) has grown in popularity as it offers decreased morbidity and faster recovery compa...

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients res...

ASO Author Reflections: Deep-Learning Radiomics Nomogram Based on Enhanced CT to Predict the Effect of Neoadjuvant Chemotherapy on Metastatic Lymph Nodes in Locally Advanced Gastric Cancer.

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) im...

Learning curve for robotic rectal cancer resection at a community-based teaching institution.

The surgical management of rectal cancer is shifting toward more widespread use of robotics across a spectrum of medical centers. There is evidence th...

Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.

RATIONALE AND OBJECTIVES: To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in brea...

Methyl cellulose/okra mucilage composite films, functionalized with Hypericum perforatum oil and gentamicin, as a potential wound dressing.

There is a growing demand for the development of functional wound dressings enriched with bioactive natural compounds to improve the quality of life o...

Application of statistical machine learning in biomarker selection.

In the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supporti...

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy.

OBJECTIVES: This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients wit...

Chewing gum prophylaxis for postoperative nausea and vomiting in the intensive care unit: a pilot randomised controlled trial.

To test the effectiveness of chewing gum in the prophylaxis of postoperative nausea and vomiting (PONV) in patients admitted to the intensive care un...

Evaluation of the cytotoxic effect of titanium dioxide nanoparticles in human embryonic lung cells.

BACKGROUND/AIM: Titanium dioxide nanoparticles are widely used in a variety of products, including sunscreens, paints, and ceramics. However, their in...

Deep learning-assisted high-content screening identifies isoliquiritigenin as an inhibitor of DNA double-strand breaks for preventing doxorubicin-induced cardiotoxicity.

BACKGROUND: Anthracyclines including doxorubicin are essential components of many cancer chemotherapy regimens, but their cardiotoxicity severely limi...

A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy.

BACKGROUND: The macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This stud...

MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models.

The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only...