Chemotherapy

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

Liposome-based anticancer agents take advantage of the increased vascular permeability and transvasc...

[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 mal...

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...

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, r...

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 of...

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 chemothe...

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 baselin...

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...

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 res...

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 ...

Application of statistical machine learning in biomarker selection.

In the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly pr...

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) a...

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 (P...

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 s...

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 chemotherap...

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 oft...

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)...

Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation.

PURPOSE: Manual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal can...

Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives.

This article proposes a summary of the current status of the research regarding the use of radiomics...

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, ...