Chemotherapy

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer.

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study.

BACKGROUND: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions.

Robot-assisted thoracic surgery for stages IIB-IVA non-small cell lung cancer: retrospective study of feasibility and outcome.

Robot-assisted thoracic surgery (RATS) for higher stages non-small cell lung carcinoma (NSCLC) remains controversial. This study reports the feasibili...

Successful all robotic-assisted excision of highly malignant mediastinal neuroblastoma in a toddler: A case report.

An otherwise well 28-month-old girl presented with fever/left thigh pain. Computed tomography identified a 7 cm right posterior mediastinal tumor exte...

Comparison of neoadjuvant chemohormonal therapy vs. extended pelvic lymph-node dissection in high-risk prostate cancer treated with robot-assisted radical prostatectomy.

We compared the impact of treatment strategies on postoperative complications and prognosis between robot-assisted radical prostatectomy (RARP) plus e...

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions.

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather tha...

Evaluation of Robotic Systems on Cytotoxic Drug Preparation: A Systematic Review and Meta-Analysis.

: With the increased prevalence of patients with cancer, the demand for preparing cytotoxic drugs was increased by health-system pharmacists. To reduc...

Clinical applications of deep learning in breast MRI.

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn fro...

Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming.

This article investigates optimal regulation scheme between tumor and immune cells based on the adaptive dynamic programming (ADP) approach. The thera...

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.

OBJECTIVES: The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-makin...

Dental innovations which will influence the oral health care of baby boomers.

From the widespread use of smartphones and tablets to the multitude of applications available, older adults are showing an interest in utilizing techn...

Adaptive fuzzy control of drug delivery in cancer treatment using combination of chemotherapy and antiangiogenic therapy.

This paper introduces the adaptive fuzzy control scheme as a promising control technique for cancer treatment from a theoretical point of view. A math...

ASO Author Reflections: Safety and Feasibility of Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) with Three-Field Lymphadenectomy and Neoadjuvant Chemoradiotherapy in Patients with Resectable Esophageal Cancer and Cervical Lymph Node Metastasis.

Safety and Feasibility of Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) with Three-Field Lymphadenectomy and Neoadjuvant Chemoradiotherapy in Patients with Resectable Esophageal Cancer and Cervical Lymph Node Metastasis.

BACKGROUND: In the West, patients with cervical lymph node metastasis of resectable esophageal cancer at diagnosis are generally precluded from curati...

Quantifying stiffness and forces of tumor colonies and embryos using a magnetic microrobot.

Stiffness and forces are two fundamental quantities essential to living cells and tissues. However, it has been a challenge to quantify both 3D tracti...

Robot-assisted versus conventional laparoscopic radical hysterectomy in cervical cancer stage IB1.

The aim of this study was to compare survival outcomes of robot-assisted laparoscopic radical hysterectomy (RRH) and conventional laparoscopic radica...

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently ...

Bioinspired microrobots: Opportunities and challenges in targeted cancer therapy.

Chemotherapy is still the most effective technique to treat many forms of cancer. However, it also carries a high risk of side effects. Numerous nanom...

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer.

Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring syst...

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients.

The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual ...