Breast Cancer

Utilization of circulating cell-free DNA profiling to guide first-line chemotherapy in advanced lung squamous cell carcinoma.

Platinum-based chemotherapy is one of treatment mainstay for patients with advanced lung squamous cell carcinoma (LUSC) but it is still a "one-size fits all" approach. Here, we aimed to investigate the predictive and monitoring role of circulating cell-free DNA (cfDNA) profiling for the outcome of first-line chemotherapy in patients with advanced LUSC. Peripheral blood samples of 155 patients fr...

Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases.

Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic ...

Increased Thyroid-Hormone Requirements Consistent With Type 3 Deiodinase Induction Related to Ibrutinib in a Thyroidectomized Woman.

OBJECTIVE: Tyrosine-kinase inhibitors (TKIs) are chemotherapeutic agents associated with increased thyroid-hormone requirements and altered deiodinase...

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic br...

Data-driven dose calculation algorithm based on deep U-Net.

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation alg...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model ...

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.

An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy plan...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment deliver...

Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology - national survey.

BACKGROUND: Artificial Intelligence (AI) is making a continuous progression into the field of Radiation Oncology in Canada and globally. While this fi...

Weight-bearing CT in foot and ankle pathology.

Cone-beam scanners (CBCT) enable CT to be performed under weight-bearing - notably for the foot and ankle. The technology is not new: it has been used...

Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.

Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning ...

Convolutional neural network for automated mass segmentation in mammography.

BACKGROUND: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as d...

Impacts of speciation and extinction measured by an evolutionary decay clock.

The hypothesis that destructive mass extinctions enable creative evolutionary radiations (creative destruction) is central to classic concepts of macr...

Residual breast tissue after robot-assisted nipple sparing mastectomy.

INTRODUCTION: While the long-term oncologic safety of robot-assisted nipple sparing mastectomy (RNSM) remains to be elucidated, histologically detecte...

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (M...

A Prospective Randomised Study to Assess the Analgesic Efficacy of Serratus Anterior Plane (SAP) Block for Modified Radical Mastectomy Under General Anaesthesia.

OBJECTIVE: Breast cancer is the most common malignancy among women and often requires surgery for the removal of the tumour. Uncontrolled pain after b...

Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.

PURPOSE: The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in c...

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides.

The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agen...

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of ...

Comparing Laparotomy with Robot-assisted Interval Debulking Surgery for Patients with Advanced Epithelial Ovarian Cancer Receiving Neoadjuvant Chemotherapy.

STUDY OBJECTIVE: Compare survival of patients with advanced epithelial ovarian cancer (EOC) undergoing interval debulking surgery (IDS) with either ro...