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Chemoradiotherapy

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Robot-assisted versus thoracolaparoscopic oesophagectomy for locally advanced oesophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Robot-assisted oesophagectomy (RAE) and thoracolaparoscopic oesophagectomy (TLE) are surgical techniques for the treatment of oesophageal cancer. This study aimed to compare the perioperative and mid-term outcomes of RAE versus TLE for ...

Case of robot-assisted salvage surgery for esophageal cancer with a mediastinal fistula after definitive chemoradiotherapy.

Asian journal of endoscopic surgery
Salvage surgery for esophageal cancer after definitive chemoradiotherapy (dCRT) is effective, but it is associated with a high rate of perioperative complications. The indications for robot-assisted minimally invasive esophagectomy (RAMIE) are expand...

Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer.

Asian journal of surgery
BACKGROUND: For locally advanced rectal cancer (LARC), accurate response evaluation is necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and-wait (W&W) strategy. Algorithms based on deep learning have shown great val...

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

Nuclear medicine communications
OBJECTIVES: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant m...

Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.

Scientific reports
Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In th...

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

La Radiologia medica
OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a me...

Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.

International journal of colorectal disease
PURPOSE: This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning-based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images.

Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts.

JAMA oncology
IMPORTANCE: Toxic effects of concurrent chemoradiotherapy (CRT) can cause treatment interruptions and hospitalizations, reducing treatment efficacy and increasing health care costs. Physical activity monitoring may enable early identification of pati...

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis.

The British journal of radiology
OBJECTIVES: To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.