AIMC Topic: Chemoradiotherapy
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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
Mar 21, 2024
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...
Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.
Scientific reports
Jan 12, 2024
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...
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
Dec 27, 2023
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...
Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer.
Asian journal of surgery
Apr 15, 2023
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...
Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning.
Acta oto-laryngologica
Feb 15, 2023
BACKGROUND: A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage.
Case of robot-assisted salvage surgery for esophageal cancer with a mediastinal fistula after definitive chemoradiotherapy.
Asian journal of endoscopic surgery
Jan 2, 2023
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...
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
Nov 29, 2022
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 ...
Short-term outcomes of robot-assisted versus conventional laparoscopic surgery for mid and low rectal cancer after neoadjuvant chemoradiotherapy: a propensity score-matched analysis.
Journal of robotic surgery
Nov 21, 2022
The benefits of robot-assisted laparoscopic surgery (RALS) for rectal cancer remain controversial. Only a few studies have evaluated the safety and feasibility of RALS following neoadjuvant chemoradiotherapy (NCRT). This study aimed to compare the sh...
Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study.
Abdominal radiology (New York)
Jun 16, 2022
PURPOSE: To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation.
A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.
The British journal of radiology
May 23, 2022
OBJECTIVES: To propose deep-learning (DL)-based predictive model for pathological complete response rate for resectable locally advanced esophageal squamous cell carcinoma (SCC) after neoadjuvant chemoradiotherapy (NCRT) with endoscopic images.
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