Assessing rectal cancer treatment response using coregistered endorectal photoacoustic and us imaging paired with deep learning

Keyphrases

• Deep Learning 100%
• Photoacoustic 100%
• Endorectal 100%
• Rectal Cancer Treatment 100%
• Cancer Therapy Responses 100%
• Convolution Neural Network 100%
• Microscopy 83%
• US System 66%
• Patient Data 33%
• Region of Interest 33%
• Imaging Systems 33%
• Area under the Receiver Operating Characteristic Curve 33%
• Photoacoustic Microscopy 33%
• Convolution Neural Network Model 33%
• Tumor 16%
• Chemotherapy 16%
• Submucosal 16%
• Imaging Marker 16%
• Rectum 16%
• Radiologic 16%
• Scar 16%
• Residual Tumor 16%
• Diagnostic Performance 16%
• Imaging Probe 16%
• Patient Study 16%
• Recovery Process 16%
• Microscopic Imaging 16%
• Tumor Bed 16%
• Microscopy System 16%
• Colorectal Tissue 16%
• Convolutional Neural Network Model 16%
• US Regions 16%
• Vascular Architecture 16%
• Residual Cancer 16%
• Neural Network Training 16%
• Network Validation 16%
• 1064 Nm Laser 16%
• Complete Tumor Response 16%
• Ring Transducer 16%
• In Vivo Images 16%

Medicine and Dentistry

• Cancer Therapy 100%
• Treatment Response 100%
• Neoplasm 100%
• Rectum Cancer 100%
• Ex Vivo 75%
• Patient-Data 50%
• Photoacoustic Microscopy 50%
• Malignant Neoplasm 25%
• Prospective Study 25%
• Scar Formation 25%
• Diagnostic Performance 25%