Adaptive Radiotherapy

Adaptive radiotherapy (ART) aims to optimize cancer treatment by adjusting radiation delivery based on real-time imaging and patient response. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformers, to improve image analysis (e.g., CBCT-to-CT synthesis, segmentation, and registration) and predict optimal treatment parameters. These advancements enable more precise dose delivery, potentially reducing side effects and improving treatment efficacy, thereby advancing personalized cancer care.

Papers

October 29, 2024

SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
Jia Wei, Yun Li, Xiaomao Fan, Wenjun Ma, Meiyu Qiu, Hongyu Chen, Wenbin Lei
Swin Transformer Feature Enhancement Lesion Segmentation Tumor Detection Adaptive Radiotherapy

October 16, 2024

Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy
Jintao Ren, Kim Hochreuter, Mathis Ersted Rasmussen, Jesper Folsted Kallehauge, Stine Sofia Korreman
Constructive Approach Human Head Radiation Oncology Neck Tumor Segmentation Adaptive Radiotherapy Gross Tumor Volume

March 7, 2024

Understanding the PULSAR Effect in Combined Radiotherapy and Immunotherapy through Attention Mechanisms with a Transformer Model
Hao Peng, Casey Moore, Debabrata Saha, Steve Jiang, Robert Timmerman
Attention Mechanism Intensity Modulated Radiotherapy Adaptive Radiotherapy Subcutaneous Immunotherapy Personalized Cancer Stereotactic Body Radiation Therapy Immune Checkpoint Pulsar Time of Arrival

February 29, 2024

Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling
Jiaqi Cui, Yuanyuan Xu, Jianghong Xiao, Yuchen Fei, Jiliu Zhou, Xingcheng Peng, Yan Wang
Relation Modeling Dose Map Adaptive Radiotherapy

December 4, 2023

A multi-channel cycleGAN for CBCT to CT synthesis
Chelsea A. H. Sargeant, Edward G. A. Henderson, Dónal M. McSweeney, Aaron G. Rankin, Denis Page
CycleGAN Model Cone Beam Effective Generation Adaptive Radiotherapy

March 7, 2023

Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction
William Trung Le, Chulmin Bang, Philippine Cordelle, Daniel Markel, Phuc Felix Nguyen-Tan, Houda Bahig, Samuel Kadoury
CNN Network Human Head Cone Beam Computed Tomography Adaptive Radiotherapy

October 27, 2022

Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction
Jiabao Sheng, Yuanpeng Zhang, Jing Cai, Sai-Kit Lam, Zhe Li, Jiang Zhang, Xinzhi Teng
Contrastive Learning Margin Maximization Multi View Contrastive Learning Nasopharyngeal Carcinoma Adaptive Radiotherapy View Contrastive Learning

June 7, 2022

Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
Xiao Liang, Howard Morgan, Ti Bai, Michael Dohopolski, Dan Nguyen, Steve Jiang
Automatic Segmentation Deformable Image Registration Cone Beam Adaptive Radiotherapy

April 26, 2022

RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy
Matteo Tortora, Ermanno Cordelli, Rosa Sicilia, Lorenzo Nibid, Edy Ippolito, Giuseppe Perrone, Sara Ramella, Paolo Soda
Multimodal Data Multimodal Learning Radiomics Feature Lung Cancer Multimodal Approach Adaptive Radiotherapy Multimodal Paradigm

February 8, 2022

Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Xiao Liang, Jaehee Chun, Howard Morgan, Ti Bai, Dan Nguyen, Justin C. Park, Steve Jiang
Segmentation Based Approach Deformable Image Registration Test Time Optimization Adaptive Radiotherapy

January 26, 2022

One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
Jue Jiang, Harini Veeraraghavan
Object Shape Anatomical Structure Cone Beam Adaptive Radiotherapy

Adaptive Radiotherapy

Papers

SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection

Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy

Understanding the PULSAR Effect in Combined Radiotherapy and Immunotherapy through Attention Mechanisms with a Transformer Model

Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling

A multi-channel cycleGAN for CBCT to CT synthesis

Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction

Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction

Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy

RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy

Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy

One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs