Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last
decade due to numerous technological breakthroughs. Imaging is now playing a critical role …
decade due to numerous technological breakthroughs. Imaging is now playing a critical role …
Radiation therapy with phenotypic medicine: towards N-of-1 personalization
In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose
divided over daily doses (fractions) given over several weeks. The treatment response is …
divided over daily doses (fractions) given over several weeks. The treatment response is …
A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy
Purpose The impact of radiation therapy on the immune system has recently gained
attention particularly when delivered in combination with immunotherapy. However, it is …
attention particularly when delivered in combination with immunotherapy. However, it is …
Deep reinforcement learning for optimal stopping with application in financial engineering
Optimal stopping is the problem of deciding the right time at which to take a particular action
in a stochastic system, in order to maximize an expected reward. It has many applications in …
in a stochastic system, in order to maximize an expected reward. It has many applications in …
Deep Reinforcement Learning for Early Diagnosis of Lung Cancer
Lung cancer remains the leading cause of cancer-related death worldwide, and early
diagnosis of lung cancer is critical for improving the survival rate of patients. Performing …
diagnosis of lung cancer is critical for improving the survival rate of patients. Performing …
Randomized optimal stopping problem in continuous time and reinforcement learning algorithm
Y Dong - SIAM Journal on Control and Optimization, 2024 - SIAM
In this paper, we study the optimal stopping problem in the so-called exploratory framework,
in which the agent takes actions randomly conditioning on the current state and a …
in which the agent takes actions randomly conditioning on the current state and a …
Optimal treatment plan adaptation using mid-treatment imaging biomarkers
SCM Ten Eikelder, P Ferjančič, A Ajdari… - Physics in Medicine …, 2020 - iopscience.iop.org
Previous studies on personalized radiotherapy (RT) have mostly focused on baseline
patient stratification, adapting the treatment plan according to mid-treatment anatomical …
patient stratification, adapting the treatment plan according to mid-treatment anatomical …
Adjustable robust treatment-length optimization in radiation therapy
SCM Ten Eikelder, A Ajdari, T Bortfeld… - Optimization and …, 2022 - Springer
Traditionally, optimization of radiation therapy (RT) treatment plans has been done before
the initiation of RT course, using population-wide estimates for patients' response to therapy …
the initiation of RT course, using population-wide estimates for patients' response to therapy …
Medical imaging biomarker discovery and integration towards AI-based personalized radiotherapy
Y Pang, H Wang, H Li - Frontiers in Oncology, 2022 - frontiersin.org
Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose
distribution sculpture and employed to modulate different dose levels into Gross Tumor …
distribution sculpture and employed to modulate different dose levels into Gross Tumor …
Artificial intelligence for response prediction and personalisation in radiation oncology
Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and
multifaceted patient data and predicting tumour and normal tissue responses to …
multifaceted patient data and predicting tumour and normal tissue responses to …