An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy

Zhang, Jiahan; Wang, Chunhao; Yang, Sheng; Palta, Manisha; Czito, Brian G.; Willett, Christopher G.; Zhang, Jiang; Jensen, P. James; Yin, Fang‐Fang; Ge, Yaorong; Wu, Qiuwen

doi:10.1016/j.ijrobp.2020.10.019

Cited by 29 publications

(26 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in a trial and error fashion. The use of reinforcement learning for medical imaging is still not very extended, but has increased in the last couple of years, with promising applications that allow mimicking physician behaviour for typical tasks such as the design of a treatment [50][51][52][53][54][55], among others (Table 1). On top of these three basic learning frameworks (supervised, unsupervised and reinforcement learning), there are other strategies that enable us to reuse previously trained models (transfer learning) or combine models (ensemble learning).…”

Section: Learning Framework and Strategiesmentioning

confidence: 99%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

200

View full text Add to dashboard Cite

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

show abstract

Section: Learning Framework and Strategiesmentioning

confidence: 99%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

200

View full text Add to dashboard Cite

show abstract

“…used the subnetwork design in their DRL‐based automatic planning for HDR brachytherapy for cervical cancers, 24 while Zhang et al. used a single model for all the actions to achieve automatic planning for pancreas SBRT 23 . We chose the subnetwork design in this study since it was more efficient to train smaller subnetworks.…”

Section: Discussionmentioning

confidence: 99%

Automatic inverse treatment planning of Gamma Knife radiosurgery via deep reinforcement learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Purpose Several inverse planning algorithms have been developed for Gamma Knife (GK) radiosurgery to determine a large number of plan parameters by solving an optimization problem, which typically consists of multiple objectives. The priorities among these objectives need to be repetitively adjusted to achieve a clinically good plan for each patient. This study aimed to achieve automatic and intelligent priority tuning by developing a deep reinforcement learning (DRL)‐based method to model the tuning behaviors of human planners. Methods We built a priority‐tuning policy network using deep convolutional neural networks. Its input was a vector composed of multiple plan metrics that were used in our institution for GK plan evaluation. The network can determine which tuning action to take based on the observed quality of the intermediate plan. We trained the network using an end‐to‐end DRL framework to approximate the optimal action‐value function. A scoring function was designed to measure the plan quality to calculate the received reward of a tuning action. Results Vestibular schwannoma was chosen as the test bed in this study. The number of training, validation and testing cases were 5, 5, and 16, respectively. For these three datasets, the average scores of the initial plans obtained with the same initial priority set were 3.63 ± 1.34, 3.83 ± 0.86 and 4.20 ± 0.78, respectively, while they were improved to 5.28 ± 0.23, 4.97 ± 0.44 and 5.22 ± 0.26 through manual priority tuning by human expert planners. Our network achieved competitive results with 5.42 ± 0.11, 5.10 ± 0. 42, 5.28 ± 0.20, respectively. Conclusions Our network can generate GK plans of comparable or slightly higher quality than the plans generated by human planners via manual priority tuning for vestibular schwannoma cases. The network can potentially be incorporated into the clinical workflow as planning assistance to improve GK planning efficiency and help to reduce plan quality variation caused by interplanner variability. We also hope that our method can reduce the workload of GK planners and allow them to spend more time on more challenging cases.

show abstract

“…Correspondingly, Shen et al [ 50 ] used DRL in a virtual environment to generate treatment plans by training on 10 and validating on 64 cases of patients with prostate cancer. In a similar approach, Zhang et al [ 51 ] trained an RL agent on augmented treatment plans of 16 previously treated patients that received pancreas stereotactic body radiation therapy which was validated on 24 treatment plans, achieving a treatment quality comparable to clinical plans. It is to be noted that while the majority of the presented studies describe their algorithms in great mathematical detail, the information about the general problem setup and algorithm architecture has to be easily accessible to both software engineers and clinicians.…”

Section: Recent Studies Of Reinforcement Learning In Malignant Diseasementioning

confidence: 99%

Reinforcement Learning for Precision Oncology

Wendt

Bornhäuser

Middeke

2021

Cancers

View full text Add to dashboard Cite

Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.

show abstract

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy

Cited by 29 publications

References 16 publications

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence and machine learning for medical imaging: A technology review

Automatic inverse treatment planning of Gamma Knife radiosurgery via deep reinforcement learning

Reinforcement Learning for Precision Oncology

Contact Info

Product

Resources

About