Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

Zhao, Wei; Shen, Liyue; Islam, Tauhidul; Qin, Wenjian; Zhang, Zhicheng; Liang, Xiaokun; Zhang, Gaolong; Xu, Shouping; Li, Xiaomeng

doi:10.21037/qims-21-199

Cited by 21 publications

(13 citation statements)

References 91 publications

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“…Artificial intelligence algorithms based on the radiomic features of CT can accurately classify whether a patient is suitable for indirect tumor tracking using RPM; this approach is also applicable to tumor tracking using SGRT. Most methods for estimating tumor motion and for indirect tumor tracking achieve accuracy in tracking performance through the use of complicated systems that ignore patient specificity (69). For some patients, it is possible to use simple and common techniques to achieve accurate tumor tracking from external respiratory signals because of the strong correlation coefficients between their external signals and internal tumor motion (31).…”

Section: Discussionmentioning

confidence: 99%

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

Li¹,

Zhang²,

Song³

et al. 2023

Quant Imaging Med Surg

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Background: Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms.Methods: Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intensity projections, and cross-validation least absolute shrinkage and selection operator (LassoCV) was used to conduct feature selection. Based on the radiomic features, a total of 26 separate models (13 for the lung and 13 for the liver) were trained and tested. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess performance.Relative standard deviation was used to assess stability.Results: Thirty-three and 22 radiomic features were selected for the lung and liver, respectively. For the lung, the AUC varied from 0.848 (decision tree) to 0.941 [support vector classifier (SVC), logistic regression]; sensitivity varied from 0.723 (extreme gradient boosting) to 0.848 [linear support vector classifier (linearSVC)]; specificity varied from 0.834 (gaussian naive bayes) to 0.936 [multilayer perceptron (MLP), wide and deep (W&D)]; and MLP and W&D had better performance and stability than the median. For the liver, the AUC varied from 0.677 [light gradient boosting machine (Light)] to 0.892 (logistic regression); sensitivity varied from 0.717 (W&D) to 0.862 (MLP); specificity varied from 0.566 (Light) to 0.829 (linearSVC); and logistic regression, MLP, and SVC had better performance and stability than the median.Conclusions: Respiratory-sensitive radiomic features extracted from CT images of lung and liver tumors were proved to contain sufficient information to establish an external/internal motion relationship. We developed a rapid and accurate method based on radiomics to classify the accuracy of monitoring a patient's external surface for lung and liver tumor tracking. Several machine learning algorithms-in particular, MLP-demonstrated excellent classification performance and stability.

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Section: Discussionmentioning

confidence: 99%

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

Li¹,

Zhang²,

Song³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…To the best of our knowledge, to date, no review has sought to summarize the latest developments and the overall situation of deep learning-based real-time target tracking with 2D kV X-ray images. Mylonas et al ( 49 ), Zhao et al ( 50 ), and Salari et al ( 51 ) reviewed the topic of artificial intelligence (AI)-based motion tracking. However, their reviews focused on AI-based methods for target tracking, including machine-learning and deep-learning methods, and considered diverse image modalities, such as magnetic resonance imaging, CT, ultrasound, and X-ray.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review

Liu,

Geng,

Huang

et al. 2024

Quant Imaging Med Surg

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Background and Objective As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Methods Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included “radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network”. Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. Key Content and Findings In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Conclusions Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

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“…Given the differences in application, reviews of simulation training for general robotics, such as [47], focus on the use of RGB(-D) imaging, which generally does not apply in the context of MIS. Further, although previous reviews include recent advances in MIS [48][49][50][51][52][53][54][55][56][57][58][59], robotic-assisted MIS [55,[60][61][62][63], machine learning in surgical interventions [34,35,[64][65][66][67][68][69][70][71][72], or surgical simulation for human training purposes [73][74][75][76], in silico training specifically for intelligent MIS systems remains an emerging area deserving of an introduction. We focus this review on frameworks and successful applications in three imaging modalities which have received the bulk of researchers' attention, namely endoscopy, ultrasound (US), and x-ray.…”

Section: Introductionmentioning

confidence: 99%

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

et al. 2023

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To mitigate the challenges of operating through narrow incisions under image guidance, there is a desire to develop intelligent systems that assist decision making and spatial reasoning in minimally invasive surgery (MIS). In this context, machine learning-based systems for interventional image analysis are receiving considerable attention because of their flexibility and the opportunity to provide immediate, informative feedback to clinicians. It is further believed that learning-based image analysis may eventually form the foundation for semior fully automated delivery of surgical treatments. A significant bottleneck in developing such systems is the availability of annotated images with sufficient variability to train generalizable models, particularly the most recently favored deep convolutional neural networks or transformer architectures. A popular alternative to acquiring and manually annotating data from the clinical practice is the simulation of these data from human-based models. Simulation has many advantages, including the avoidance of ethical issues, precisely controlled environments, and the scalability of data collection. Here, we survey recent work that relies on in silico training of learning-based MIS systems, in which data are generated via computational simulation. For each imaging modality, we review available simulation tools in terms of compute requirements, image quality, and usability, as well as their applications for training intelligent systems. We further discuss open challenges for simulation-based development of MIS systems, such as the need for integrated imaging and physical modeling for non-optical modalities, as well as generative patient models not dependent on underlying CT, MRI, or other patient data. In conclusion, as the capabilities of in silico training mature, with respect to sim-to-real transfer, computational efficiency, and degree of control, they are contributing toward the next generation of intelligent surgical systems.

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Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

Cited by 21 publications

References 91 publications

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

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