Artificial intelligence in radiotherapy

Li, Guangqi; Wu, Xin; Ma, Xuelei

doi:10.1016/j.semcancer.2022.08.005

Cited by 25 publications

(7 citation statements)

References 171 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also improve accuracy and consistency in tasks requiring high precision, leading to more effective treatment plans [ 9 ]. AI-based tools add the advantage of adaptability, improving performance with increasing data inputs, and can manage extensive data volumes and complex computations [ 10 , 11 ]. Despite challenges such as ensuring reliability, data privacy, and seamless workflow integration, these tools represent a significant advancement in radiotherapy.…”

Section: Discussionmentioning

confidence: 99%

Survey in radiation oncology departments in Germany, Austria, and Switzerland: state of digitalization by 2023

Janssen,

El Shafie,

Grohmann

et al. 2023

Strahlenther Onkol

View full text Add to dashboard Cite

Purpose The aim of this work was to assess the current state of digitalization in radiation oncology departments in Germany, Austria, and Switzerland. Methods A comprehensive survey was conducted in a digital format, consisting of 53 questions that covered various aspects of digitalization including patient workflow, departmental organization, radiotherapy planning, and employee-related aspects. Results Overall, 120 forms were eligible for evaluation. Participants were mainly physicians or medical physicists responsible for digitalization aspects in their departments. Nearly 70% of the institutions used electronic patient records, with 50% being completely paperless. However, the use of smartphone apps for electronic patient reported outcomes (ePROMs) and digital health applications (DIGA) was limited (9% and 4.9%, respectively). In total, 70.8% of the radio-oncology departments had interfaces with diagnostic departments, and 36% had digital interchanges with other clinics. Communication with external partners was realized mainly through fax (72%), e‑mails (55%), postal letters (63%), or other digital exchange formats (28%). Almost half of the institutions (49%) had dedicated IT staff for their operations. Conclusion To the best of our knowledge, this survey is the first of its kind conducted in German-speaking radiation oncology departments within the medical field. The findings suggest that there is a varied level of digitalization implementation within these departments, with certain areas exhibiting lower rates of digitalization that could benefit from targeted improvement initiatives.

show abstract

Section: Discussionmentioning

confidence: 99%

Survey in radiation oncology departments in Germany, Austria, and Switzerland: state of digitalization by 2023

Janssen,

El Shafie,

Grohmann

et al. 2023

Strahlenther Onkol

View full text Add to dashboard Cite

show abstract

“…In the future, both biomarkers and artificial intelligence science are anticipated to assist with stratifying patients into specific groups by creating patient profiles who share common features. These tools will lead to the development of individualized treatments and prognostic treatment-response scores in chemotherapy and/or radiotherapy[ 68 , 69 ].…”

Section: Discussionmentioning

confidence: 99%

Primary adrenal Ewing sarcoma: A systematic review of the literature

Manatakis,

Tsouknidas,

Mylonakis

et al. 2023

World J Clin Cases

View full text Add to dashboard Cite

β cells are the main cells responsible for the hypoglycemic function of pancreatic islets, and the insulin secreted by these cells is the only hormone that lowers blood glucose levels in the human body. β cells are regulated by various factors, among which neurotransmitters make an important contribution. This paper discusses the effects of neurotransmitters secreted by various sympathetic and parasympathetic nerves on β cells and summarizes the mechanisms by which various neurotransmitters regulate insulin secretion. Many neurotransmitters do not have a single source and are not only released from nerve terminals but also synthesized by β cells themselves, allowing them to synergistically regulate insulin secretion. Almost all of these neurotransmitters depend on the presence of glucose to function, and their actions are mostly related to the Ca2+ and cAMP concentrations. Although neurotransmitters have been extensively studied, many of their mechanisms remain unclear and require further exploration by researchers.

show abstract

“…Traditionally, evaluating the segmentation quality involves comparing the predicted contours with the ground-truth contours (manually generated by an experienced physicist and independently verified by a radiation oncologist). 19 , 20 However, it might be impossible to use the traditional measure to assess automatic segmentation quality if some contours lack the ground-truth contours. Therefore, a new method to evaluate segmentation quality without requiring ground-truth contours is crucial for clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…For a more review about QA of DL segmentations, we refer readers to references. 19,20 In this study, a fully automatic QA method based on image texture 29 and ML is proposed. This QA method adopted an integrated ML model for quality prediction and used an anisotropic method for further analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy

Luan

Xue

Wei

et al. 2023

Technol Cancer Res Treat

View full text Add to dashboard Cite

Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists’ labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells’ radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours’ location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices.

show abstract

Artificial intelligence in radiotherapy

Cited by 25 publications

References 171 publications

Survey in radiation oncology departments in Germany, Austria, and Switzerland: state of digitalization by 2023

Survey in radiation oncology departments in Germany, Austria, and Switzerland: state of digitalization by 2023

Primary adrenal Ewing sarcoma: A systematic review of the literature

Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy

Contact Info

Product

Resources

About