Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases

Arends, Sebastiaan R.S.; Savenije, M.H.F.; Eppinga, Wietse S.C.; Velden, Joanne M. van der; Berg, Cornelis A.T. van den; Verhoeff, Joost J.C.

doi:10.1016/j.phro.2022.02.003

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…Two papers in this review focused on automatic segmentation for treatment response [ 45 ] and treatment planning [ 89 ] for metastatic lesions. Moreau et al [ 45 ] compared two methods for bone lesion segmentation in metastatic breast cancer based on the nn-Unet [ 120 ] architecture: (1) use of lesion annotations with PET and CT images as 2-channel input; (2) use of both the reference bone and lesion masks as ground truth.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Paranavithana

Stirling

Ros

et al. 2023

Cancers

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Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.

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Section: Discussionmentioning

confidence: 99%

“…Improved results can be obtained using multimodal imaging modalities like PET/CT [ 36 ]. Arends et al [ 89 ] showed that automatic vertebral body delineation using CNN was of high quality, which can save time in a clinical radiotherapy workflow.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Paranavithana

Stirling

Ros

et al. 2023

Cancers

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“…Their automated segmentation solution achieved a Dice–Sørensen coefficient (DSC) of up to 0.78 and mean sensitivity rates up to 78.9% on par with inter-reader variability DSC of 0.79. Potentially, these models will not only reduce the need for time-consuming manual segmentation of spinal metastases, but also support stereotactic body radiotherapy planning, and improve the performance [ 117 , 118 ] and treatment outcome of minimally invasive interventions for spinal metastasis such as radiofrequency ablation [ 95 ]. In respect to radiotherapy, precise automated tumour contours will improve treatment planning, reduce segmentation times and reduce the radiation dose to the surrounding organs at risk, including the spinal cord.…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Ong

Zhu

Zhang

et al. 2022

Cancers

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Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

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“…The one-day workflow proposed by Palacios et al [2] used automation only to a minor extent and it is therefore highly dependent on the availability of staff throughout the day and not easily scalable to increasing patient numbers. Automated tools for various steps in the radiotherapy planning workflow such as automatic contouring [4] , [5] , [6] , [7] , [8] and radiotherapy planning [9] , [10] , [11] , [12] , [13] recently gained attention. For instance, Johnston et al [7] showed the usability of a convolutional neural network for segmentation of thoracic organs at risk.…”

mentioning

confidence: 99%

“…Although auto-contouring of targets is more challenging, Xie et al [8] recently introduced a 3D neural network for lung lesion contouring. Also, for treatment plan optimization different approaches were proposed [9] , [10] , [11] , [12] , [13] . While automation tools for single workflow steps are already in clinical use, the next goal should be an autonomous workflow integrating contouring and plan optimization.…”

mentioning

confidence: 99%