MRI-based tumour control probability in skull-base chordomas treated with carbon-ion therapy

Buizza, Giulia; Molinelli, S.; D’Ippolito, E.; Fontana, Giulia; Pella, Andrea; Valvo, Francesca; Preda, Lorenzo; Orecchia, Roberto; Baroni, Guido; Paganelli, Chiara

doi:10.1016/j.radonc.2019.04.018

Cited by 11 publications

(12 citation statements)

References 34 publications

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“…Specifically for functional qMRI protocols, dynamic contrast enhanced and diffusion weighted MRI have been widely explored [80][81][82] due to their sensitivity to vasculature architecture [83] and tissue structure [76,84], respectively. These sequences could improve any stage of the radiotherapy workflow [75,85,86]: from diagnosis and patient stratification [87,88], through contouring and dose optimization [89,90], to treatment monitoring and response assessment [83,91,92]. Initial clinical experience for diffusion weighted MRI has been reported for the low field MR-linac [93].…”

Section: Quantitative Imagingmentioning

confidence: 99%

Medical physics challenges in clinical MR-guided radiotherapy

et al. 2020

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The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART. Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation. Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing. The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

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Section: Quantitative Imagingmentioning

confidence: 99%

Medical physics challenges in clinical MR-guided radiotherapy

et al. 2020

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“…Radiomics is not only used in diagnosing chordoma but also in predicting the control rate of chordoma with treatment [35,36]. The model built on radiomic features describing tumor shape and disomic heterogeneity achieved the best performance in predicting tumor control with radiotherapy [35].…”

Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study

Yamazawa

Takahashi

Shin

et al. 2022

Cancers

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Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures.

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“…A recent study showed that MRI-driven cellular density can enhance TCP value differences in patients ( 28 ). Buizza et al evaluated DW-MRI for modelling TCP in skull-base chordomas, which has enrolled in CIRT protocol ( 29 ). The aforementioned methods facilitate personalized and optimized treatments.…”

Section: Functional Imagingmentioning

confidence: 99%

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Pang

Wang

2022

Front. Oncol.

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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 Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

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MRI-based tumour control probability in skull-base chordomas treated with carbon-ion therapy

Cited by 11 publications

References 34 publications

Medical physics challenges in clinical MR-guided radiotherapy

Medical physics challenges in clinical MR-guided radiotherapy

MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

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