Interval between planning and frameless stereotactic radiosurgery for brain metastases: are our margins still accurate?

Bronnimann, Charlotte; Huchet, A.; Benech-Faure, J.; Dutriaux, Caroline; Saut, Olivier; Blais, E.; Mollier, Olivier; Trouette, R.; Vendrely, V.

doi:10.1093/nop/npz048

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Our study of large brain metastases showed that tumor volume increases in a short period of time. This finding is similar to those in previous reports measuring brain metastases of various sizes in non-small cell lung cancer and melanoma, which showed that tumor progression between dMRI and pMRI was observed in 82% of cases [ 7 ]. However, our average growth rate of 0.21 cm 3 /day appears to be considerably higher than previously reported rates.…”

Section: Discussionsupporting

confidence: 92%

“…If the tumor grows beyond the PTV margin between SRS-planning magnetic resonance imaging (MRI) and delivery of treatment, the tumor may not receive an adequate radiation dose. Although previous studies have reported growth rates of small brain metastases before sfSRS planning [ 7 – 9 ], to the best of our knowledge, none have examined tumor growth speed of large (≥ 2 cm) brain metastases before fSRS planning. Therefore, we aimed to examine the growth speed of large brain metastases between diagnostic and radiosurgical planning MRI and investigate the predictors of rapid tumor growth.…”

Section: Introductionmentioning

confidence: 99%

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Growth speed of large brain metastases between diagnostic and radiosurgical planning MRI and predictors of rapid tumor growth

Takata,

Kosen,

Matsumoto

et al. 2024

Jpn J Radiol

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Purpose We aimed to assess volumetric changes of large brain metastases (≥ 2 cm) between their diagnosis and planning for treatment with fractionated stereotactic radiation surgery (fSRS). Predictors of rapid tumor growth were also analyzed. Materials and methods One hundred nine patients harboring 126 large brain metastases were retrospectively evaluated. Tumor characteristics were evaluated on diagnostic magnetic resonance imaging (dMRI) and MRI performed when planning fSRS (pMRI). Average tumor growth rate and percentage growth rate were calculated. Predictors of rapid growth (percentage growth rate > 5%) were determined using multivariate logistic regression. Results Both tumor diameter and volume were significantly larger on pMRI than on dMRI (P < 0.001). Median tumor percentage growth rate was 2.6% (range, − 10.8–43.3%). Eighty-eight tumors (70%) were slow-growing (percentage growth rate < 5%) and 38 (30%) grew rapidly (percentage growth rate ≥ 5%). Major peritumoral edema and no steroids were predictors of rapid tumor growth. Conclusion Large brain metastases can grow considerably between the time of diagnosis and the time of fSRS treatment planning. We recommend the time between dMRI and fSRS treatment initiation be as short as possible.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Growth speed of large brain metastases between diagnostic and radiosurgical planning MRI and predictors of rapid tumor growth

Takata,

Kosen,

Matsumoto

et al. 2024

Jpn J Radiol

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“…Several authors have wondered whether the interval between SRS planning and treatment is accurate [37][38][39][40]. In order to study this, they compared volumes from diagnostic imaging and radiosurgery planning MRI and extrapolated the growth linearly to the day of SRS, since only two time points were available.…”

Section: Discussionmentioning

confidence: 99%

The Growth Laws of Brain Metastases

Ocaña-Tienda

Pérez-Beteta

Molina-García

et al. 2022

Preprint

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Tumor growth is the result of the interplay of complex biological processes in a huge number of individual cells in a changing environment. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or in animal models with bounded-growth dynamics accurately. However, results for human cancers in patients are scarce. The study mined a dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up, treated with radiosurgery (SRS) to find growth laws for untreated BMs, relapsing treated BMs, and radiation necrosis (RN). Untreated BMs showed sustained growth acceleration, most likely related to the underlying evolutionary dynam- ics. Relapsing BM growth was slower, most probably due to a reduction in tumor heterogeneity after SRS, which may limit the evolutionary possibilities of the tumor. RN lesions had significantly larger growth exponents than relapsing BMs, providing a way to differentiate them from true pro- gression. This may help in solving a problem of clinical relevance, since the first condition may resolve spontaneously, and not require further work-up, while the second requires therapeutic action.

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“…With precise delineation and tight PTV margins, delays from the treatment planning to the treatment completion must be as short as possible. A recent brain MRI (<15 days) is essential for the delineation of GTV and it often needs to be renewed to meet this criterion [ 44 ]. With our workflow, one patient could be treated within one week of the diagnostic MRI (vs 3 to 4 weeks).…”

Section: Discussionmentioning

confidence: 99%

Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy

Bourbonne

Jaouen

Hognon

et al. 2021

Cancers

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Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local control rates and better sparing of organs at risk. An MRI-only workflow could reduce the risk of misalignment between magnetic resonance imaging (MRI) brain studies and computed tomography (CT) scanning for SRT planning, while shortening delays in planning. Given the absence of a calibrated electronic density in MRI, we aimed to assess the equivalence of synthetic CTs generated by a generative adversarial network (GAN) for planning in the brain SRT setting. Methods: All patients with available MRIs and treated with intra-cranial SRT for brain metastases from 2014 to 2018 in our institution were included. After co-registration between the diagnostic MRI and the planning CT, a synthetic CT was generated using a 2D-GAN (2D U-Net). Using the initial treatment plan (Pinnacle v9.10, Philips Healthcare), dosimetric comparison was performed using main dose-volume histogram (DVH) endpoints in respect to ICRU 91 guidelines (Dmax, Dmean, D2%, D50%, D98%) as well as local and global gamma analysis with 1%/1 mm, 2%/1 mm and 2%/2 mm criteria and a 10% threshold to the maximum dose. t-test analysis was used for comparison between the two cohorts (initial and synthetic dose maps). Results: 184 patients were included, with 290 treated brain metastases. The mean number of treated lesions per patient was 1 (range 1–6) and the median planning target volume (PTV) was 6.44 cc (range 0.12–45.41). Local and global gamma passing rates (2%/2 mm) were 99.1 CI95% (98.1–99.4) and 99.7 CI95% (99.6–99.7) respectively (CI: confidence interval). DVHs were comparable, with no significant statistical differences regarding ICRU 91′s endpoints. Conclusions: Our study is the first to compare GAN-generated CT scans from diagnostic brain MRIs with initial CT scans for the planning of brain stereotactic radiotherapy. We found high similarity between the planning CT and the synthetic CT for both the organs at risk and the target volumes. Prospective validation is under investigation at our institution.

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Interval between planning and frameless stereotactic radiosurgery for brain metastases: are our margins still accurate?

Cited by 7 publications

References 14 publications

Growth speed of large brain metastases between diagnostic and radiosurgical planning MRI and predictors of rapid tumor growth

Growth speed of large brain metastases between diagnostic and radiosurgical planning MRI and predictors of rapid tumor growth

The Growth Laws of Brain Metastases

Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy

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