MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation

Background

The glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation.

Materials and methods

We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model.

Results

Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68-0.88).

Conclusion

This is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy.

P02.11.B An hypothesis generating study of MRI-Derived Radiomics on tumor and microenvironment tissue heterogeneity to guide post-operative management of glioblastoma: toward personalized radiation treatment volume delineation

Background

The glioblastoma’s bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification.The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigated the role of radiomic analysis in GBM management, to verify if radiomic features in the tissue around the resection cavity which may guide the radiation target volume delineation.

Material and Methods

We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction.The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologist reviewed by a neuroradiologist, both with at least 10 years of experience. The Region of interest (ROI) considered for the analysis were: the surgical cavity +/- post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modelling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using the 226 features extracted from the Ring to perform a binary classification and predict the PFS at 6 months (statistical, morphological and textural features). A 3-fold cross-validation repeated five times was implemented for internal validation of the model.

Results

Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68 - 0.88).

Conclusion

This is the first hypothesis-generating study who applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GBM patients in order to achieve a margin reduction strategy.

VMAT-like plans for magnetic resonance guided radiotherapy: Addressing unmet needs

PURPOSE

VMAT delivery technique is currently not applicable to Magnetic Resonance-guided radiotherapy (MRgRT) hybrid systems. Aim of this study is to evaluate an innovative VMAT-like (VML) delivery technique.

MATERIAL AND METHODS

First, planning and dosimetric evaluation of the MRgRT VML treatment have been performed on 10 different disease sites and the results have been compared with the corresponding IMRT plans. Then, in the second phase, 10 of the most dosimetrically challenging locally advanced pancreas treatment plans have been retrospectively re-planned using the VML approach to explore the potentiality of this new delivery technique. Finally, VML robustness was evaluated and compared with the IMRT plans, considering a lateral positioning error of ± 5 mm.

RESULTS

In phase one, all VML plans were within constraint for all OARs. When PTV coverage is considered, in the 50% of the cases VML PTV coverage is equal or higher than in IMRT plan. In the remaining 50%, the highest target under coverage difference in comparison with IMRT plan is -1.71%. The mean and maximum treatment time differences (VML-IMRT) is 0.2 min and 3.1 min respectively. In phase two, the treatment time variation (VML-IMRT), shows a mean, maximum and minimum variations of 1.3, 4.6 and -0.6 min respectively. All VML plans have a better target coverage if compared with IMRT plans, keeping in any case the OARs constraints within tolerance. VML doesn't increase plan robustness.

CONCLUSION

VMAT-like treatment approach appeared to be an efficient planning solution and it was decided to clinically implement it in daily practice, especially in the frame of hypo fractionated treatments.

Dosimetric accuracy of dual isocenter irradiation in low magnetic field resonance guided radiotherapy system for extended abdominal tumours

PURPOSE

Due to limited field size of Magnetic Resonance Linear Accelerators (MR-Linac), some treatments could require a dual-isocenter planning approach to achieve a complete target coverage and thus exploit the benefits of the online adaptation. This study evaluates the dosimetric accuracy of the dual-isocenter intensity modulated radiation therapy (IMRT) delivery technique for MR-Linac.

MATERIAL AND METHODS

Dual-isocenter multi leaf collimator (MLC) and couch accuracy tests have been performed to evaluate the delivery accuracy of the system. A mono-isocenter plan delivered in clinical practice has then been retrospectively re-planned with dual-isocenter technique. The dual-isocenter plan has been re-calculated and delivered on a 3-dimensional (3D) ArcCHECK phantom and 2-dimensional (2D) films to assess its dosimetric accuracy in terms of gamma analysis. Clinical and planning target volume (CTV and PTV respectively) coverage robustness was then investigated after the introduction of ± 2 mm and ± 5 mm positioning errors by shifting the couch.

RESULTS

MLC and couch accuracy tests confirmed the system accuracy in delivering a dual-isocenter irradiation. 2D/3D gamma analysis results occurred always to be above 95% if considered a gamma criteria 1%/2 mm and 1%/1 mm respectively for the 2D and 3D analysis. The mean variations for CTV D98% and PTV V95% were 0.2% and 1.1% respectively when positioning error was introduced separately in each direction, while the maximum observed variations were 0.9% (CTV) and 3.7% (PTV).

CONCLUSION

The dosimetric accuracy of dual-isocenter irradiation has been verified for MR-Linac, achieving accurate and robust treatment strategy and improving dose conformality also in presence of targets whose extension exceeds the nominal maximum field size.

Radiation therapy technologists' involvement and opinion in research: A national survey in Italy

Introduction

This survey describes Italian RTTs' involvement and opinions in research activities related to radiation oncology. Primary aim was to assess the degree of involvement of the national RTTs community in research and to describe how RTTs can integrate their skills collaborating with other professionals.

Materials and methods

A ten-items multiple-choice questionnaire, with 2-8 possible responses, was developed by a steering committee and generated on a survey platform. Links were sent via email to Italian RTTs.The questions were divided in 3 domains: demographic data; scientific research and activity; opinions about RTTs role in scientific research. The survey started on October 1, 2018 and ended on January 31, 2019.

Results

One hundred thirty-five out of 509 (26.5%) RTTs responded to the questionnaire at its expiring date; 97.73% think to be valid contributors in radiation oncology research, expressing clear interest in "data collection" tasks (52.71%); 38.64% feel unsupported by other professionals in the research team and 59.85% of the respondents are not members in any scientific society.

Conclusions

The role of Italian RTTs in research is heterogeneous. Mainly RTTs in the age range from 30 to 40 years responded to the survey showing their interest in scientific research. This might be related to different informatics and educational skills as well as to personal attitudes. RTTs particular skills, like data management and technical hypothesis generation abilities, are of benefit to realize research projects. Therefore, engaging RTTs in research activities is strongly suggested.