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Register a new userRadiotherapy Effects on Diffuse Low-Grade Gliomas: Confronting Theory With Clinical Data
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Added by
Stephane Plaszczynski
Publication date
2021
fields
Physics
and research's language is
English
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Diffuse low grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the tumor radius evolution before and after the radiotherapy process and propose a novel model that is simple, yet biologically motivated, and that remedies some shortcomings of previously proposed ones. We confront it with clinical data consisting in time-series of tumor radius for 43 patient records, using a stochastic optimization technique and obtain very good fits in all the cases. Since our model describes the evolution of the tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile-likelihood to extract all the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade glioma seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data.
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Objectives: Glioblastomas are the most aggressive brain and central nervous system (CNS) tumors with poor prognosis in adults. The purpose of this study is to develop a machine-learning based classification method using radio-mic features of multi-parametric MRI to classify high-grade gliomas (HGG) and low-grade gliomas (LGG). Methods: Multi-parametric MRI of 80 patients, 40 HGG and 40 LGG, with gliomas from the MICCAI BRATs 2015 training database were used in this study. Each patients T1, contrast-enhanced T1, T2, and Fluid Attenuated Inversion Recovery (FLAIR) MRIs as well as the tumor contours were provided in the database. Using the given contours, radiomic features from all four multi-parametric MRIs were extracted. Of these features, a feature selection process using two-sample T-test and least absolute shrinkage, selection operator (LASSO), and a feature correlation threshold was applied to various combinations of T1, contrast-enhanced T1, T2, and FLAIR MRIs separately. These selected features were then used to train, test, and cross-validate a random forest to differentiate HGG and LGG. Finally, the classification accuracy and area under the curve (AUC) were used to evaluate the classification method. Results: Optimized parameters showed that on average, the overall accuracy of our classification method was 0.913 or 73 out of 80 correct classifications, 36/40 for HGG and 37/40 for LGG, with an AUC of 0.956 based on the combination with FLAIR, T1, T1c and T2 MRIs. Conclusion: This study shows that radio-mic features derived from multi-parametric MRI could be used to accurately classify high and lower grade gliomas. The radio-mic features from multi-parametric MRI in combination with even more advanced machine learning methods may further elucidate the underlying tumor biology and response to therapy.
Radiotherapy is often the most straightforward first line cancer treatment for solid tumors. While it is highly effective against tumors, there is also collateral damage to healthy proximal tissues especially with high doses. The use of radiosensitizers is an effective way to boost the killing efficacy of radiotherapy against the tumor while drastically limiting the received dose and reducing the possible damage to normal tissues. Here, we report the design and application of a good radiosensitizer by using ultrasmall gold nanoclusters with a naturally occurring peptide (e.g., glutathione or GSH) as the protecting shell. The GSH coated gold nanoclusters can escape the RES absorption, leading to a good tumor uptake (8.1% ID/g at 24 h post injection). As a result, the as-designed Au nanoclusters led to a strong enhancement for radiotherapy, as well as a negligible damage to normal tissues. After the treatment, the ultrasmall gold nanoclusters can be efficiently cleared by the kidney, thereby avoiding potential long term side effects caused by the accumulation of gold atoms in the body. Our data suggest that the ultrasmall peptide protected Au nanoclusters are a promising radiosensitizer for cancer radiotherapy.
Purpose: A Monte Carlo (MC) beam model and its implementation in a clinical treatment planning system (TPS, Varian Eclipse) are presented for a modified ultra-high dose-rate electron FLASH radiotherapy (eFLASH-RT) LINAC. Methods: The gantry head without scattering foils or targets, representative of the LINAC modifications, was modelled in Geant4. The energy spectrum ({sigma}E) and beam source emittance cone angle ({theta}cone) were varied to match the calculated and Gafchromic film measured central-axis percent depth dose (PDD) and lateral profiles. Its Eclipse configuration was validated with measured profiles of the open field and nominal fields for clinical applicators. eFLASH-RT plans were MC forward calculated in Geant4 for a mouse brain treatment and compared to a conventional (Conv-RT) plan in Eclipse for a human patient with metastatic renal cell carcinoma. Results: The beam model and its Eclipse configuration agreed best with measurements at {sigma}E=0.5 MeV and {theta}cone=3.9+/-0.2 degrees to clinically acceptable accuracy (the absolute average error was within 1.5% for in-water lateral, 3% for in-air lateral, and 2% for PDD). The forward dose calculation showed dose was delivered to the entire mouse brain with adequate conformality. The human patient case demonstrated the planning capability with routine accessories in relatively complex geometry to achieve an acceptable plan (90% of the tumor volume receiving 95% and 90% of the prescribed dose for eFLASH and Conv-RT, respectively). Conclusion: To the best of our knowledge, this is the first functional beam model commissioned in a clinical TPS for eFLASH-RT, enabling planning and evaluation with minimal deviation from Conv-RT workflow. It facilitates the clinical translation as eFLASH-RT and Conv-RT plan quality were comparable for a human patient. The methods can be expanded to model other eFLASH irradiators.
Non-specific chronic low back pain (NSCLBP) is a major health problem, affecting about one fifth of the population worldwide. To avoid further pain or injury, patients with NSCLBP seem to adopt a stiffer movement pattern during everyday living activities. However, it remains unknown how NSCLBP affects the lumbar lordosis angle (LLA) during repetitive activities such as walking or running. This pilot study therefore aimed at exploring possible NSCLBP-related alterations in LLAs during walking and running by focusing on discrete parameters as well as continuous data. Thirteen patients with NSCLBP and 20 healthy pain-free controls were enrolled and underwent a full-body movement analysis involving various everyday living activities such as standing, walking and running. LLAs were derived from markers placed on the spinous processes of the vertebrae L1-L5 and S1. Possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs were analyzed descriptively using mean differences with confidence intervals ranging from 95% to 75%. Patients with NSCLBP indicated reduced average LLAs during standing, walking and running and a tendency for lower LLA-ROM during walking. Analyses of continuous data indicated the largest group differences occurring around 25% and 70% of the walking and 25% and 75% of the running cycle. Furthermore, patients indicated a reversed movement pattern during running, with increasing instead of a decreasing LLAs after foot strike. This study provides preliminary evidence that NSCLBP might affect LLAs during walking and running. These results can be used as a basis for future large-scale investigations involving hypothesis testing.
Monte Carlo (MC) methods provide the most accurate to-date dose calculations in heterogeneous media and complex geometries, and this spawns increasing interest in incorporating MC calculations into treatment planning quality assurance process. This involves MC dose calculations for clinically produced treatment plans. To perform these calculations, a number of treatment plan parameters specifying radiation beam and patient geometries need to be transferred to MC codes, such as BEAMnrc and DOSXYZnrc. Extracting these parameters from DICOM files is not a trivial task, one that has previously been performed mostly using Matlab-based software. This paper describes the DICOM tags that contain information required for MC modeling of conformal and IMRT plans, and reports the development of an in-house DICOM interface, through a library (named Vega) of platform-independent, object-oriented C++ codes. The Vega library is small and succinct, offering just the fundamental functions for reading/modifying/writing DICOM files in a C++ program. The library, however, is flexible enough to extract all MC required data from DICOM files, and write MC produced dose distributions into DICOM files that can then be processed in a treatment planning system environment. The library can be made available upon request to the authors.
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