Improved dosimetric accuracy with semi-automatic contour propagation of organs-at-risk in glioblastoma patients undergoing chemoradiation

Evolving concepts in margin strategies and adaptive radiotherapy for glioblastoma: A new future is on the horizon

Chemoradiotherapy is the standard treatment after maximal safe resection for glioblastoma (GBM). Despite advances in molecular profiling, surgical techniques, and neuro-imaging, there have been no major breakthroughs in radiotherapy (RT) volumes in decades. Although the majority of recurrences occur within the original gross tumor volume (GTV), treatment of a clinical target volume (CTV) ranging from 1.5 to 3.0 cm beyond the GTV remains the standard of care. Over the past 15 years, the incorporation of standard and functional MRI sequences into the treatment workflow has become a routine practice with increasing adoption of MR simulators, and new integrated MR-Linac technologies allowing for daily pre-, intra- and post-treatment MR imaging. There is now unprecedented ability to understand the tumor dynamics and biology of GBM during RT, and safe CTV margin reduction is being investigated with the goal of improving the therapeutic ratio. The purpose of this review is to discuss margin strategies and the potential for adaptive RT for GBM, with a focus on the challenges and opportunities associated with both online and offline adaptive workflows. Lastly, opportunities to biologically guide adaptive RT using non-invasive imaging biomarkers and the potential to define appropriate volumes for dose modification will be discussed.

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Segmentation of post-operative glioblastoma multiforme (GBM) is essential for the planning of Tumor Treating Fields (TTFields) treatment and other clinical applications. Recent methods developed for pre-operative GBM segmentation perform poorly on post-operative GBM MRI scans. In this paper we present a method for the segmentation of GBM in post-operative patients. Our method incorporates an ensemble of segmentation networks and the Kullback–Leibler divergence agreement score in the objective function to estimate the prediction label uncertainty and cope with noisy labels and inter-observer variability. Moreover, our method integrates the surgery type and computes non-tumorous tissue delineation to automatically segment the tumor. We trained and validated our method on a dataset of 340 enhanced T1 MRI scans of patients that were treated with TTFields (270 scans for train and 70 scans for test). For validation, we developed a tool that uses the uncertainty map along with the segmentation result. Our tool allows visualization and fast editing of the tissues to improve the results dependent on user preference. Three physicians reviewed and graded our segmentation and editing tool on 12 different MRI scans. The validation set average (SD) Dice scores were 0.81 (0.11), 0.71 (0.24), 0.64 (0.25), and 0.68 (0.19) for whole-tumor, resection, necrotic-core, and enhancing-tissue, respectively. The physicians rated 72% of the segmented GBMs acceptable for treatment planning or better. Another 22% can be edited manually in a reasonable time to achieve a clinically acceptable result. According to these results, the proposed method for GBM segmentation can be integrated into TTFields treatment planning software in order to shorten the planning process. To conclude, we have extended a state-of-the-art pre-operative GBM segmentation method with surgery-type, anatomical information, and uncertainty visualization to facilitate a clinically viable segmentation of post-operative GBM for TTFields treatment planning.

Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies

PURPOSE

The performance of deformable medical image registration (DIR) algorithms has become a major concern.

METHODS

We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines.

RESULTS

One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed.

CONCLUSIONS

This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.

The Promise of Magnetic Resonance Imaging in Radiation Oncology Practice in the Management of Brain, Prostate, and GI Malignancies

Magnetic resonance imaging (MRI) has a key role to play at multiple steps of the radiotherapy (RT) treatment planning and delivery process. Development of high-precision RT techniques such as intensity-modulated RT, stereotactic ablative RT, and particle beam therapy has enabled oncologists to escalate RT dose to the target while restricting doses to organs at risk (OAR). MRI plays a critical role in target volume delineation in various disease sites, thus ensuring that these high-precision techniques can be safely implemented. Accurate identification of gross disease has also enabled selective dose escalation as a means to widen the therapeutic index. Morphological and functional MRI sequences have also facilitated an understanding of temporal changes in target volumes and OAR during a course of RT, allowing for midtreatment volumetric and biological adaptation. The latest advancement in linear accelerator technology has led to the incorporation of an MRI scanner in the treatment unit. MRI-guided RT provides the opportunity for MRI-only workflow along with online adaptation for either target or OAR or both. MRI plays a key role in post-treatment response evaluation and is an important tool for guiding decision making. In this review, we briefly discuss the RT-related applications of MRI in the management of brain, prostate, and GI malignancies.

Pattern of Recurrence of Glioblastoma Versus Grade 4 IDH-Mutant Astrocytoma Following Chemoradiation: A Retrospective Matched-Cohort Analysis

Background and Purpose: To quantitatively compare the recurrence patterns of glioblastoma (isocitrate dehydrogenase-wild type) versus grade 4 isocitrate dehydrogenase-mutant astrocytoma (wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase, respectively) following primary chemoradiation. Materials and Methods: A retrospective matched cohort of 22 wild type isocitrate dehydrogenase and 22 mutant isocitrate dehydrogenase patients were matched by sex, extent of resection, and corpus callosum involvement. The recurrent gross tumor volume was compared to the original gross tumor volume and clinical target volume contours from radiotherapy planning. Failure patterns were quantified by the incidence and volume of the recurrent gross tumor volume outside the gross tumor volume and clinical target volume, and positional differences of the recurrent gross tumor volume centroid from the gross tumor volume and clinical target volume. Results: The gross tumor volume was smaller for wild type isocitrate dehydrogenase patients compared to the mutant isocitrate dehydrogenase cohort (mean ± SD: 46.5 ± 26.0 cm³ vs 72.2 ± 45.4 cm³, P = .026). The recurrent gross tumor volume was 10.7 ± 26.9 cm³ and 46.9 ± 55.0 cm³ smaller than the gross tumor volume for the same groups (P = .018). The recurrent gross tumor volume extended outside the gross tumor volume in 22 (100%) and 15 (68%) (P= .009) of wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase patients, respectively; however, the volume of recurrent gross tumor volume outside the gross tumor volume was not significantly different (12.4 ± 16.1 cm³ vs 8.4 ± 14.2 cm³, P = .443). The recurrent gross tumor volume centroid was within 5.7 mm of the closest gross tumor volume edge for 21 (95%) and 22 (100%) of wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase patients, respectively. Conclusion: The recurrent gross tumor volume extended beyond the gross tumor volume less often in mutant isocitrate dehydrogenase patients possibly implying a differential response to chemoradiotherapy and suggesting isocitrate dehydrogenase status might be used to personalize radiotherapy. The results require validation in prospective randomized trials.

Quantitating Interfraction Target Dynamics During Concurrent Chemoradiation for Glioblastoma: A Prospective Serial Imaging Study

PURPOSE

Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study quantifies interfraction dynamics (tumor size, position, and geometry) based on sequential magnetic resonance imaging scans obtained during standard 6-week chemoradiation.

METHODS AND MATERIALS

Sixty-one patients were prospectively imaged with gadolinium-enhanced T1 (T1c) and T2/FLAIR axial sequences at planning (Fx0), fraction 10 (Fx10), fraction 20 (Fx20), and 1 month after the final fraction of chemoradiation therapy (P1M). Gross tumor volumes (GTVs) and clinical target volumes (CTVs) were contoured at all time points. Target dynamics were quantified by absolute volume (V), volume relative to Fx0 (V_rel), and the migration distance (d_migrate; the linear displacement of the GTV or CTV relative to Fx0). Temporal changes were assessed using a linear mixed-effects model.

RESULTS

Median volumes at Fx0, Fx10, Fx20, and P1M for the GTV were 18.4 cm³ (range, 1.1-110.5 cm³), 14.7 cm³ (range, 0.9-115.1 cm³), 13.7 cm³ (range, 0.6-174.2 cm³), and 13.0 cm³ (range, 0.9-76.3 cm³), respectively, with corresponding median V_rel of 0.88 at Fx10, 0.77 at Fx20, and 0.71 at P1M relative to Fx0 (P < .001 for all). The GTV (CTV) migration distances were greater than 5 mm in 46% (54%) of patients at Fx10, 50% (58%) of patients at Fx20, and 52% (57%) of patients at P1M. Dynamic tumor morphologic changes were observed, with 40% of patients exhibiting a decreased GTV (V_rel ≤1) with a d_migrate >5 mm during chemoradiation therapy.

CONCLUSIONS

Clinically meaningful tumor dynamics were observed during chemoradiation therapy for glioblastoma, supporting evaluation of daily MRI guided radiation therapy and treatment plan adaptation.

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.