Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience

Palhares, Daniel Moore Freitas; Ho, Lee Kwang; Lü, Lin; Chugh, Brige; Vesprini, Danny; Karam, Irene; Soliman, Hany; Symons, Sean; Leung, Eric; Loblaw, Andrew; Myrehaug, Sten; Stanisz, Greg J.; Sahgal, Arjun; Czarnota, Gregory J.

doi:10.1186/s13014-023-02209-4

Cited by 21 publications

(9 citation statements)

References 39 publications

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“…Reassuringly, the inter-observer variability in urethra contouring on MRI in our study (mean dice similarity coefficient of 0.61) is not dissimilar to prior studies (mean dice similarity coefficient of 0.61) [22] , suggesting our findings are not due to ‘poorer’ urethra contouring on MRI among the observers. However, as we move towards MRI only workflow [25] or treatment on MR-Linac (i.e., Linac with on-board MRI) [26] , a planning CT may no longer be performed as part of prostate SBRT planning. In this situation, additional urethra-optimized MRI sequences (in addition to the standard T2-weighted MRI used for prostate contouring) will be required to better visualize the urethra.…”

Section: Discussionmentioning

confidence: 99%

Urethra contouring on computed tomography urethrogram versus magnetic resonance imaging for stereotactic body radiotherapy in prostate cancer

Ong,

Allan Hupman,

Davidson

et al. 2024

Clinical and Translational Radiation Oncology

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Section: Discussionmentioning

confidence: 99%

Urethra contouring on computed tomography urethrogram versus magnetic resonance imaging for stereotactic body radiotherapy in prostate cancer

Ong,

Allan Hupman,

Davidson

et al. 2024

Clinical and Translational Radiation Oncology

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“…Radiotherapy and MRgFUS-MB treatment were conducted on an outpatient basis. The radiation simulation process included computed tomography for all patients, with the possibility of additional MR imaging simulation [ 23 ] for enhanced contouring, determined at the discretion of the treating physician. The target tumour was delineated prospectively using all the available imaging modalities to guide treatment planning.…”

Section: Methodsmentioning

confidence: 99%

Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial

Moore-Palhares,

Dasgupta,

Saifuddin

et al. 2024

PLoS Med

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Background Preclinical studies have demonstrated that tumour cell death can be enhanced 10- to 40-fold when radiotherapy is combined with focussed ultrasound-stimulated microbubble (FUS-MB) treatment. The acoustic exposure of microbubbles (intravascular gas microspheres) within the target volume causes bubble cavitation, which induces perturbation of tumour vasculature and activates endothelial cell apoptotic pathways responsible for the ablative effect of stereotactic body radiotherapy. Subsequent irradiation of a microbubble-sensitised tumour causes rapid increased tumour death. The study here presents the mature safety and efficacy outcomes of magnetic resonance (MR)-guided FUS-MB (MRgFUS-MB) treatment, a radioenhancement therapy for breast cancer. Methods and findings This prospective, single-center, single-arm Phase 1 clinical trial included patients with stages I–IV breast cancer with in situ tumours for whom breast or chest wall radiotherapy was deemed adequate by a multidisciplinary team (clinicaltrials.gov identifier: NCT04431674). Patients were excluded if they had contraindications for contrast-enhanced MR or microbubble administration. Patients underwent 2 to 3 MRgFUS-MB treatments throughout radiotherapy. An MR-coupled focussed ultrasound device operating at 800 kHz and 570 kPa peak negative pressure was used to sonicate intravenously administrated microbubbles within the MR-guided target volume. The primary outcome was acute toxicity per Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Secondary outcomes were tumour response at 3 months and local control (LC). A total of 21 female patients presenting with 23 primary breast tumours were enrolled and allocated to intervention between August/2020 and November/2022. Three patients subsequently withdrew consent and, therefore, 18 patients with 20 tumours were included in the safety and LC analyses. Two patients died due to progressive metastatic disease before 3 months following treatment completion and were excluded from the tumour response analysis. The prescribed radiation doses were 20 Gy/5 fractions (40%, n = 8/20), 30 to 35 Gy/5 fractions (35%, n = 7/20), 30 to 40 Gy/10 fractions (15%, n = 3/20), and 66 Gy/33 fractions (10%, n = 2/20). The median follow-up was 9 months (range, 0.3 to 29). Radiation dermatitis was the most common acute toxicity (Grade 1 in 16/20, Grade 2 in 1/20, and Grade 3 in 2/20). One patient developed grade 1 allergic reaction possibly related to microbubbles administration. At 3 months, 18 tumours were evaluated for response: 9 exhibited complete response (50%, n = 9/18), 6 partial response (33%, n = 6/18), 2 stable disease (11%, n = 2/18), and 1 progressive disease (6%, n = 1/18). Further follow-up of responses indicated that the 6-, 12-, and 24-month LC rates were 94% (95% confidence interval [CI] [84%, 100%]), 88% (95% CI [75%, 100%]), and 76% (95% CI [54%, 100%]), respectively. The study’s limitations include variable tumour sizes and dose fractionation regimens and the anticipated small sample size typical for a Phase 1 clinical trial. Conclusions MRgFUS-MB is an innovative radioenhancement therapy associated with a safe profile, potentially promising responses, and durable LC. These results warrant validation in Phase 2 clinical trials. Trial registration clinicaltrials.gov, identifier NCT04431674.

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“…This constitutes an analysis of a prospective cohort of patients with biopsy-proven de novo cT0-4N1-3M0 primary H&N cancer who underwent a standard course of upfront radiotherapy with 70 Gy in 33-35 fractions, with or without concurrent chemotherapy, between 2015 and 2020 (ClinicalTrials.gov (accessed on 11 June 2024), identifier: NCT03908684). For the purpose of this current study, we included a subgroup of patients from that cohort who had enlarged regional lymph nodes measuring ≥15 mm in short axis, as assessed by CT scan, and underwent baseline magnetic resonance imaging at the time of diagnosis or radiation planning [30], while excluding patients whose MRI contained motion and dental artifacts.…”

Section: Methodsmentioning

confidence: 99%

“…Contouring and treatment plans were made in line with institutional guidelines and in accordance with standard practice. As part of patient clinical care for radiation treatment planning, the primary and lymph node gross tumor volumes (GTV) were contoured by a clinical team on planning CT scans, co-registered with diagnostic or planning magnetic resonance imaging data that were acquired from a 1.5 T Philips Ingenia Elition X (Philips Medical Systems, Amsterdam, Netherlands) device [30]. The typical contouring approach consisted of (i) expanding the GTV by 3-5 mm to generate the high-dose clinical target volume (CTV) and further expanding it by 5 mm to generate the high-dose planning target volume (PTV); (ii) expanding the GTV by 10 mm and including the elective nodal regions to generate the low-dose CTV.…”

Section: Treatment Approach and Follow-up Imagingmentioning

confidence: 99%

Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers

Safakish,

Moslemi,

Moore-Palhares

et al. 2024

Radiation

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Background: Head and neck cancer treatment does not yield desired outcomes for all patients. This investigation aimed to explore the feasibility of predicting treatment outcomes from routine pre-treatment magnetic resonance images (MRIs). Radiomics features were “mined” and used to train machine learning (ML) classifiers to predict treatment outcomes. Moreover, iterative deep texture analysis (DTA) was explored to boost model performances. Methods: Radiomics features were determined from T1-weighted post-contrast MRIs of pathologically involved lymph node (LN) segmentations for n = 63 patients. SVM, k-NN, and FLD classifier models were trained, selecting for 1–10 features. The model with the top balanced accuracy was chosen for an iteration of DTA. New feature sets were used to retrain and test the ML. Radiomics features were explored for a total of three layers through two iterations of DTA. Results: Models proved useful in predicting treatment outcomes. The best model was a nine-feature multivariable k-NN model with a sensitivity (%Sn) of 93%, specificity (%Sp) of 74%, 86% accuracy (%Acc), and 86% precision (%Per). The best model for two of the three classifiers (k-NN and FLD) was trained using features from three layers. The performance of the average k-NN and FLD models trained with features was boosted significantly with the inclusion of deeper-layer features. Conclusions: Pre-treatment LN MRIs contain quantifiable texture information that can be used to train ML models to predict cancer treatment outcomes. Furthermore, DTA proved useful to boosting predictive models.

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Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience

Cited by 21 publications

References 39 publications

Urethra contouring on computed tomography urethrogram versus magnetic resonance imaging for stereotactic body radiotherapy in prostate cancer

Urethra contouring on computed tomography urethrogram versus magnetic resonance imaging for stereotactic body radiotherapy in prostate cancer

Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial

Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers

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