Our department has a long-established comprehensive quality assurance (QA) planning clinic for patients undergoing radiation therapy (RT) for head and neck cancer. Our aim is to assess the impact of ...a real-time peer review QA process on the quantitative and qualitative radiation therapy plan changes in the era of intensity modulated RT (IMRT).
Prospective data for 85 patients undergoing head and neck IMRT who presented at a biweekly QA clinic after simulation and contouring were collected. A standard data collection form was used to document alterations made during this process. The original pre-QA clinical target volumes (CTVs) approved by the treating-attending physicians were saved before QA and compared with post-QA consensus CTVs. Qualitative assessment was done according to predefined criteria. Dice similarity coefficients (DSC) and other volume overlap metrics were calculated for each CTV level and were used for quantitative comparison. Changes are categorized as major, minor, and trivial according to the degree of overlap. Patterns of failure were analyzed and correlated to plan changes.
All 85 patients were examined by at least 1 head and neck subspecialist radiation oncologist who was not the treating-attending physician; 80 (94%) were examined by ≥3 faculty members. New clinical findings on physical examination were found in 12 patients (14%) leading to major plan changes. Quantitative DSC analysis revealed significantly better agreement in CTV1 (0.94 ± 0.10) contours than in CTV2 (0.82 ± 0.25) and CTV3 (0.86 ± 0.2) contours (P=.0002 and P=.03, respectively; matched-pair Wilcoxon test). The experience of the treating-attending radiation oncologist significantly affected DSC values when all CTV levels were considered (P=.012; matched-pair Wilcoxon text). After a median follow-up time of 38 months, only 10 patients (12%) had local recurrence, regional recurrence, or both, mostly in central high-dose areas.
Comprehensive peer review planning clinic is an essential component of IMRT QA that led to major changes in one-third of the study population. This process ensured safety related to target definition and led to favorable disease control profiles, with no identifiable recurrences attributable to geometric misses or delineation errors.
High-field magnetic resonance-linear accelerators (MR-Linacs), linear accelerators combined with a diagnostic magnetic resonance imaging (MRI) scanner and online adaptive workflow, potentially give ...rise to novel online anatomic and response adaptive radiation therapy paradigms. The first high-field (1.5T) MR-Linac received regulatory approval in late 2018, and little is known about clinical use, patient tolerability of daily high-field MRI, and toxicity of treatments. Herein we report the initial experience within the MOMENTUM Study (NCT04075305), a prospective international registry of the MR-Linac Consortium.
Patients were included between February 2019 and October 2020 at 7 institutions in 4 countries. We used descriptive statistics to describe the patterns of care, tolerability (the percentage of patients discontinuing their course early), and safety (grade 3-5 Common Terminology Criteria for Adverse Events v.5 acute toxicity within 3 months after the end of treatment).
A total 943 patients participated in the MOMENTUM Study, 702 of whom had complete baseline data at the time of this analysis. Patients were primarily male (79%) with a median age of 68 years (range, 22-93) and were treated for 39 different indications. The most frequent indications were prostate (40%), oligometastatic lymph node (17%), brain (12%), and rectal (10%) cancers. The median number of fractions was 5 (range, 1-35). Six patients discontinued MR-Linac treatments, but none due to an inability to tolerate repeated high-field MRI. Of the 415 patients with complete data on acute toxicity at 3-month follow-up, 18 (4%) patients experienced grade 3 acute toxicity related to radiation. No grade 4 or 5 acute toxicity related to radiation was observed.
In the first 21 months of our study, patterns of care were diverse with respect to clinical utilization, body sites, and radiation prescriptions. No patient discontinued treatment due to inability to tolerate daily high-field MRI scans, and the acute radiation toxicity experience was encouraging.
In the context of clinical oncology, a fundamental goal of radiomics is the extraction of large amounts of quantitative features whose subsequent analysis can be used for decision support towards ...personalized and actionable cancer care. Head and neck cancers present a unique set of diagnostic and therapeutic challenges by nature of its complex anatomy and heterogeneity. Radiomics holds the potential to address these barriers, but only if as a collective field we direct future effort towards investigating specific oncologic function and oncologic outcomes, with external validation and collaborative multi-institutional efforts to begin standardizing and refining radiomic signatures. Here we present an overview of radiomic texture analysis methods as well as the software infrastructure, review the developments of radiomics in head and neck cancer applications, discuss unmet challenges, and propose key recommendations for moving the field forward.
Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral ...carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.
Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where ...detailed knowledge of the pathways of microscopic tumor spread is necessary. This paper proposes a solution to auto-segment these volumes in oropharyngeal cancer patients using a two-channel 3D U-Net architecture. The first channel feeds the network with the patient's CT image providing anatomical context, whereas the second channel provides the network with tumor location and morphological information. Radiation therapy simulation computer tomography scans and their corresponding manually delineated CTV and gross tumor volume (GTV) delineations from 285 oropharyngeal patients previously treated at MD Anderson Cancer Center were used in this study. CTV and GTV delineations underwent rigorous group peer-review prior to the start of treatment delivery. The convolutional network's parameters were fine-tuned using a training set of 210 patients using 3-fold cross-validation. During hyper-parameter selection, we use a score based on the overlap (dice similarity coefficient (DSC)) and missed volumes (false negative dice (FND)) to minimize any possible under-treatment. Three auto-delineated models were created to estimate tight, moderate, and wide CTV margin delineations. Predictions on our test set (75 patients) resulted in auto-delineations with high overlap and close surface distance agreement (DSC > 0.75 on 96% of cases for tight and moderate auto-delineation models and 97% of cases having mean surface distance 5.0 mm) to the ground-truth. We found that applying a 5 mm uniform margin expansion to the auto-delineated CTVs would cover at least 90% of the physician CTV volumes for a large majority of patients; however, determination of appropriate margin expansions for auto-delineated CTVs merits further investigation.
Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed ...tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR‐guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time‐consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.
Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images ...obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.
Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.
Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies Spearman's ρ = 0.94 (
< 0.0001).
Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We ...hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV
). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.
To determine whether apparent diffusion coefficient (ADC) value is predictive of survival after definitive chemoradiation for cervical cancer independent of established imaging and clinical ...prognostic factors.
Between 2011 and 2013, the pretreatment MRI scans for 69 patients treated with definitive chemoradiation for newly diagnosed cervical cancer were retrieved. Scans were acquired with a 1.5-T magnetic resonance scanner, including diffusion-weighted imaging sequences. Mean ADC value was measured within a region of interest in the primary cervical cancer on the baseline MRI scan. Baseline tumor maximum standardized uptake value on positron emission tomography/computed tomography was determined by the reading radiologist. Treatment included external beam radiation therapy to the pelvis followed by brachytherapy in 97%, and with concurrent weekly cisplatin in 99% of patients. Univariate and multivariate analyses were done to investigate the association of clinical and imaging variables with disease control and survival endpoints using a Cox proportional hazard test.
Median follow-up was 16.7 months (range, 3.1-44.2 months). The 1-year overall survival, locoregional recurrence-free survival, and disease-free survival rates were 91%, 86%, and 74%, respectively. The median ADC value was 0.941 × 10
mm
/s (range, 0.256-1.508 × 10
mm
/s). The median standardized uptake value in the primary tumor was 15 (range, 6.2-43.4). In multivariate analysis, higher ADC value (hazard ratio HR 0.36, 95% confidence interval CI 0.15-0.85, P=.02), higher stage (HR 2.4, 95% CI 1.1-5.5, P=.033), and nonsquamous histology (HR 0.23, 95% CI 0.07-0.82, P=.024) were independent predictors of disease-free survival.
The mean ADC value of the primary tumor on pretreatment MRI was the only imaging feature that was an independent predictor of disease-free survival in cervical cancer patients treated with chemoradiation. Further validation will be needed to determine whether ADC values may prove useful in identifying cervical patients at high risk of recurrence.
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