Impact of clinical target volume margin reduction in glioblastoma patients treated with concurrent chemoradiation.

Background: Glioblastoma (GBM) is widely treated using large radiotherapy margins, resulting in substantial irradiation of the surrounding cerebral structures. In this context, the question arises whether these margins could be safely reduced. In 2018, clinical target volume (CTV) expansion was reduced in our institution from 20 to 15 mm around the gross target volume (GTV) (ie, the contrast-enhancing tumor/cavity). We sought to retrospectively analyze the impact of this reduction. Methods: All adult patients with GBM treated between January 2015 and December 2020 with concurrent chemoradiation (60Gy/2Gy or 59.4Gy/1.8Gy) were analyzed. Patients treated using a 20 (CTV20, n = 57) or 15 mm (CTV15, n = 56) CTV margin were compared for target volumes, dose parameters to the surrounding organs, pattern of recurrence, and survival outcome. Results: Mean GTV was similar in both groups (ie, CTV20: 39.7cm3; CTV15: 37.8cm3; P = .71). Mean CTV and PTV were reduced from 238.9cm3 to 176.7cm3 (P = .001) and from 292.6cm3 to 217.0cm3 (P < .001), for CTV20 and CTV15, respectively. As a result, average brain mean dose (Dmean) was reduced from 25.2Gy to 21.0Gy (P = .002). Significantly lower values were also observed for left hippocampus Dmean, brainstem D0.03cc, cochleas Dmean, and pituitary Dmean. Pattern of recurrence was similar, as well as patient outcome, ie, median progression-free survival was 8.0 and 7.0 months (P = .80), and median overall survival was 11.0 and 14.0 months (P = .61) for CTV20 and CTV15, respectively. Conclusions: In GBM patients treated with chemoradiation, reducing the CTV margin from 20 to 15 mm appears to be safe and offers the potential for less treatment toxicity.

The existence of cranial bone flap displacement during brain radiotherapy.

This retrospective study examined bone flap displacement during radiotherapy in 25 post-operative brain tumour patients. Though never exceeding 2.5 mm, the sheer frequency of displacement highlights the need for future research on larger populations to validate its presence and assess the potential clinical impact on planning tumour volume margins.

Skull-Base Chondrosarcoma: A Systematic Review of the Role of Postoperative Radiotherapy.

Surgery and radiotherapy are key elements to the treatment of skull-base chondrosarcomas; however, there is currently no consensus regarding whether or not adjuvant radiotherapy has to be administered. This study searched the EMBASE, Cochrane, and PubMed databases for clinical studies evaluating the long-term prognosis of surgery with or without adjuvant radiotherapy. After reviewing the search results, a total of 22 articles were selected for this review. A total of 1388 patients were included in this cohort, of which 186 received surgery only. With mean follow-up periods ranging from 39.1 to 86 months, surgical treatment provided progression-free survival (PFS) rates ranging from 83.7 to 92.9% at 3 years, 60.0 to 92.9% at 5 years, and 58.2 to 64.0% at 10 years. Postoperative radiotherapy provides PFS rates ranging between 87 and 96.2% at 3 years, 57.1 and 100% at 5 years, and 67 and 100% at 10 years. Recurrence rates varied from 5.3% to 39.0% in the surgery-only approach and between 1.5% and 42.90% for the postoperative radiotherapy group. When considering prognostic variables, higher age, brainstem/optic apparatus compression, and larger tumor volume prior to radiotherapy were found to be significant factors for local recurrence.

Anatomical changes in resection cavity during brain radiotherapy.

BACKGROUND AND PURPOSE: Brain tumors are in general treated with a maximal safe resection followed by radiotherapy of remaining tumor including the resection cavity (RC) and chemotherapy. Anatomical changes of the RC during radiotherapy can have impact on the coverage of the target volume. The aim of the current study was to quantify the potential changes of the RC and to identify risk factors for RC changes. MATERIALS AND METHODS: Sixteen patients treated with pencil beam scanning proton therapy between October 2019 and April 2020 were retrospectively analyzed. The RC was delineated on pre-treatment computed tomography (CT) and magnetic resonance imaging, and weekly CT-scans during treatment. Isotropic expansions were applied to the pre-treatment RC (1-5 mm). The percentage of volume of the RC during treatment within the expanded pre-treatment volumes was quantified. Potential risk factors (volume of RC, time interval surgery-radiotherapy and relationship of RC to the ventricles) were evaluated using Spearman's rank correlation coefficient. RESULTS: The average variation in relative RC volume during treatment was 26.1% (SD 34.6%). An expansion of 4 mm was required to cover > 95% of the RC volume in > 90% of patients. There was a significant relationship between the absolute volume of the pre-treatment RC and the volume changes during treatment (Spearman's ρ = - 0.644; p = 0.007). CONCLUSION: RCs are dynamic after surgery. Potentially, an additional margin in brain cancer patients with an RC should be considered, to avoid insufficient target coverage. Future research on local recurrence patterns is recommended.

Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology.

PURPOSE: Atlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas. METHODS: Image and contouring data from 76 neuro-oncological patients were included. Two atlas-based models (CT-atlas and MR-atlas) and one DLC-model (MR-DLC) were created. Manual contours on registered CT-MR-images were used as ground-truth. Results were analyzed in terms of geometrical (volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), added path length (APL), and mean slice-wise Hausdorff distance (MSHD)) and dosimetrical accuracy. Distance-to-tumor analysis was performed to analyze to which extent the location of the OAR relative to planning target volume (PTV) has dosimetric impact, using Wilcoxon rank-sum tests. RESULTS: CT-atlas outperformed MR-atlas for 22/26 OARs. MR-DLC outperformed MR-atlas for all OARs. Highest median (95 %CI) vDSC and sDSC were found for the brainstem in MR-DLC: 0.92 (0.88-0.95) and 0.84 (0.77-0.89) respectively, as well as lowest MSHD: 0.27 (0.22-0.39)cm. Median dose differences (ΔD) were within ± 1 Gy for 24/26(92 %) OARs for all three models. Distance-to-tumor showed a significant correlation for ΔDmax,0.03cc-parameters when splitting the data in ≤ 4 cm and > 4 cm OAR-distance (p < 0.001). CONCLUSION: MR-based DLC and CT-based atlas-contouring enable high-quality segmentation. It was shown that a combination of both CT- and MR-autocontouring models results in the best quality.

Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients.

OBJECTIVE: Several studies have shown that dual-energy CT (DECT) can lead to improved accuracy for proton range estimation. This study investigated the clinical benefit of reduced range uncertainty, enabled by DECT, in robust optimisation for neuro-oncological patients. METHODS: DECT scans for 27 neuro-oncological patients were included. Commercial software was applied to create stopping-power ratio (SPR) maps based on the DECT scan. Two plans were robustly optimised on the SPR map, keeping the beam and plan settings identical to the clinical plan. One plan was robustly optimised and evaluated with a range uncertainty of 3% (as used clinically; denoted 3%-plan); the second plan applied a range uncertainty of 2% (2%-plan). Both plans were clinical acceptable and optimal. The dose-volume histogram parameters were compared between the two plans. Two experienced neuro-radiation oncologists determined the relevant dose difference for each organ-at-risk (OAR). Moreover, the OAR toxicity levels were assessed. RESULTS: For 24 patients, a dose reduction >0.5/1 Gy (relevant dose difference depending on the OAR) was seen in one or more OARs for the 2%-plan; e.g. for brainstem D0.03cc in 10 patients, and hippocampus D40% in 6 patients. Furthermore, 12 patients had a reduction in toxicity level for one or two OARs, showing a clear benefit for the patient. CONCLUSION: Robust optimisation with reduced range uncertainty allows for reduction of OAR toxicity, providing a rationale for clinical implementation. Based on these results, we have clinically introduced DECT-based proton treatment planning for neuro-oncological patients, accompanied with a reduced range uncertainty of 2%. ADVANCES IN KNOWLEDGE: This study shows the clinical benefit of range uncertainty reduction from 3% to 2% in robustly optimised proton plans. A dose reduction to one or more OARs was seen for 89% of the patients, and 44% of the patients had an expected toxicity level decrease.

Linear Energy Transfer and Relative Biological Effectiveness Investigation of Various Structures for a Cohort of Proton Patients With Brain Tumors.

Purpose: The current knowledge on biological effects associated with proton therapy is limited. Therefore, we investigated the distributions of dose, dose-averaged linear energy transfer (LETd), and the product between dose and LETd (DLETd) for a patient cohort treated with proton therapy. Different treatment planning system features and visualization tools were explored. Methods and Materials: For a cohort of 24 patients with brain tumors, the LETd, DLETd, and dose was calculated for a fixed relative biological effectiveness value and 2 variable models: plan-based and phenomenological. Dose threshold levels of 0, 5, and 20 Gy were imposed for LETd visualization. The relationship between physical dose and LETd and the frequency of LETd hotspots were investigated. Results: The phenomenological relative biological effectiveness model presented consistently higher dose values. For lower dose thresholds, the LETd distribution was steered toward higher values related to low treatment doses. Differences up to 26.0% were found according to the threshold. Maximum LETd values were identified in the brain, periventricular space, and ventricles. An inverse relationship between LETd and dose was observed. Frequency information to the domain of dose and LETd allowed for the identification of clusters, which steer the mean LETd values, and the identification of higher, but sparse, LETd values. Conclusions: Identifying, quantifying, and recording LET distributions in a standardized fashion is necessary, because concern exists over a link between toxicity and LET hotspots. Visualizing DLETd or dose × LETd during treatment planning could allow for clinicians to make informed decisions.

Neurocognition in adults with intracranial tumors: does location really matter?

OBJECTIVE: As preservation of cognitive functioning increasingly becomes important in the light of ameliorated survival after intracranial tumor treatments, identification of eloquent brain areas would enable optimization of these treatments. METHODS: This cohort study enrolled adult intracranial tumor patients who received neuropsychological assessments pre-irradiation, estimating processing speed, verbal fluency and memory. Anatomical magnetic resonance imaging scans were used for multivariate voxel-wise lesion-symptom predictions of the test scores (corrected for age, gender, educational level, histological subtype, surgery, and tumor volume). Potential effects of histological and molecular subtype and corresponding WHO grades on the risk of cognitive impairment were investigated using Chi square tests. P-values were adjusted for multiple comparisons (p < .001 and p < .05 for voxel- and cluster-level, resp.). RESULTS: A cohort of 179 intracranial tumor patients was included [aged 19-85 years, median age (SD) = 58.46 (14.62), 50% females]. In this cohort, test-specific impairment was detected in 20-30% of patients. Higher WHO grade was associated with lower processing speed, cognitive flexibility and delayed memory in gliomas, while no acute surgery-effects were found. No grading, nor surgery effects were found in meningiomas. The voxel-wise analyses showed that tumor locations in left temporal areas and right temporo-parietal areas were related to verbal memory and processing speed, respectively. INTERPRETATION: Patients with intracranial tumors affecting the left temporal areas and right temporo-parietal areas might specifically be vulnerable for lower verbal memory and processing speed. These specific patients at-risk might benefit from early-stage interventions. Furthermore, based on future validation studies, imaging-informed surgical and radiotherapy planning could further be improved.

The Use of 18F-FET-PET-MRI in Neuro-Oncology: The Best of Both Worlds-A Narrative Review.

Gliomas are the most frequent primary tumors of the brain. They can be divided into grade II-IV astrocytomas and grade II-III oligodendrogliomas, based on their histomolecular profile. The prognosis and treatment is highly dependent on grade and well-identified prognostic and/or predictive molecular markers. Multi-parametric MRI, including diffusion weighted imaging, perfusion, and MR spectroscopy, showed increasing value in the non-invasive characterization of specific molecular subsets of gliomas. Radiolabeled amino-acid analogues, such as 18F-FET, have also been proven valuable in glioma imaging. These tracers not only contribute in the diagnostic process by detecting areas of dedifferentiation in diffuse gliomas, but this technique is also valuable in the follow-up of gliomas, as it can differentiate pseudo-progression from real tumor progression. Since multi-parametric MRI and 18F-FET PET are complementary imaging techniques, there may be a synergistic role for PET-MRI imaging in the neuro-oncological imaging of primary brain tumors. This could be of value for both primary staging, as well as during treatment and follow-up.

Pre-treatment visualization of predicted radiation-induced acute alopecia in brain tumour patients.

BACKGROUND AND PURPOSE: Temporary alopecia is a common side-effect in brain tumour patients receiving cranial radiotherapy with a significant psychological burden for the affected patient. The purpose of this study was to generate a method in our treatment planning system (TPS) to visualize the expected radiation-induced alopecia 4 weeks after treatment, in order to inform the patients thereupon before the start of radiotherapy. MATERIAL AND METHODS: A pilot study was conducted in ten patients receiving hypo- (HF) or conventionally fractionated (CF) photon beam Volumetric Modulated Arc Therapy (VMAT) for an intracranial lesion. Dose calculations were correlated to visible alopecia four weeks after the end of treatment to create a structure predictive of alopecia in our TPS. These alopecia structures for both fractionation schedules were validated in two cohorts of 69 HF and 78 CF patients undergoing radiotherapy between 2016 and 2019. RESULTS: In the pilot cohort, a total physical dose of 4 Gy for HF and 12.6 Gy for CF radiotherapy were found to be predictive of alopecia 4 weeks after treatment. Applying these doses to our validation cohort, we found an accurate prediction of alopecia in 59/69 (86%) HF and 73/78 (96%) CF patients. For the total patient group of 147 patients, the predicted amount of alopecia was accurate in 90% of the cases. All inaccurate predictions overestimated the expected extent of alopecia. CONCLUSION: The presented straightforward method to visualize predicted alopecia 4 weeks after treatment has proven to predict the extent alopecia highly accurate in the vast majority of patients. Sharing these results with the patients pre-treatment may result in stress reduction before cranial irradiation.

Development of explanatory movies for the delineation of new organs at risk in neuro-oncology.

Ten new organs at risk (OARs) were recently introduced in the updated European Particle Therapy Network neurological contouring atlas. Despite the use of the illustrated atlas and descriptive text, interindividual contouring variations may persist. To further facilitate the contouring of these OARs, educational films were developed and published on www.cancerdata.org.

Deciphering the glioblastoma phenotype by computed tomography radiomics.

INTRODUCTION: Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. MATERIALS AND METHODS: Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated. RESULTS: Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71. CONCLUSION: In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.

Update of the EPTN atlas for CT- and MR-based contouring in Neuro-Oncology.

BACKGROUND AND PURPOSE: To update the digital online atlas for organs at risk (OARs) delineation in neuro-oncology based on high-quality computed tomography (CT) and magnetic resonance (MR) imaging with new OARs. MATERIALS AND METHODS: In this planned update of the neurological contouring atlas published in 2018, ten new clinically relevant OARs were included, after thorough discussion between experienced neuro-radiation oncologists (RTOs) representing 30 European radiotherapy-oncology institutes. Inclusion was based on daily practice and research requirements. Consensus was reached for the delineation after critical review. Contouring was performed on registered CT with intravenous (IV) contrast (soft tissue & bone window setting) and 3 Tesla (T) MRI (T1 with gadolinium & T2 FLAIR) images of one patient (1 mm slices). For illustration purposes, delineation on a 7 T MRI without IV contrast from a healthy volunteer was added. OARs were delineated by three experienced RTOs and a neuroradiologist based on the relevant literature. RESULTS: The presented update of the neurological contouring atlas was reviewed and approved by 28 experts in the field. The atlas is available online and includes in total 25 OARs relevant to neuro-oncology, contoured on CT and MRI T1 and FLAIR (3 T & 7 T). Three-dimensional (3D) rendered films are also available online. CONCLUSION: In order to further decrease inter- and intra-observer OAR delineation variability in the field of neuro-oncology, we propose the use of this contouring atlas in photon and particle therapy, in clinical practice and in the research setting. The updated atlas is freely available on www.cancerdata.org.

The ROCOCO performance scoring system translates dosimetric differences into clinically relevant endpoints: Comparing IMPT to VMAT in an example pilocytic astrocytoma dataset.

BACKGROUND AND PURPOSE: Proton therapy is expected to outperform photon-based treatment regarding organs at risk (OAR) sparing but to date there is no method to practically measure clinical benefit. Here, we introduce the novel ROCOCO Performance Scoring System (RPSS) translating dose differences into clinically relevant endpoints and apply this to a treatment plan comparison of volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT) in 20 pilocytic astrocytoma patients. MATERIAL AND METHODS: The RPSS was developed on the basis of expert-based weighting factors and toxicity scores per OAR. The imaging datasets of 20 pilocytic astrocytoma patients having undergone radiotherapy were included in this in silico dosimetric comparison trial as proof of principle. For each of these patients, treatment plans to a total dose of 54 Gy (RBE) were generated for VMAT and IMPT and these were compared regarding radiation dose to the clinical target volume (CTV) and OARs. The RPSS was calculated for each treatment plan comparing VMAT and IMPT. RESULTS: In 40 analysed treatment plans, the average and low dose volumes to various OARs were significantly reduced when using IMPT compared to VMAT (p < 0.05). Using the RPSS, a significant difference between both treatment modalities was found, with 85% of the patients having a lower RPSS in favour of the IMPT plan. CONCLUSION: There are dosimetric differences between IMPT and VMAT in pilocytic astrocytoma patients. In absence of clinically validated NTCP models we introduce the RPSS model in order to objectively compare treatment modalities by translating dosimetric differences in potential clinical differences.

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma.

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64-0.78) and used to stratify Kaplan-Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582-8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522-0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436-0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

Chloroquine combined with concurrent radiotherapy and temozolomide for newly diagnosed glioblastoma: a phase IB trial.

Treatment of glioblastoma xenografts with chloroquine results in macroautophagy/autophagy inhibition, resulting in a reduction of tumor hypoxia and sensitization to radiation. Preclinical data show that EGFRvIII-expressing glioblastoma may benefit most from chloroquine because of autophagy dependency. This study is the first to explore the safety, pharmacokinetics and maximum tolerated dose of chloroquine in combination with radiotherapy and concurrent daily temozolomide in patients with a newly diagnosed glioblastoma. This study is a single-center, open-label, dose-finding phase I trial. Patients received oral chloroquine daily starting one week before the course of chemoradiation (temozolomide 75 mg/m2/d) until the end of radiotherapy (59.4 Gy/33 fractions). Thirteen patients were included in the study (n = 6: 200 mg, n = 3: 300 mg, n = 4: 400 mg chloroquine). A total of 44 adverse events, possibly related to chloroquine, were registered including electrocardiogram QTc prolongation, irreversible blurred vision and nausea/vomiting resulting in cessation of temozolomide or delay of adjuvant cycles. The maximum tolerated dose was 200 mg chloroquine. Median overall survival was 16 months (range 2-32). Median survival was 11.5 months for EGFRvIII- patients and 20 months for EGFRvIII+ patients. A daily dose of 200 mg chloroquine was determined to be the maximum tolerated dose when combined with radiotherapy and concurrent temozolomide for newly diagnosed glioblastoma. Favorable toxicity and promising overall survival support further clinical studies.Abbreviations: AE: adverse events; CQ: chloroquine; DLT: dose-limiting toxicities; EGFR: epidermal growth factor receptor; GBM: glioblastoma; HCQ: hydroxychloroquine; IDH1/2: isocitrate dehydrogenase (NADP(+)) 1/2; MTD: maximum tolerated dose; CTC: National Cancer Institute Common Toxicity Criteria; MGMT: O-6-methylguanine-DNA methyltransferase; OS: overall survival; po qd: per os quaque die; SAE: serious adverse events; TMZ: temozolomide; WHO: World Health Organization.

Lymph node response to chemoradiotherapy in oesophageal cancer patients: relationship with radiotherapy fields.

BACKGROUND: The presence of lymph node metastasis (LNmets) is a poor prognostic factor in oesophageal cancer (OeC) patients treated with neoadjuvant chemoradiotherapy (nCRT) followed by surgery. Tumour regression grade (TRG) in LNmets has been suggested as a predictor for survival. The aim of this study was to investigate whether TRG in LNmets is related to their location within the radiotherapy (RT) field. METHODS: Histopathological TRG was retrospectively classified in 2565 lymph nodes (LNs) from 117 OeC patients treated with nCRT and surgery as: (A) no tumour, no signs of regression; (B) tumour without regression; (C) viable tumour and regression; and (D) complete response. Multivariate survival analysis was used to investigate the relationship between LN location within the RT field, pathological TRG of the LN and TRG of the primary tumour. RESULTS: In 63 (54%) patients, viable tumour cells or signs of regression were seen in 264 (10.2%) LNs which were classified as TRG-B (n = 56), C (n = 104) or D (n = 104) LNs. 73% of B, C and D LNs were located within the RT field. There was a trend towards a relationship between LN response and anatomical LN location with respect to the RT field (p = 0.052). Multivariate analysis showed that only the presence of LNmets within the RT field with TRG-B is related to poor overall survival. CONCLUSION: Patients have the best survival if all LNmets show tumour regression, even if LNmets are located outside the RT field. Response in LNmets to nCRT is heterogeneous which warrants further studies to better understand underlying mechanisms.

Sensitivity to change of the EORTC quality of life module measuring cancer-related fatigue (EORTC QlQ-Fa12): Results from the international psychometric validation.

OBJECTIVE: The European Organisation for Research and Treatment of Cancer Quality of Life Group (EORTC QLG) has developed a multidimensional instrument measuring cancer-related fatigue, the EORTC QLQ-FA12. The analysis of sensitivity to change is an essential part of psychometric validation. With this study, we investigated the EORTC QLQ-FA12's sensitivity to change. METHODS: The methodology follows the EORTC guidelines of EORTC QLG for phase IV validation of modules. We included cancer patients undergoing curative and palliative treatment at t1 and followed them up prospectively over the course of their treatment (t2) and 4 weeks after completion of treatment (t3). Data were collected prospectively at 17 sites in 11 countries. Sensitivity to change was investigated using analysis of variance. RESULTS: A total sample of 533 patients was enrolled with various tumour types, different stages of cancer, and receiving either curative treatment (n=311) or palliative treatment (n=222). Over time all fatigue scores were significantly higher in the palliative treatment group compared with the curative group (p < .001). Physical fatigue increased with medium effect size over the course of treatment in the curative group (standardized response mean [SRM] (t1,t2) = 0.44]. After treatment physical [SRM (t2,t3) = 0.39], emotional [SRM (t2,t3)= 0.28] and cognitive fatigue (SRM [t2,t3] = 0.22) declined significantly in the curative group. In the palliative group, emotional (SRM [t2,t3] = 0.18) as well as cognitive [SRM [t2,t3] = 0.26) fatigue increases significantly. CONCLUSIONS: The EORTC-QLQ-FA12 proved to identify clinically significant changes in fatigue in the course of curative and palliative cancer treatment.

Decision Support Systems in Oncology.

Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data-clinical, imaging, biologic, genetic, cost-to produce validated predictive models. DSSs compare the personalized probable outcomes-toxicity, tumor control, quality of life, cost effectiveness-of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders-clinicians, medical directors, medical insurers, patient advocacy groups-and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.

Characterizing geometrical accuracy in clinically optimised 7T and 3T magnetic resonance images for high-precision radiation treatment of brain tumours.

BACKGROUND AND PURPOSE: In neuro-oncology, high spatial accuracy is needed for clinically acceptable high-precision radiation treatment planning (RTP). In this study, the clinical applicability of anatomically optimised 7-Tesla (7T) MR images for reliable RTP is assessed with respect to standard clinical imaging modalities. MATERIALS AND METHODS: System- and phantom-related geometrical distortion (GD) were quantified on clinically-relevant MR sequences at 7T and 3T, and on CT images using a dedicated anthropomorphic head phantom incorporating a 3D grid-structure, creating 436 points-of-interest. Global GD was assessed by mean absolute deviation (MADGlobal). Local GD relative to the magnetic isocentre was assessed by MADLocal. Using 3D displacement vectors of individual points-of-interest, GD maps were created. For clinically acceptable radiotherapy, 7T images need to meet the criteria for accurate dose delivery (GD < 1 mm) and present comparable GD as tolerated in clinically standard 3T MR/CT-based RTP. RESULTS: MADGlobal in 7T and 3T images ranged from 0.3 to 2.2 mm and 0.2-0.8 mm, respectively. MADLocal increased with increasing distance from the isocentre, showed an anisotropic distribution, and was significantly larger in 7T MR sequences (MADLocal = 0.2-1.2 mm) than in 3T (MADLocal = 0.1-0.7 mm) (p < 0.05). Significant differences in GD were detected between 7T images (p < 0.001). However, maximum MADLocal remained ≤1 mm within 68.7 mm diameter spherical volume. No significant differences in GD were found between 7T and 3T protocols near the isocentre. CONCLUSIONS: System- and phantom-related GD remained ≤1 mm in central brain regions, suggesting that 7T MR images could be implemented in radiotherapy with clinically acceptable spatial accuracy and equally tolerated GD as in 3T MR/CT-based RTP. For peripheral regions, GD should be incorporated in safety margins for treatment uncertainties. Moreover, the effects of sequence-related factors on GD needs further investigation to obtain RTP-specific MR protocols.