[18F]FDG PET/CT-based response assessment of stage IV non-small cell lung cancer treated with paclitaxel-carboplatin-bevacizumab with or without nitroglycerin patches.

PURPOSE: Nitroglycerin (NTG) is a vasodilating drug, which increases tumor blood flow and consequently decreases hypoxia. Therefore, changes in [18F] fluorodeoxyglucose positron emission tomography ([18F]FDG PET) uptake pattern may occur. In this analysis, we investigated the feasibility of [18F]FDG PET for response assessment to paclitaxel-carboplatin-bevacizumab (PCB) treatment with and without NTG patches. And we compared the [18F]FDG PET response assessment to RECIST response assessment and survival. METHODS: A total of 223 stage IV non-small cell lung cancer (NSCLC) patients were included in a phase II study (NCT01171170) randomizing between PCB treatment with or without NTG patches. For 60 participating patients, a baseline and a second [18F]FDG PET/computed tomography (CT) scan, performed between day 22 and 24 after the start of treatment, were available. Tumor response was defined as a 30 % decrease in CT and PET parameters, and was compared to RECIST response at week 6. The predictive value of these assessments for progression free survival (PFS) and overall survival (OS) was assessed with and without NTG. RESULTS: A 30 % decrease in SUVpeak assessment identified more patients as responders compared to a 30 % decrease in CT diameter assessment (73 % vs. 18 %), however, this was not correlated to OS (SUVpeak30 p = 0.833; CTdiameter30 p = 0.557). Changes in PET parameters between the baseline and the second scan were not significantly different for the NTG group compared to the control group (p value range 0.159-0.634). The CT-based (part of the [18F]FDG PET/CT) parameters showed a significant difference between the baseline and the second scan for the NTG group compared to the control group (CT diameter decrease of 7 ± 23 % vs. 19 ± 14 %, p = 0.016, respectively). CONCLUSIONS: The decrease in tumoral FDG uptake in advanced NSCLC patients treated with chemotherapy with and without NTG did not differ between both treatment arms. Early PET-based response assessment showed more tumor responders than CT-based response assessment (part of the [18F]FDG PET/CT); this was not correlated to survival. This might be due to timing of the [18F]FDG PET shortly after the bevacizumab infusion.

Quality assessment of positron emission tomography scans: recommendations for future multicentre trials.

BACKGROUND: Standardization protocols and guidelines for positron emission tomography (PET) in multicenter trials are available, despite a large variability in image acquisition and reconstruction parameters exist. In this study, we investigated the compliance of PET scans to the guidelines of the European Association of Nuclear Medicine (EANM). From these results, we provide recommendations for future multicenter studies using PET. MATERIAL AND METHODS: Patients included in a multicenter randomized phase II study had repeated PET scans for early response assessment. Relevant acquisition and reconstruction parameters were extracted from the digital imaging and communications in medicine (DICOM) header of the images. The PET image parameters were compared to the guidelines of the EANM for tumor imaging version 1.0 recommended parameters. RESULTS: From the 223 included patients, 167 baseline scans and 118 response scans were available from 15 hospitals. Scans of 19% of the patients had an uptake time that fulfilled the Uniform Protocols for Imaging in Clinical Trials response assessment criteria. The average quality score over all hospitals was 69%. Scans with a non-compliant uptake time had a larger standard deviation of the mean standardized uptake value (SUVmean) of the liver than scans with compliant uptake times. CONCLUSIONS: Although a standardization protocol was agreed on, there was a large variability in imaging parameters. For future, multicenter studies including PET imaging a prospective central quality review during patient inclusion is needed to improve compliance with image standardization protocols as defined by EANM.

Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study.

BACKGROUND: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition- and reconstruction protocols influences robustness of radiomic analyses. The aim of this study was to investigate the influence of different CT-scanners, slice thicknesses, exposures and gray-level discretization on radiomic feature values and their stability. MATERIAL AND METHODS: A texture phantom with ten different inserts was scanned on nine different CT-scanners with varying tube currents. Scans were reconstructed with 1.5 mm or 3 mm slice thickness. Image pre-processing comprised gray-level discretization in ten different bin widths ranging from 5 to 50 HU and different resampling methods (i.e., linear, cubic and nearest neighbor interpolation to 1 × 1 × 3 mm3 voxels) were investigated. Subsequently, 114 textural radiomic features were extracted from a 2.1 cm3 sphere in the center of each insert. The influence of slice thickness, exposure and bin width on feature values was investigated. Feature stability was assessed by calculating the concordance correlation coefficient (CCC) in a test-retest setting and for different combinations of scanners, tube currents and slice thicknesses. RESULTS: Bin width influenced feature values, but this only had a marginal effect on the total number of stable features (CCC > 0.85) when comparing different scanners, slice thicknesses or exposures. Most radiomic features were affected by slice thickness, but this effect could be reduced by resampling the CT-images before feature extraction. Statistics feature 'energy' was the most dependent on slice thickness. No clear correlation between feature values and exposures was observed. CONCLUSIONS: CT-scanner, slice thickness and bin width affected radiomic feature values, whereas no effect of exposure was observed. Optimization of gray-level discretization to potentially improve prognostic value can be performed without compromising feature stability. Resampling images prior to feature extraction decreases the variability of radiomic features.

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine.

BACKGROUND: Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided. AIM: The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm. DISCUSSION: Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

Turning the attention to time-resolved EPID-images: treatment error classification with transformer multiple instance learning.

Objective.The aim of this work was to develop a novel artificial intelligence-assistedin vivodosimetry method using time-resolved (TR) dose verification data to improve quality of external beam radiotherapy.Approach. Although threshold classification methods are commonly used in error classification, they may lead to missing errors due to the loss of information resulting from the compression of multi-dimensional electronic portal imaging device (EPID) data into one or a few numbers. Recent research has investigated the classification of errors on time-integrated (TI)in vivoEPID images, with convolutional neural networks showing promise. However, it has been observed previously that TI approaches may cancel out the error presence onγ-maps during dynamic treatments. To address this limitation, simulated TRγ-maps for each volumetric modulated arc radiotherapy angle were used to detect treatment errors caused by complex patient geometries and beam arrangements. Typically, such images can be interpreted as a set of segments where only set class labels are provided. Inspired by recent weakly supervised approaches on histopathology images, we implemented a transformer based multiple instance learning approach and utilized transfer learning from TI to TRγ-maps.Main results. The proposed algorithm performed well on classification of error type and error magnitude. The accuracy in the test set was up to 0.94 and 0.81 for 11 (error type) and 22 (error magnitude) classes of treatment errors, respectively.Significance. TR dose distributions can enhance treatment delivery decision-making, however manual data analysis is nearly impossible due to the complexity and quantity of this data. Our proposed model efficiently handles data complexity, substantially improving treatment error classification compared to models that leverage TI data.

Variation in current prescription practice of stereotactic body radiotherapy for peripherally located early stage non-small cell lung cancer: Recommendations for prescribing and recording according to the ACROP guideline and ICRU report 91.

BACKGROUND AND PURPOSE: In 2017 the ACROP guideline on SBRT for peripherally located early stage NSCLC was published. Later that year ICRU-91 about prescribing, recording and reporting was published. The purpose of this study is to quantify the current variation in prescription practice in the institutions that contributed to the ACROP guideline and to establish the link between the ACROP and ICRU-91 recommendations. MATERIAL AND METHODS: From each of the eight participating centres, 15 SBRT plans for stage I NSCLC were analyzed. Plans were generated following the institutional protocol, centres prescribed 3 × 13.5 Gy, 3 × 15 Gy, 3 × 17 Gy or 3 × 18 Gy. Dose parameters of the target volumes were reported as recommended by ICRU-91 and also converted to BED10Gy. RESULTS: The intra-institutional variance in D98%, Dmean and D2% of the PTV and GTV/ITV is substantially smaller than the inter-institutional spread, indicating well protocollised planning procedures are followed. The median values per centre ranged from 56.1 Gy to 73.1 Gy (D2%), 50.4 Gy to 63.3 Gy (Dmean) and 40.5 Gy to 53.6 Gy (D98%) for the PTV and from 57.1 Gy to 73.6 Gy (D2%), 53.7 Gy to 68.7 Gy (Dmean) and 48.5 Gy to 62.3 Gy (D98%) for the GTV/ITV. Comparing the variance in PTV D98% with the variance in GTV Dmean per centre, using an F-test, shows that four centres have a larger variance in GTV Dmean, while one centre has a larger variance in PTV D98% (p values <0.01). This shows some centres focus on achieving a constant PTV coverage while others aim at a constant GTV coverage. CONCLUSION: More detailed recommendations for dose planning and reporting of lung SBRT in line with ICRU-91 were formulated, including a minimum PTV D98% of 100 Gy BED10Gy and minimum GTV/ITV mean dose of 150 Gy BED10Gy and a D2% in the range of 60-70 Gy.

Genomics of non-small cell lung cancer (NSCLC): Association between CT-based imaging features and EGFR and K-RAS mutations in 122 patients-An external validation.

OBJECTIVE: To validate previously identified associations between radiological features and clinical features with Epidermal Growth Factor Receptor (EGFR)/ Kirsten RAt Sarcoma (KRAS) alterations in an independent group of patients with Non-Small Cell Lung Cancer (NSCLC). MATERIAL AND METHODS: A total of 122 patients with NSCLC tested for EGFR/KRAS alterations were included. Clinical and radiological features were recorded. Univariate analysis were performed to look at the associations of the studied features with EGFR/KRAS alterations. Previously calculated composite model parameters for each gene alteration prediction were applied to this validation cohort. ROC (Receiver Operating Characteristic) curves were drawn using the previously validated composite models, and also for each significant individual characteristic of the previous training cohort model. The Area Under the ROC Curve (AUC) with 95% Confidence Intervals (CI) was calculated and compared between the full models. RESULTS: At univariate analysis, EGFR+ confirmed an association with an internal air bronchogram, pleural retraction, emphysema and lack of smoking; KRAS+ with round shape, emphysema and smoking. The AUC (95%CI) in the new cohort was confirmed to be high for EGFR+ prediction, with a value of: 0.82 (0.69-0.95) vs. 0.82 in the previous cohort, whereas it was smaller for KRAS+ prediction, with a value of 0.60 (0.48-0.72) vs. 0.67 in the previous cohort. Looking at single features in the new cohort, we found that the AUC for the models including only smoking was similar to that of the full model (including radiological and clinical features) for both gene alterations. CONCLUSIONS: Although this study validated the significant association of clinical and radiological features with EGFR/KRAS alterations, models based on these composite features are not superior to smoking history alone to predict the mutations.

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.

What you see is (not) what you get: tools for a non-radiologist to evaluate image quality in lung cancer.

Medical images are an integral part of oncological patient records and they are reviewed by many different specialists. Therefore, it is important that besides imaging experts, other clinicians are also aware that the diagnostic value of a scan is influenced by the applied imaging protocol. Based on two clinical lung cancer trials, we experienced that, even within a study protocol, there is a large variability in imaging parameters, which has direct impact on the interpretation of the image. These two trials were: 1) the NTR3628 in which the added value of gadolinium magnetic resonance imaging (Gd-MRI) to dedicated contrast enhanced computed tomography (CE-CT) for detecting asymptomatic brain metastases in stage III non-small cell lung cancer (NSCLC) was investigated and 2) a sub-study of the NVALT 12 trial (NCT01171170) in which repeated 18 F-fludeoxyglucose positron emission tomography (18F-FDG-PET) imaging for early response assessment was investigated. Based on the problems encountered in the two trials, we provide recommendations for non-radiology clinicians, which can be used in daily interpretation of imaging. Variations in image parameters cannot only influence trial results, but sub-optimal imaging can also influence treatment decisions in daily lung cancer care, when a physician is not aware of the scanning details.

Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score.

INTRODUCTION: In this review we describe recent developments in the field of radiomics along with current relevant literature linking it to tumor biology. We furthermore explore the methodologic quality of these studies with our in-house radiomics quality scoring (RQS) tool. Finally, we offer our vision on necessary future steps for the development of stable radiomic features and their links to tumor biology. METHODS: Two authors (S.S. and H.W.) independently performed a thorough systematic literature search and outcome extraction to identify relevant studies published in MEDLINE/PubMed (National Center for Biotechnology Information, NCBI), EMBASE (Ovid) and Web of Science (WoS). Two authors (S.S, H.W) separately and two authors (J.v.T and E.d.J) concordantly scored the articles for their methodology and analyses according to the previously published radiomics quality score (RQS). RESULTS: In summary, a total of 655 records were identified till 25-09-2017 based on the previously specified search terms, from which n = 236 in MEDLINE/PubMed, n = 215 in EMBASE and n = 204 from Web of Science. After determining full article availability and reading the available articles, a total of n = 41 studies were included in the systematic review. The RQS scoring resulted in some discrepancies between the reviewers, e.g. reviewer H.W scored 4 studies ≥50%, reviewer S.S scored 3 studies ≥50% while reviewers J.v.T and E.d.J scored 1 study ≥50%. Up to nine studies were given a quality score of 0%. The majority of studies were scored below 50%. DISCUSSION: In this study, we performed a systematic literature search linking radiomics to tumor biology. All but two studies (n = 39) revealed that radiomic features derived from ultrasound, CT, PET and/or MR are significantly associated with one or several specific tumor biologic substrates, from somatic mutation status to tumor histopathologic grading and metabolism. Considerable inter-observer differences were found with regard to RQS scoring, while important questions were raised concerning the interpretability of the outcome of such scores.

Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer.

OBJECTIVES: Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinoma patients undergoing chemotherapy. MATERIALS AND METHODS: Two datasets of chemo-naive stage IV adenocarcinoma patients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial. The main exclusion criteria were EGFR mutation or unknown mutation status and non-delineated primary tumor. Radiomic features were calculated for the primary tumor. The c-index of cox regression was calculated and compared to the signature performance for overall survival (OS). RESULTS: In total CT scans from 195 patients were eligible for analysis. Patients having a prognostic index (PI) lower than the signature median (n = 92) had a significantly better OS than patients with a PI higher than the median (n = 103, HR 1.445, 95% CI 1.07-1.95, p = 0.02, c-index 0.576, 95% CI 0.527-0.624). CONCLUSION: The radiomic signature, derived from daily practice CT scans, has prognostic value for stage IV NSCLC, however the signature performs less than previously described for stage I-III NSCLC stages. In the future, machine learning techniques can potentially lead to a better prognostic imaging based model for stage IV NSCLC.

Radiomics: the bridge between medical imaging and personalized medicine.

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Decision support systems for personalized and participative radiation oncology.

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

A comparison of the relative biological effectiveness of low energy electronic brachytherapy sources in breast tissue: a Monte Carlo study.

Electronic brachytherapy sources use low energy photons to treat the tumor bed during or after breast-conserving surgery. The relative biological effectiveness of two electronic brachytherapy sources was explored to determine if spectral differences due to source design influenced radiation quality and if radiation quality decreased with distance in the breast. The RBE was calculated through the number of DNA double strand breaks (RBEDSB) using the Monte Carlo damage simulator (MCDS) in combination with other Monte Carlo electron/photon spectrum calculations. 50kVp photons from the Intrabeam (Carl Zeiss Surgical) and Axxent (Xoft) through 40-mm spherical applicators were simulated to account for applicator and tissue attenuation in a variety of breast tissue compositions. 40kVp Axxent photons were also simulated. Secondary electrons (known to be responsible for most DNA damage) spectra at different distance were inputted into MCDS to calculate the RBEDSB. All RBEDSB used a cobalt-60 reference. RBEDSB data was combined with corresponding average photon spectrum energy for the Axxent and applied to model-based average photon energy distributions to produce an RBEDSB map of an accelerated partial breast irradiation (APBI) patient. Both Axxent and Intrabeam 50kVp spectra were shown to have a comparable RBEDSB of between 1.4 and 1.6 at all distances in spite of progressive beam hardening. The Axxent 40kVp also demonstrated a similar RBEDSB at distances. Most RBEDSB variability was dependent on the tissue type as was seen in rib (RBEDSB ≈ 1.4), gland (≈1.55), adipose (≈1.59), skin (≈1.52) and lung (≈1.50). RBEDSB variability between both sources was within 2%. A correlation was shown between RBEDSB and average photon energy and used to produce an RBEDSB map of a dose distribution in an APBI patient dataset. Radiation quality is very similar between electronic brachytherapy sources studied. No significant reductions in RBEDSB were observed with increasing distance from the source.