Hiel, B. van der; Aalbersberg, E.A.; Eertwegh, A.J.M. van den; Veen, L.J.D.V. de van der; Stokkel, M.P.M.; Lopez-Yurda, M.; ... ; Haanen, J.B.A.G. 2024
Purpose: The aims of this study were to investigate whether (early) PERCIST response monitoring with F-18-FDG PET/CT is predictive for progression-free survival (PFS) in unresectable stage III or... Show morePurpose: The aims of this study were to investigate whether (early) PERCIST response monitoring with F-18-FDG PET/CT is predictive for progression-free survival (PFS) in unresectable stage III or IV melanoma patients treated with BRAF/MEK inhibitor (MEKi) and to define dissemination patterns at progression with a lesion-based evaluation in direct comparison to baseline to improve our understanding of F-18-FDG PET/CT during BRAF/MEKi.Patients and methods: This prospective multicenter single-arm study included 70 patients with unresectable stage III/IV BRAF-mutated melanoma who underwent contrast-enhanced CT and F-18-FDG PET/CT at baseline and 2 and 7 weeks during treatment with vemurafenib plus cobimetinib and at progression if possible. Tumor response assessment was done with RECIST1.1 and PERCIST. Follow-up PET/CT scans were visually compared with baseline to assess dissemination patterns.Results: Using RECIST1.1, PFS was not significantly different between the response groups (P = 0.26). At 2 weeks, PERCIST median PFS was 15.7 months for patients with complete metabolic response (CMR) versus 8.3 months for non-CMR (P = 0.035). The hazards ratio (HR) for progression/death in non-CMR versus CMR was 1.99 (95% confidence interval [CI], 1.03-3.84; P = 0.040) and 1.77 (95% CI, 0.91-3.43; P = 0.0935) when adjusting for lactate dehydrogenase (LDH). At 7 weeks, median PFS for PERCIST CMR was 16.7 months versus 8.5 months for non-CMR (P = 0.0003). The HR for progression/death in the non-CMR group was significantly increased (HR, 2.94; 95% CI, 1.60-5.40; P = 0.0005), even when adjusting for LDH (HR, 2.65; 95% CI, 1.43-4.91; P = 0.0020). At week 7, F-18-FDG PET/CT was false-positive in all 4 (6%) patients with new FDG-avid lesions but CMR of known metastases. When F-18-FDG PET/CT was performed at progressive disease, 18/22 (82%) patients had progression of known metastases with or without new F-18-FDG-avid lesions.Conclusions: This study shows that PERCIST response assessment at week 7 is predictive for PFS, regardless of LDH. At 2 weeks, patients with CMR have longer PFS than patients with non-CMR, but different PET parameters should be investigated to further evaluate the added value of early F-18-FDG PET/CT. Disease progression on PET/CT is predominated by progression of known metastases, and new F-18-FDG-avid lesions during BRAF/MEKi are not automatically a sign of recurrent disease. Show less
Introduction: Zr-89-immuno-PET (positron emission tomography with zirconium-89-labeled monoclonal antibodies ([Zr-89]Zr-mAbs)) can be used to study the biodistribution of mAbs targeting the immune... Show moreIntroduction: Zr-89-immuno-PET (positron emission tomography with zirconium-89-labeled monoclonal antibodies ([Zr-89]Zr-mAbs)) can be used to study the biodistribution of mAbs targeting the immune system. The measured uptake consists of target-specific and non-specific components, and it can be influenced by plasma availability of the tracer. To find evidence for target-specific uptake, i.e., target engagement, we studied five immune-checkpoint-targeting [Zr-89]Zr-mAbs to (1) compare the uptake with previously reported baseline values for non-specific organ uptake (ns-baseline) and (2) look for saturation effects of increasing mass doses. Method: Zr-89-immuno-PET data from five [Zr-89]Zr-mAbs, i.e., nivolumab and pembrolizumab (anti-PD-1), durvalumab (anti-PD-L1), BI 754,111 (anti-LAG-3), and ipilimumab (anti-CTLA-4), were analysed. For each mAb, 2-3 different mass doses were evaluated. PET scans and blood samples from at least two time points 24 h post injection were available. In 35 patients, brain, kidneys, liver, spleen, lungs, and bone marrow were delineated. Patlak analysis was used to account for differences in plasma activity concentration and to quantify irreversible uptake (K-i). To identify target engagement, K-i values were compared to ns-baseline K-i values previously reported, and the effect of increasing mass doses on K-i was investigated. Results: All mAbs, except ipilimumab, showed K-i values in spleen above the ns-baseline for the lowest administered mass dose, in addition to decreasing K-i values with higher mass doses, both indicative of target engagement. For bone marrow, no ns-baseline was established previously, but a similar pattern was observed. For kidneys, most mAbs showed K-i values within the ns-baseline for both low and high mass doses. However, with high mass doses, some saturation effects were seen, suggestive of a lower ns-baseline value. K-i values were near zero in brain tissue for all mass doses of all mAbs. Conclusion: Using Patlak analysis and the established ns-baseline values, evidence for target engagement in (lymphoid) organs for several immune checkpoint inhibitors could be demonstrated. A decrease in the K-i values with increasing mass doses supports the applicability of Patlak analysis for the assessment of target engagement for PET ligands with irreversible uptake behavior. Show less
Rational: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool.... Show moreRational: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made.Methods: PubMed, and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging.Results: A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult.Conclusion: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included. Show less
Noortman, W.A.; Aide, N.; Vriens, D.; Arkes, L.S.; Slump, C.H.; Boellaard, R.; ... ; Geus-Oei, L.F. de 2023
Aim: To build and externally validate an [F-18]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC).Methods: Two multicentre datasets of... Show moreAim: To build and externally validate an [F-18]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC).Methods: Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [F-18]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index).Results: In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82.Conclusion: Although assessed in two small but independent cohorts, an [F-18]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort. Show less
Huiskamp, M.; Yaqub, M.; Lingen, M.R. van; Pouwels, P.J.W.; de Ruiter, L.R.J.; Killestein, J.; ... ; Hulst, H.E. 2023
Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing... Show moreBackground Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified. Show less
Verhoeff, S.R.; Donk, P.P. van de; Aarntzen, E.H.J.G.; Oosting, S.F.; Brouwers, A.H.; Miedema, I.H.C.; ... ; Herpen, C.M.L. van 2022
In this PD-L1 ImagiNg to prediCt durvalumab treatment response in SCCHN (PINCH) study, we performed 89Zr-DFO-durvalumab (anti- PD-L1 [programmed death ligand 1]) PET/CT in patients with recurrent... Show moreIn this PD-L1 ImagiNg to prediCt durvalumab treatment response in SCCHN (PINCH) study, we performed 89Zr-DFO-durvalumab (anti- PD-L1 [programmed death ligand 1]) PET/CT in patients with recurrent or metastatic (R/M) squamous cell carcinoma of the head and neck (SCCHN) before monotherapy durvalumab treatment. The primary aims were to assess safety and feasibility of 89Zr-DFO-durvalumab PET imag-ing and predict disease control rate during durvalumab treatment. Sec-ondary aims were to correlate 89Zr-DFO-durvalumab uptake to tumor PD-L1 expression, 18F-FDG uptake, and treatment response of individ-ual lesions.Methods: In this prospective multicenter phase I-II study (NCT03829007), patients with incurable R/M SCCHN underwent base-line 18F-FDG PET and CT or MRI. Subsequently, PD-L1 PET imaging was performed 5 d after administration of 37 MBq of 89Zr-DFO-durvalumab. To optimize imaging conditions, dose finding was per-formed in the first 14 patients. For all patients (n = 33), durvalumab treatment (1,500 mg/4 wk, intravenously) was started within 1 wk after PD-L1 PET imaging and continued until disease progression or unacceptable toxicity (maximum, 24 mo). CT evaluation was assessed according to RECIST 1.1 every 8 wk. PD-L1 expression was deter-mined by combined positive score on (archival) tumor tissue. 89Zr-DFO-durvalumab uptake was measured in 18F-FDG-positive lesions, primary and secondary lymphoid organs, and blood pool.Results: In total, 33 patients with locoregional recurrent (n = 12) or metastatic SCCHN (n = 21) were enrolled. 89Zr-DFO-durvalumab injection was safe. A dose of 10 mg of durvalumab resulted in highest tumor-to-blood ratios. After a median follow-up of 12.6 mo, overall response rate was 26%. The disease control rate at 16 wk was 48%, with a mean duration of 7.8 mo (range, 1.7-21.1). On a patient level, 89Zr-DFO-durvalumab SUVpeak or tumor-to-blood ratio could not predict treatment response (hazard ratio, 1.5 [95% CI, 0.5-3.9; P = 0.45] and 1.3 [95% CI, 0.5-3.3; P = 0.60], respectively). Also, on a lesion level, 89Zr-DFO-durvalumab SUVpeak showed no substantial correlation to treatment response (Spearman p, 0.45; P = 0.051). Lesional 89Zr-DFO-durvalumab uptake did not correlate to PD-L1 combined positive score but did correlate to 18F-FDG SUVpeak (Spearman p, 0.391; P = 0.005).Conclusion: PINCH is the first, to our knowledge, PD-L1 PET/CT study in patients with R/M SCCHN and has shown the feasibility and safety of 89Zr-DFO-durvalumab PET/CT in a multicenter trial. 89Zr-DFO-durvalumab uptake did not correlate to durvalumab treat-ment response. Show less
PET radiomics applied to oncology allow the measurement of intratumoral heterogeneity. This quantification can be affected by image protocols; hence, there is an increased interest in understanding... Show morePET radiomics applied to oncology allow the measurement of intratumoral heterogeneity. This quantification can be affected by image protocols; hence, there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that interest, this study explored how radiomic features are affected by changes in F-18-FDG uptake time, image reconstruction, lesion delineation, and radiomic binning settings. Methods: Ten non-small cell lung cancer patients underwent F-18-FDG PET on 2 consecutive days. On each day, scans were obtained at 60 and 90 min after injection and reconstructed following EARL version 1 and with point-spread-function resolution modeling (PSF-EARL2). Lesions were delineated with an SUV threshold of 4.0, with 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both a fixed bin width (FBW) and a fixed bin number before the calculation of the radiomic features. Repeatability of features was measured with the intraclass correlation coefficient, and the change in feature value over time was calculated as a function of its repeatability. Features were then classified into use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (intraclass correlation coefficient. 0.9), 35% being classified for dual-time-point use cases as being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with an unclear dependency on time, 20% were classified for cross-sectional use while being robust to uptake time changes, and 6% were discarded for poor repeatability. EARL version 1 images had 1 fewer repeatable feature (neighborhood gray-level different matrix coarseness) than PSF- EARL2; the contrast-based delineation had the poorest repeatability of the delineation methods, with 45% of features being discarded; and fixed bin number resulted in lower repeatability than FBW ( 45% and 6% of features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. On the basis of their susceptibility to uptake time, radiomic features were classified into specific non-small cell lung cancer PET radiomics use cases. Show less
Better biomarkers are needed to predict treatment outcome in non-small cell lung cancer (NSCLC) patients treated with anti-programmed death1/programmed death-ligand 1 (PD-1/PD-L1) checkpoint... Show moreBetter biomarkers are needed to predict treatment outcome in non-small cell lung cancer (NSCLC) patients treated with anti-programmed death1/programmed death-ligand 1 (PD-1/PD-L1) checkpoint inhibitors. PD-L1 immunohistochemistry has limited predictive value, possibly because of tumor heterogeneity of PD-L1 expression. Noninvasive PD-L1 imaging using Zr-89-durvalumab might better reflect tumor PD-L1 expression. Methods: NSCLC patients eligible for second-line immunotherapy were enrolled. Patients received 2 injections of Zr-89-durvalumab: one without a preceding dose of unlabeled durvalumab (tracer dose only) and one with a preceding dose of 750 mg of durvalumab, directly before tracer injection. Up to 4 PET/CT scans were obtained after tracer injection. After imaging acquisition, patients were treated with 750 mg of durvalumab every 2 wk. Tracer biodistribution and tumor uptake were visually assessed and quantified as SUV, and both imaging acquisitions were compared. Tumor tracer uptake was correlated with PD-L1 expression and clinical outcome, defined as response to durvalumab treatment. Results: Thirteen patients were included, and 10 completed all scheduled PET scans. No tracer-related adverse events were observed, and all patients started durvalumab treatment. Biodistribution analysis showed Zr-89-durvalumab accumulation in the blood pool, liver, and spleen. Serial imaging showed that image acquisition 120 h after injection delivered the best tumor-to-blood pool ratio. Most tumor lesions were visualized with the tracer dose only versus the coinjection imaging acquisition (25% vs. 13.5% of all lesions). Uptake heterogeneity was observed within (SUVpeak range, 0.2-15.1) and between patients. Tumor uptake was higher in patients with treatment response or stable disease than in patients with disease progression according to RECIST 1.1. However, this difference was not statistically significant (median SUVpeak , 4.9 vs. 2.4; P = 0.06). SUVpeak correlated better with the combined tumor and immune cell PD-L1 score than with PD-L1 expression on tumor cells, although neither was statistically significant (P = 0.06 and P = 0.93, respectively). Conclusion: Zr-89-durvalumab was safe, without any tracer-related adverse events, and more tumor lesions were visualized using the tracer dose-only imaging acquisition. Zr-89-durvalumab tumor uptake was higher in patients with a response to durvalumab treatment but did not correlate with tumor PD-L1 immunohistochemistry. Show less
Metastatic tumor deposits in bone marrow elicit differential bone responses that vary with the type of malignancy. This results in either sclerotic, lytic, or mixed bone lesions, which can change... Show moreMetastatic tumor deposits in bone marrow elicit differential bone responses that vary with the type of malignancy. This results in either sclerotic, lytic, or mixed bone lesions, which can change in morphology due to treatment effects and/or secondary bone remodeling. Hence, morphological imaging is regarded unsuitable for response assessment of bone metastases and in the current Response Evaluation Criteria In Solid Tumors 1.1 (RECIST1.1) guideline bone metastases are deemed unmeasurable. Nevertheless, the advent of functional and molecular imaging modalities such as whole-body magnetic resonance imaging (WB-MRI) and positron emission tomography (PET) has improved the ability for follow-up of bone metastases, regardless of their morphology. Both these modalities not only have improved sensitivity for visual detection of bone lesions, but also allow for objective measurements of bone lesion characteristics. WB-MRI provides a global assessment of skeletal metastases and for a one-step "all-organ" approach of metastatic disease. Novel MRI techniques include diffusion-weighted imaging (DWI) targeting highly cellular lesions, dynamic contrast-enhanced MRI (DCE-MRI) for quantitative assessment of bone lesion vascularization, and multiparametric MRI (mpMRI) combining anatomical and functional sequences. Recommendations for a homogenization of MRI image acquisitions and generalizable response criteria have been developed. For PET, many metabolic and molecular radiotracers are available, some targeting tumor characteristics not confined to cancer type (e.g. F-18-FDG) while other targeted radiotracers target specific molecular characteristics, such as prostate specific membrane antigen (PSMA) ligands for prostate cancer. Supporting data on quantitative PET analysis regarding repeatability, reproducibility, and harmonization of PET/CT system performance is available. Bone metastases detected on PET and MRI can be quantitatively assessed using validated methodologies, both on a whole-body and individual lesion basis. Both have the advantage of covering not only bone lesions but visceral and nodal lesions as well. Hybrid imaging, combining PET with MRI, may provide complementary parameters on the morphologic, functional, metabolic and molecular level of bone metastases in one examination. For clinical implementation of measuring bone metastases in response assessment using WB-MRI and PET, current RECIST1.1 guidelines need to be adapted. This review summarizes available data and insights into imaging of bone metastases using MRI and PET. Show less
Fournier, L.; Costaridou, L.; Bidaut, L.; Michoux, N.; Lecouvet, F.E.; Geus-Oei, L.F. de; ... ; European Soc Radiology 2021
Purpose In order to achieve comparability of image quality, harmonisation of PET system performance is imperative. In this study, prototype harmonisation criteria for PET brain studies were... Show morePurpose In order to achieve comparability of image quality, harmonisation of PET system performance is imperative. In this study, prototype harmonisation criteria for PET brain studies were developed.Methods Twelve clinical PET/CT systems (4 GE, 4 Philips, 4 Siemens, including SiPM-based "digital" systems) were used to acquire 30-min PET scans of a Hoffman 3D Brain phantom filled with similar to 33 kBq.mL(-1) [F-18]FDG. Scan data were reconstructed using various reconstruction settings. The images were rigidly coregistered to a template (voxel size 1.17 x 1.17 x 2.00 mm(3)) onto which several volumes of interest (VOIs) were defined. Recovery coefficients (RC) and grey matter to white matter ratios (GMWMr) were derived for eroded (denoted in the text by subscript e) and non-eroded grey (GM) and white (WM) matter VOIs as well as a mid-phantom cold spot (VOIcold) and VOIs from the Hammers atlas. In addition, left-right hemisphere differences and voxel-by-voxel differences compared to a reference image were assessed.Results Systematic differences were observed for reconstructions with and without point-spread-function modelling (PSFON and PSFOFF, respectively). Normalising to image-derived activity, upper and lower limits ensuring image comparability were as follows: for PSFON, RCGMe = [0.97-1.01] and GMWMr(e) = [3.51-3.91] for eroded VOI and RCGM = [0.78-0.83] and GMWMr = [1.77-2.06] for non-eroded VOI, and for PSFOFF, RCGMe = [0.92-0.99] and GMWMr(e) = [3.14-3.68] for eroded VOI and RCGM = [0.75-0.81] and GMWMr = [1.72-1.95] for non-eroded VOI.Conclusions To achieve inter-scanner comparability, we propose selecting reconstruction settings based on RCGMe and GMWMr(e) as specified in "Results". These proposed standards should be tested prospectively to validate and/or refine the harmonisation criteria. Show less
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before... Show moreExisting quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Show less
The aim of this work was to quantify the uptake of F-18-BMS-986192, a programmed cell death ligand 1 (PD-L1) adnectin PET tracer, in patients with non-small cell lung cancer. To this end, plasma... Show moreThe aim of this work was to quantify the uptake of F-18-BMS-986192, a programmed cell death ligand 1 (PD-L1) adnectin PET tracer, in patients with non-small cell lung cancer. To this end, plasma input kinetic modeling of dynamic tumor uptake data with online arterial blood sampling was performed. In addition, the accuracy of simplified uptake metrics such as SUV was investigated. Methods: Data from a study with F-18-BMS-986192 in patients with advanced-stage non-small cell lung cancer eligible for nivolumab treatment were used if a dynamic scan was available and lesions were present in the field of view of the dynamic scan. After injection of F-18-BMS-986192, a 60-min dynamic PET/CT scan was started, followed by a 30-min whole-body PET/CT scan. Continuous arterial and discrete arterial and venous blood sampling were performed to determine a plasma input function. Tumor time-activity curves were fitted by several plasma input kinetic models. Simplified uptake parameters included tumor-to-blood ratio as well as several SUV measures. Results: Twenty-two tumors in 9 patients were analyzed. The arterial plasma input single-tissue reversible compartment model with fitted blood volume fraction seems to be the most preferred model as it best fitted 11 of 18 tumor time-activity curves. The distribution volume (V-T) ranged from 0.4 to 4.8 mL.cm(-3). Similar values were obtained with an image-derived input function. From the simplified measures, SUV normalized for body weight at 50 and 67 min after injection correlated best with V-T, with an R-2 of more than 0.9. Conclusion: A single-tissue reversible model can be used to quantify tumor uptake of the PD-L1 PET tracer F-18-BMS-986192. SUV at 60 min after injection, normalized for body weight, is an accurate simplified parameter for uptake assessment of baseline studies. To assess its predictive value for response evaluation during programmed cell death protein 1 or PD-L1 immune checkpoint inhibition, further validation of SUV against V-T based on an image-derived input function is recommended. Show less
OBJECTIVES This study compared the performance of the quantitative flow ratio (QFR) with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) myocardial... Show moreOBJECTIVES This study compared the performance of the quantitative flow ratio (QFR) with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) myocardial perfusion imaging (MPI) for the diagnosis of fractional flow reserve (FFR)-defined coronary artery disease (CAD).BACKGROUND QFR estimates FFR solely based on cine contrast images acquired during invasive coronary angiography (ICA). Head-to-head studies comparing QFR with noninvasive MPI are lacking.METHODS A total of 208 (624 vessels) patients underwent technetium -99m tetrofosmin SPECT and [15O]H2O PET imaging before ICA in conjunction with FFR measurements. ICA was obtained without using a dedicated QFR acquisition protocol, and QFR computation was attempted in all vessels interrogated by FFR (552 vessels).RESULTS QFR computation succeeded in 286 (52%) vessels. QFR correlated well with invasive FFR overall (R = 0.79; p < 0.001) and in the subset of vessels with an intermediate (30% to 90%) diameter stenosis (R = 0.76; p < 0.001). Overall, per-vessel analysis demonstrated QFR to exhibit a superior sensitivity (70%) in comparison with SPECT (29%; p < 0.001), whereas it was similar to PET (75%; p = 1.000). Specificity of QFR (93%) was higher than PET (79%; p < 0.001) and not different from SPECT (96%; p = 1.000). As such, the accuracy of QFR (88%) was superior to both SPECT (82%; p = 0.010) and PET (78%; p = 0.004). Lastly, the area under the receiver operating characteristics curve of QFR, in the overall sample (0.94) and among vessels with an intermediate lesion (0.90) was higher than SPECT (0.63 and 0.61; p < 0.001 for both) and PET (0.82; p < 0.001 and 0.77; p = 0.002), respectively.CONCLUSIONS In this head-to-head comparative study, QFR exhibited a higher diagnostic value for detecting FFRdefined significant CAD compared with perfusion imaging by SPECT or PET. (J Am Coll Cardiol Img 2020;13:1976-85) (c) 2020 by the American College of Cardiology Foundation. Show less
Mes, S.W.; Velden, F.H.P. van; Peltenburg, B.; Peeters, C.F.W.; Beest, D.E. te; Wiel, M.A. van de; ... ; Graaf, P. de 2020
Objectives Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic... Show moreObjectives Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. Materials and Methods Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). Results In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). Conclusions MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. Show less
Zwanenburg, A.; Vallieres, M.; Abdalah, M.A.; Aerts, H.J.W.L.; Andrearczyk, V.; Apte, A.; ... ; Lock, S. 2020
Background: Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use... Show moreBackground: Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use.Purpose: To standardize a set of 174 radiomic features.Materials and Methods: Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features.Results: Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI).Conclusion: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. (C) RSNA, 2020 Show less
Pfaehler, E.; Sluis, J. van; Merema, B.B.J.; Ooijen, P. van; Berendsen, R.C.M.; Velden, F.H.P. van; Boellaard, R. 2020
The sensitivity of radiomic features to several confounding factors, such as reconstruction settings, makes clinical use challenging. To investigate the impact of harmonized image reconstructions... Show moreThe sensitivity of radiomic features to several confounding factors, such as reconstruction settings, makes clinical use challenging. To investigate the impact of harmonized image reconstructions on feature consistency, a multicenter phantom study was performed using 3-dimensionally printed phantom inserts reflecting realistic tumor shapes and heterogeneity uptakes. Methods: Tumors extracted from real PET/CT scans of patients with non-small cell lung cancer served as model for three 3-dimensionally printed inserts. Different heterogeneity pattern were realized by printing separate compartments that could be filled with different activity solutions. The inserts were placed in the National Electrical Manufacturers Association image-quality phantom and scanned various times. First, a list-mode scan was acquired and 5 statistically equal replicates were reconstructed. Second, the phantom was scanned 4 times on the same scanner. Third, the phantom was scanned on 6 PET/CT systems. All images were reconstructed using EANM Research Ltd. (EARL)-compliant and locally clinically preferred reconstructions. EARL-compliant reconstructions were performed without (EARL1) or with (EARL2) point-spread function. Images were analyzed with and without resampling to 2-mm cubic voxels. Images were discretized with a fixed bin width (FBW) of 0.25 and a fixed bin number (FBN) of 64. The intraclass correlation coefficient (ICC) of each scan setup was calculated and compared across reconstruction settings. An ICC above 0.75 was regarded as high. Results: The percentage of features yielding a high ICC was largest for the statistically equal replicates (70%-91% for FBN; 90%-96% for FBW discretization). For scans acquired on the same system, the percentage decreased, but most features still resulted in a high ICC (FBN, 52%-63%; FBW, 75%-85%). The percentage of features yielding a high ICC decreased more in the multicenter setting. In this case, the percentage of features yielding a high ICC was larger for images reconstructed with EARL-compliant reconstructions: for example, 40% for EARL1 and 60% for EARL2 versus 21% for the clinically preferred setting for FBW discretization. When discretized with FBW and resampled to isotropic voxels, this benefit was more pronounced. Conclusion: EARL-compliant reconstructions harmonize a wide range of radiomic features. FBW discretization and a sampling to isotropic voxels enhances the benefits of EARL-compliant reconstructions. Show less
