ObjectiveValidation of automated 2-dimensional (2D) diameter measurements of vestibular schwannomas on magnetic resonance imaging (MRI). Study DesignRetrospective validation study using 2 data sets... Show moreObjectiveValidation of automated 2-dimensional (2D) diameter measurements of vestibular schwannomas on magnetic resonance imaging (MRI). Study DesignRetrospective validation study using 2 data sets containing MRIs of vestibular schwannoma patients. SettingUniversity Hospital in The Netherlands. MethodsTwo data sets were used, 1 containing 1 scan per patient (n = 134) and the other containing at least 3 consecutive MRIs of 51 patients, all with contrast-enhanced T1 or high-resolution T2 sequences. 2D measurements of the maximal extrameatal diameters in the axial plane were automatically derived from a 3D-convolutional neural network compared to manual measurements by 2 human observers. Intra- and interobserver variabilities were calculated using the intraclass correlation coefficient (ICC), agreement on tumor progression using Cohen's kappa. ResultsThe human intra- and interobserver variability showed a high correlation (ICC: 0.98-0.99) and limits of agreement of 1.7 to 2.1 mm. Comparing the automated to human measurements resulted in ICC of 0.98 (95% confidence interval [CI]: 0.974; 0.987) and 0.97 (95% CI: 0.968; 0.984), with limits of agreement of 2.2 and 2.1 mm for diameters parallel and perpendicular to the posterior side of the temporal bone, respectively. There was satisfactory agreement on tumor progression between automated measurements and human observers (Cohen's & kappa; = 0.77), better than the agreement between the human observers (Cohen's & kappa; = 0.74). ConclusionAutomated 2D diameter measurements and growth detection of vestibular schwannomas are at least as accurate as human 2D measurements. In clinical practice, measurements of the maximal extrameatal tumor (2D) diameters of vestibular schwannomas provide important complementary information to total tumor volume (3D) measurements. Combining both in an automated measurement algorithm facilitates clinical adoption. Show less
Kalisvaart, G.M.; Berghe, T. van den; Grootjans, W.; Lejoly, M.; Huysse, W.C.J.; Bovée, J.V.M.G.; ... ; Bloem, J.L. 2023
ObjectiveTo identify which dynamic contrast-enhanced (DCE-)MRI features best predict histological response to neoadjuvant chemotherapy in patients with an osteosarcoma.MethodsPatients with... Show moreObjectiveTo identify which dynamic contrast-enhanced (DCE-)MRI features best predict histological response to neoadjuvant chemotherapy in patients with an osteosarcoma.MethodsPatients with osteosarcoma who underwent DCE-MRI before and after neoadjuvant chemotherapy prior to resection were retrospectively included at two different centers. Data from the center with the larger cohort (training cohort) was used to identify which method for region-of-interest selection (whole slab or focal area method) and which change in DCE-MRI features (time to enhancement, wash-in rate, maximum relative enhancement and area under the curve) gave the most accurate prediction of histological response. Models were created using logistic regression and cross-validated. The most accurate model was then externally validated using data from the other center (test cohort).ResultsFifty-five (27 poor response) and 30 (19 poor response) patients were included in training and test cohorts, respectively. Intraclass correlation coefficient of relative DCE-MRI features ranged 0.81–0.97 with the whole slab and 0.57–0.85 with the focal area segmentation method. Poor histological response was best predicted with the whole slab segmentation method using a single feature threshold, relative wash-in rate <2.3. Mean accuracy was 0.85 (95%CI: 0.75–0.95), and area under the receiver operating characteristic curve (AUC-index) was 0.93 (95%CI: 0.86–1.00). In external validation, accuracy and AUC-index were 0.80 and 0.80.ConclusionIn this study, a relative wash-in rate of <2.3 determined with the whole slab segmentation method predicted histological response to neoadjuvant chemotherapy in osteosarcoma. Consistent performance was observed in an external test cohort. Show less
Background: Oncological sigmoid and rectal resections are accompanied with substantial risk of anastomotic leakage. Preoperative risk assessment and patient selection remain difficult, highlighting... Show moreBackground: Oncological sigmoid and rectal resections are accompanied with substantial risk of anastomotic leakage. Preoperative risk assessment and patient selection remain difficult, highlighting the importance of finding easy-to-use parameters. This study evaluates the prognostic value of contrast-enhanced (CE) computed tomography (CT)-based muscle measurements for predicting anastomotic leakage. Methods: Patients that underwent oncological sigmoid and rectal resections in the LUMC between 2016 and 2020 were included. Preoperative CE-CT scans, were analyzed using Vitrea software to measure total abdominal muscle area (TAMA) and total psoas area (TPA). Muscle areas were standardized using patient's height into: psoas muscle index (PMI) and skeletal muscle index (SMI) (cm(2)/m(2)). Results: In total 46 patients were included, of which 13 (8.9%) suffered from anastomotic leakage. Patients with anastomotic leakage had a significantly lower PMI (22.1 vs. 25.1, p < 0.01) and SMI (41.8 vs. 46.6, p < 0.01). After adjusting for confounders (age and comorbidity), lower PMI (odds ratio [OR]: 0.85, 95% confidence interval [CI] 0.71-0.99, p = 0.03) and SMI (OR: 0.93, 95%CI 0.86-0.99, p = 0.02) were both associated with anastomotic leakage. Conclusion: This study showed that lower PMI and SMI were associated with anastomotic leakage. These results indicate that preoperative CT-based muscle measurements can be used as prognostic factor for risk stratification for anastomotic leakage. Show less
Purpose: To investigate the time and effort needed to perform vertebral morphometry, as well as inter-observer agreement for identification of vertebral fractures on vertebral fracture assessment ... Show morePurpose: To investigate the time and effort needed to perform vertebral morphometry, as well as inter-observer agreement for identification of vertebral fractures on vertebral fracture assessment (VFA) images. Methods: Ninety-six images were retrospectively selected, and three radiographers independently performed semi-automatic 6-point morphometry. Fractures were identified and graded using the Genant classification. Time needed to annotate each image was recorded, and reader fatigue was assessed using a modified Simulator Sickness Questionnaire (SSQ). Inter-observer agreement was assessed per-patient and per-vertebra for detecting fractures of all grades (grades 1-3) and for grade 2 and 3 fractures using the kappa statistic. Variability in measured vertebral height was evaluated using the intraclass correlation coefficient (ICC). Results: Per-patient agreement was 0.59 for grades 1-3 fracture detection, and 0.65 for grades 2-3 only. Agreement for per-vertebra fracture classification was 0.92. Vertebral height measurements had an ICC of 0.96. Time needed to annotate VFA images ranged between 91 and 540 s, with a mean annotation time of 259 s. Mean SSQ scores were significantly lower at the start of a reading session (1.29; 95% CI: 0.81-1.77) compared to the end of a session (3.25; 95% CI: 2.60-3.90; p < 0.001). Conclusion: Agreement for detection of patients with vertebral fractures was only moderate, and vertebral morphometry requires substantial time investment. This indicates that there is a potential benefit for automating VFA, both in improving inter-observer agreement and in decreasing reading time and burden on readers. Show less
Grootjans, W.; Rietbergen, D.D.D.; Velden, F.H.P. van 2022
Positron emission tomography (PET) is an important imaging modality for personalizing clinical management of patients with lung cancer. In this regard, PET imaging is essential for adequate... Show morePositron emission tomography (PET) is an important imaging modality for personalizing clinical management of patients with lung cancer. In this regard, PET imaging is essential for adequate clinical staging and monitoring of treatment response in patients with lung cancer. The key advantage of PET over other radiological imaging modalities is its high sensitivity for the detection of pulmonary lesions, normal-sized metastatic hilar and/or mediastinal lymph nodes, and distant metastases. Furthermore, with increasing clinical evidence, the role of PET imaging for treatment selection, adaptation, early response mon-itoring and follow up in patients with lung cancer is being increasingly recognized. At the heart of PET imaging lies the ability to visualize and quantify numerous biological parame-ters that are responsible for treatment resistance. In order to ensure accurate and repro-ducible image quantification, harmonization of patient preparation and imaging protocols is essential. Additionally, there are several technical factors during PET scanning that have to be taken care of to safeguard image quality and quantitative accuracy. One of these factors is the occurrence of respiratory motion artifacts, which is a well-known fac-tor that can significantly influence image quality and quantitative accuracy of PET images. If left uncorrected, respiratory motion artifacts can introduce uncertainties in diagnosis and staging, inaccuracies in definition of target volumes for radiation treatment planning, and hinder adequate monitoring of therapy response. Although many different respiratory gating techniques have been developed to correct PET images for respiratory motion arti-facts, respiratory gating has traditionally not been widely adopted in clinical practice. This is due to the fact that these methods tend to be disruptive for the clinical workflow due the lengthening of image acquisition times, higher amounts of activity being administered to the patient, and the requirement to synchronize additional hardware with the scanner. Developments in respiratory gating techniques over the last years have resulted in considerable technical improvements. These newer respiratory gating techniques can operate directly on the acquired PET data without the use of additional hardware to trace respiratory motion and can be seamlessly applied into clinical routine. Furthermore, instead of only using a fraction of the acquired PET data newer methods have the ability to use all of the acquired PET data for image reconstruction, thereby improving image quality. The clinically added value of respiratory gating lies in improving image quality by reducing the amount of respiration-induced image blurring. This considerably improves the detection and characterization of small lesions, potentially improving early diagnosis and staging of patients with lung cancer. Furthermore, the incorpo-ration of (4D) respiratory gated PET for radiotherapy purposes has shown to improve target vol-ume definition through more accurate tracking of tumor motion. In addition, the effect of respiratory motion artifacts on widely used volumetric and uptake parameters in PET have been described. Although respiratory gating improves quantitative accuracy of PET images, the exact impact of these corrections on clinical management of patients with lung cancer often still needs to be determined. Show less
Weeda, Y.A.; Kalisvaart, G.M.; Velden, F.H.P. van; Gelderblom, H.; Molen, A.J. van der; Bovee, J.V.M.G.; ... ; Geus-Oei, L.F. de 2022
Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic... Show moreGastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic disease. It is important to assess the efficacy of TKI treatment at an early stage to optimize therapy strategies and eliminate futile ineffective treatment, side effects and unnecessary costs. This systematic review provides an overview of the imaging features obtained from contrast-enhanced (CE)-CT and 2-deoxy-2-[F-18]fluoro-D-glucose ([F-18]FDG) PET/CT to predict and monitor TKI treatment response in GIST patients. PubMed, Web of Science, the Cochrane Library and Embase were systematically screened. Articles were considered eligible if quantitative outcome measures (area under the curve (AUC), correlations, sensitivity, specificity, accuracy) were used to evaluate the efficacy of imaging features for predicting and monitoring treatment response to various TKI treatments. The methodological quality of all articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies, v2 (QUADAS-2) tool and modified versions of the Radiomics Quality Score (RQS). A total of 90 articles were included, of which 66 articles used baseline [F-18]FDG-PET and CE-CT imaging features for response prediction. Generally, the presence of heterogeneous enhancement on baseline CE-CT imaging was considered predictive for high-risk GISTs, related to underlying neovascularization and necrosis of the tumor. The remaining articles discussed therapy monitoring. Clinically established imaging features, including changes in tumor size and density, were considered unfavorable monitoring criteria, leading to under- and overestimation of response. Furthermore, changes in glucose metabolism, as reflected by [F-18]FDG-PET imaging features, preceded changes in tumor size and were more strongly correlated with tumor response. Although CE-CT and [F-18]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended. Show less
Neve, O.M.; Chen, Y.J.; Tao, Q.; Romeijn, S.R.; Boer, N.P. de; Grootjans, W.; ... ; Staring, M. 2022
Purpose: To develop automated vestibular schwannoma measurements on contrast-enhanced T1- and T2-weighted MRI scans.Materials and Methods: MRI data from 214 patients in 37 different centers were... Show morePurpose: To develop automated vestibular schwannoma measurements on contrast-enhanced T1- and T2-weighted MRI scans.Materials and Methods: MRI data from 214 patients in 37 different centers were retrospectively analyzed between 2020 and 2021. Patients with hearing loss (134 positive for vestibular schwannoma [mean age 6 SD, 54 years 6 12; 64 men] and 80 negative for vestibular schwannoma) were randomly assigned to a training and validation set and to an independent test set. A convolutional neural network (CNN) was trained using fivefold cross-validation for two models (T1 and T2). Quantitative analysis, including Dice index, Hausdorff distance, surface-to-surface distance (S2S), and relative volume error, was used to compare the computer and the human delineations. An observer study was performed in which two experienced physicians evaluated both delineations.Results: The T1-weighted model showed state-of-the-art performance, with a mean S2S distance of less than 0.6 mm for the whole tumor and the intrameatal and extrameatal tumor parts. The whole tumor Dice index and Hausdorff distance were 0.92 and 2.1 mm in the independent test set, respectively. T2-weighted images had a mean S2S distance less than 0.6 mm for the whole tumor and the intrameatal and extrameatal tumor parts. The whole tumor Dice index and Hausdorff distance were 0.87 and 1.5 mm in the independent test set. The observer study indicated that the tool was similar to human delineations in 85%-92% of cases.Conclusion: The CNN model detected and delineated vestibular schwannomas accurately on contrast-enhanced T1- and T2-weighted MRI scans and distinguished the clinically relevant difference between intrameatal and extrameatal tumor parts. (C) RSNA, 2022 Show less
Background: Accuracy and precision assessment in radiomic features is important for the determination of their potential to characterize cancer lesions. In this regard, simulation of different... Show moreBackground: Accuracy and precision assessment in radiomic features is important for the determination of their potential to characterize cancer lesions. In this regard, simulation of different imaging conditions using specialized phantoms is increasingly being investigated. In this study, the design and evaluation of a modular multimodality imaging phantom to simulate heterogeneous uptake and enhancement patterns for radiomics quantification in hybrid imaging is presented. Methods: A modular multimodality imaging phantom was constructed that could simulate different patterns of heterogeneous uptake and enhancement patterns in positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT), and magnetic resonance (MR) imaging. The phantom was designed to be used as an insert in the standard NEMA-NU2 IEC body phantom casing. The entire phantom insert is composed of three segments, each containing three separately fillable compartments. The fillable compartments between segments had different sizes in order to simulate heterogeneous patterns at different spatial scales. The compartments were separately filled with different ratios of Tc-99m-pertechnetate, F-18-fluorodeoxyglucose ([F-18]FDG), iodine- and gadolinium-based contrast agents for SPECT, PET, CT, and T-1-weighted MR imaging respectively. Image acquisition was performed using standard oncological protocols on all modalities and repeated five times for repeatability assessment. A total of 93 radiomic features were calculated. Variability was assessed by determining the coefficient of quartile variation (CQV) of the features. Comparison of feature repeatability at different modalities and spatial scales was performed using Kruskal-Wallis-, Mann-Whitney U-, one-way ANOVA- and independent t-tests. Results: Heterogeneous uptake and enhancement could be simulated on all four imaging modalities. Radiomic features in SPECT were significantly less stable than in all other modalities. Features in PET were significantly less stable than in MR and CT. A total of 20 features, particularly in the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) class, were found to be relatively stable in all four modalities for all three spatial scales of heterogeneous patterns (with CQV < 10%). Conclusion: The phantom was suitable for simulating heterogeneous uptake and enhancement patterns in [F-18]FDG-PET, Tc-99m-SPECT, CT, and T-1-weighted MR images. The results of this work indicate that the phantom might be useful for the further development and optimization of imaging protocols for radiomic quantification in hybrid imaging modalities. Show less
Kalisvaart, G.M.; Grootjans, W.; Bovee, J.V.M.G.; Gelderblom, H.; Hage, J.A. van der; Sande, M.A.J. van de; ... ; Geus-Oei, L.F. de 2021
Background: Prognostic biomarkers are pivotal for adequate treatment decision making. The objective of this study was to determine the added prognostic value of quantitative [F-18]FDG-PET features... Show moreBackground: Prognostic biomarkers are pivotal for adequate treatment decision making. The objective of this study was to determine the added prognostic value of quantitative [F-18]FDG-PET features in patients with metastases from soft tissue sarcoma (STS). Methods: Patients with metastases from STS, detected by (re)staging [F-18]FDG-PET/CT at Leiden University Medical Centre, were retrospectively included. Clinical and histopathological patient characteristics and [F-18]FDG-PET features (SUVmax, SUVpeak, SUVmean, total lesion glycolysis, and metabolic tumor volume) were analyzed as prognostic factors for overall survival using a Cox proportional hazards model and Kaplan-Meier methods. Results: A total of 31 patients were included. SUVmax and SUVpeak were significantly predictive for overall survival (OS) in a univariate analysis (p = 0.004 and p = 0.006, respectively). Hazard ratios (HRs) were 1.16 per unit increase for SUVmax and 1.20 per unit for SUVpeak. SUVmax and SUVpeak remained significant predictors for overall survival after correction for the two strongest predictive clinical characteristics (number of lesions and performance status) in a multivariate analysis (p = 0.02 for both). Median SUVmax and SUVpeak were 5.7 and 4.9 g/mL, respectively. The estimated mean overall survival in patients with SUVmax > 5.7 g/mL was 14 months; otherwise, it was 39 months (p < 0.001). For patients with SUVpeak > 4.9 g/mL, the estimated mean overall survival was 18 months; otherwise, it was 33 months (p = 0.04). Conclusions: In this study, SUVmax and SUVpeak were independent prognostic factors for overall survival in patients with metastases from STS. These results warrant further investigation of metabolic imaging with [F-18]FDG-PET/CT in patients with metastatic STS. Show less
Kalisvaart, G.M.; Bloem, J.L.; Bovee, J.V.M.G.; Sande, M.A.J. van de; Gelderblom, H.; Hage, J.A. van der; ... ; Grootjans, W. 2021
Over the last decades, technological developments in the field of radiology have resulted in a widespread use of imaging for personalising medicine in oncology, including patients with a sarcoma.... Show moreOver the last decades, technological developments in the field of radiology have resulted in a widespread use of imaging for personalising medicine in oncology, including patients with a sarcoma. New scanner hardware, imaging protocols, image reconstruction algorithms, radiotracers, and contrast media, enabled the assessment of the physical and biological properties of tumours associated with response to treatment. In this context, medical imaging has the potential to select sarcoma patients who do not benefit from (neo-)adjuvant treatment and facilitate treatment adaptation. Due to the biological heterogeneity in sarcomas, the challenge at hand is to acquire a practicable set of imaging features for specific sarcoma subtypes, allowing response assessment. This review provides a comprehensive overview of available clinical data on imaging-based response monitoring in sarcoma patients and future research directions. Eventually, it is expected that imaging-based response monitoring will help to achieve successful modification of (neo)adjuvant treatments and improve clinical care for these patients. (C) 2021 The Royal College of Radiologists. Published by Elsevier Ltd. Show less
Noortman, W.A.; Vriens, D.; Grootjans, W.; Tao, Q.; Geus-Oei, L.F. de; Velden, F.H. van 2020
In recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of... Show moreIn recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of handcrafted features combined with sophisticated statistical methods or machine learning algorithms for modelling, or deep learning algorithms that both learn features from raw data and perform modelling. These features have the potential to serve as non-invasive biomarkers for tumor characterization, prognostic stratification and response prediction. thereby contributing to precision medicine. However, especially in nuclear medicine, variable results are obtained when using radiomics for these purposes. Individual studies show promising results, but due to small numbers of patients per study and little standardization, it is difficult to compare and validate results on other datasets. This review describes the radiomic pipeline, its applications and the increasing role of artificial intelligence within the field. Furthermore, the challenges that need to be overcome to achieve clinical translation are discussed, so that, eventually, radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine, by providing the right treatment to the right patient, with the right dose. at the right time. Show less
Grootjans, W.; Kok, P.; Butter, J.; Aarntzen, E. 2020
Positron emission tomography (PET) combined with X-ray computed tomography (CT) is an important molecular imaging platform that is required for accurate diagnosis and clinical staging of a variety... Show morePositron emission tomography (PET) combined with X-ray computed tomography (CT) is an important molecular imaging platform that is required for accurate diagnosis and clinical staging of a variety of diseases. The advantage of PET imaging is the ability to visualize and quantify a myriad of biological processes in vivo with high sensitivity and accuracy. However, there are multiple factors that determine image quality and quantitative accuracy of PET images. One of the foremost factors influencing image quality in PET imaging of the thorax and upper abdomen is respiratory motion, resulting in respiration-induced motion blurring of anatomical structures. Correction of these artefacts is required for providing optimal image quality and quantitative accuracy of PET images.Several respiratory gating techniques have been developed, typically relying on acquisition of a respiratory signal simultaneously with PET data. Based on the respiratory signal acquired, PET data is selected for reconstruction of a motion-free image. Although these methods have been shown to effectively remove respiratory motion artefacts from PET images, the performance is dependent on the quality of the respiratory signal being acquired. In this study, the use of an amplitude-based optimal respiratory gating (ORG) algorithm is discussed. In contrast to many other respiratory gating algorithms, ORG permits the user to have control over image quality versus the amount of rejected motion in the reconstructed PET images. This is achieved by calculating an optimal amplitude range based on the acquired surrogate signal and a user-specified duty cycle (the percentage of PET data used for image reconstruction). The optimal amplitude range is defined as the smallest amplitude range still containing the amount of PET data required for image reconstruction. It was shown that ORG results in effective removal of respiration-induced image blurring in PET imaging of the thorax and upper abdomen, resulting in improved image quality and quantitative accuracy. Show less
Vos, C.S. van der; Meeuwis, A.P.W.; Grootjans, W.; Geus-Oei, L.F. de; Visser, E.P. 2019
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