Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images:Dosimetric validation and reader study based clinical acceptability testing

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Segmentation uncertainty estimation as a sanity check for image biomarker studies

SIMPLE SUMMARY: Radiomics is referred to as quantitative image biomarker analysis. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, the radiomic biomarkers lack reproducibility. In this manuscript, we show how this protocol-induced uncertainty can drastically reduce prognostic model performance and propose some insights on how to use it for developing better prognostic models. ABSTRACT: Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lack reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients, respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure of less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on the Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability of segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p = 0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty, to effect ratio, only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with a log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain in its predictions for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation, without considering segmentation, uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing)

Vascular responses to radiotherapy and androgen-deprivation therapy in experimental prostate cancer

Background: Radiotherapy (RT) and androgen-deprivation therapy (ADT) are standard treatments for advanced prostate cancer (PC). Tumor vascularization is recognized as an important physiological feature likely to impact on both RT and ADT response, and this study therefore aimed to characterize the vascular responses to RT and ADT in experimental PC. Methods: Using mice implanted with CWR22 PC xenografts, vascular responses to RT and ADT by castration were visualized in vivo by DCE MRI, before contrast-enhancement curves were analyzed both semi-quantitatively and by pharmacokinetic modeling. Extracted image parameters were correlated to the results from ex vivo quantitative fluorescent immunohistochemical analysis (qIHC) of tumor vascularization (9 F1), perfusion (Hoechst 33342), and hypoxia (pimonidazole), performed on tissue sections made from tumors excised directly after DCE MRI. Results: Compared to untreated (Ctrl) tumors, an improved and highly functional vascularization was detected in androgen-deprived (AD) tumors, reflected by increases in DCE MRI parameters and by increased number of vessels (VN), vessel density ( VD), and vessel area fraction ( VF) from qIHC. Although total hypoxic fractions ( HF) did not change, estimated acute hypoxia scores ( AHS) – the proportion of hypoxia staining within 50 μm from perfusion staining – were increased in AD tumors compared to in Ctrl tumors. Five to six months after ADT renewed castration-resistant (CR) tumor growth appeared with an even further enhanced tumor vascularization. Compared to the large vascular changes induced by ADT, RT induced minor vascular changes. Correlating DCE MRI and qIHC parameters unveiled the semi-quantitative parameters area under curve ( AUC) from initial time-points to strongly correlate with VD and VF, whereas estimation of vessel size ( VS) by DCE MRI required pharmacokinetic modeling. HF was not correlated to any DCE MRI parameter, however, AHS may be estimated after pharmacokinetic modeling. Interestingly, such modeling also detected tumor necrosis very strongly. Conclusions: DCE MRI reliably allows non-invasive assessment of tumors’ vascular function. The findings of increased tumor vascularization after ADT encourage further studies into whether these changes are beneficial for combined RT, or if treatment with anti-angiogenic therapy may be a strategy to improve the therapeutic efficacy of ADT in advanced PC.publishedVersio

Tumor microenvironmental changes induced by the sulfamate carbonic anhydrase IX inhibitor S4 in a laryngeal tumor model

BACKGROUND AND PURPOSE: Carbonic anhydrase IX (CAIX) plays a pivotal role in pH homeostasis, which is essential for tumor cell survival. We examined the effect of the CAIX inhibitor 4-(3'(3",5"-dimethylphenyl)-ureido)phenyl sulfamate (S4) on the tumor microenvironment in a laryngeal tumor model by analyzing proliferation, apoptosis, necrosis, hypoxia, metabolism and CAIX ectodomain shedding. METHODS: SCCNij202 tumor bearing-mice were treated with S4 for 1, 3 or 5 days. CAIX ectodomain shedding was measured in the serum after therapy. Effects on tumor cell proliferation, apoptosis, necrosis, hypoxia (pimonidazole) and CAIX were investigated with quantitative immunohistochemistry. Metabolic transporters and enzymes were quantified with qPCR. RESULTS: CAIX ectodomain shedding decreased after treatment with S4 (p<0.01). S4 therapy did neither influence tumor cell proliferation nor the amount of apoptosis and necrosis. Hypoxia (pimonidazole) and CAIX expression were also not affected by S4. CHOP and MMP9 mRNA as a reference of intracellular pH did not change upon treatment with S4. Compensatory mechanisms of pH homeostasis at the mRNA level were not observed. CONCLUSION: As the clinical and biological meaning of the decrease in CAIX ectodomain shedding after S4 therapy is not clear, studies are required to elucidate whether the CAIX ectodomain has a paracrine or autocrine signaling function in cancer biology. S4 did not influence the amount of proliferation, apoptosis, necrosis and hypoxia. Therefore, it is unlikely that S4 can be used as single agent to influence tumor cell kill and proliferation, and to target primary tumor growth

Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients : evaluation of the added prognostic value for overall survival and locoregional recurrence

Background and purpose: The prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been developed that includes the change of radiomic features during treatment. Therefore, the aim of this study was to investigate the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC patients. Materials and methods: This retrospective study includes a training dataset of 141 stage I-IV NSCLC patients and three external validation datasets of 94, 61 and 41 patients, all treated with curative intended (chemo) radiotherapy. The change of radiomic features extracted from CBCT images was summarized as the slope of a linear regression. The CBCT slope-features and CT-extracted features were used as input for a Cox proportional hazards model. Moreover, prognostic performance of clinical parameters was investigated for overall survival and locoregional recurrence. Model performances were assessed using the Kaplan-Meier curves and c-index. Results: The radiomics model contained only CT-derived features and reached a c-index of 0.63 for overall survival and could be validated on the first validation dataset. No model for locoregional recurrence could be developed that validated on the validation datasets. The clinical parameters model could not be validated for either overall survival or locoregional recurrence. Conclusion: In this study we could not confirm our hypothesis that longitudinal CBCT-extracted radiomic features contribute to improved prognostic information. Moreover, performance of baseline radiomic features or clinical parameters was poor, probably affected by heterogeneity within and between datasets

The role of 18F-FDG PET in the differentiation between lung metastases and synchronous second primary lung tumours

Contains fulltext : 87717.pdf (publisher's version ) (Closed access)PURPOSE: In lung cancer patients with multiple lesions, the differentiation between metastases and second primary tumours has significant therapeutic and prognostic implications. The aim of this retrospective study was to investigate the potential of (18)F-FDG PET to discriminate metastatic disease from second primary lung tumours. METHODS: Of 1,396 patients evaluated by the thoracic oncology group between January 2004 and April 2009 at the Radboud University Nijmegen Medical Centre, patients with a synchronous second primary lung cancer were selected. Patients with metastatic disease involving the lungs served as the control group. Maximum standardized uptake values (SUVs) measured with (18)F-FDG PET were determined for two tumours in each patient. The relative difference between the SUVs of these tumours (SUV) was determined and compared between the second primary group and metastatic disease group. Receiver-operating characteristic (ROC) curve analysis was performed to determine the sensitivity and specificity of the SUV for an optimal cut-off value. RESULTS: A total of 37 patients (21 metastatic disease, 16 second primary cancer) were included for analysis. The SUV was significantly higher in patients with second primary cancer than in those with metastatic disease (58 vs 28%, respectively, p < 0.001). The area under the ROC curve was 0.81 and the odds ratio for the optimal cut-off was 18.4. CONCLUSION: SUVs from (18)F-FDG PET images can be helpful in differentiating metastatic disease from second primary tumours in patients with synchronous pulmonary lesions. Further studies are warranted to confirm the consistency of these results.1 november 201

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train

Background and purpose Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy

Role of Mobility Strategy in moderating the effect Of ERP performance to operational performance: (Study in Indonesian palm oil plantation industries)

Introduction: Inter-observer variability (IOV) in target volume delineation is a well-documented source of geometric uncertainty in radiotherapy. Such variability has not yet been explored in the context of adaptive re-delineation based on imaging data acquired during treatment. We compared IOV in the pre- and mid-treatment setting using expert primary gross tumour volume (GTV) and clinical target volume (CTV) delineations in locoregionally advanced head-and-neck squamous cell carcinoma (HNSCC) and (non-)small cell lung cancer [(N)SCLC]. Material and methods: Five and six observers participated in the HNSCC and (N)SCLC arm, respectively, and provided delineations for five cases each. Imaging data consisted of CT studies partly complemented by FDG-PET and was provided in two separate phases for pre- and mid-treatment. Global delineation compatibility was assessed with a volume overlap metric (the Generalised Conformity Index), while local extremes of IOV were identified through the standard deviation of surface distances from observer delineations to a median consensus delineation. Details of delineation procedures, in particular, GTV to CTV expansion and adaptation strategies, were collected through a questionnaire. Results: Volume overlap analysis revealed a worsening of IOV in all but one case per disease site, which failed to reach significance in this small sample (p-value range .063-.125). Changes in agreement were propagated from GTV to CTV delineations, but correlation could not be formally demonstrated. Surface distance based analysis identified longitudinal target extent as a pervasive source of disagreement for HNSCC. High variability in (N)SCLC was often associated with tumours abutting consolidated lung tissue or potentially invading the mediastinum. Adaptation practices were variable between observers with fewer than half stating that they consistently adapted pre-treatment delineations during treatment. Conclusion: IOV in target volume delineation increases during treatment, where a disparity in institutional adaptation practices adds to the conventional causes of IOV. Consensus guidelines are urgently needed

Distributed radiomics as a signature validation study using the Personal Health Train infrastructure

Contains fulltext : 209427.pdf (publisher's version ) (Open Access