High-grade mesenchymal pancreatic ductal adenocarcinoma drives stromal deactivation through CSF-1.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an abundance of stroma. Multiple molecular classification efforts have identified a mesenchymal tumor subtype that is consistently characterized by high-grade growth and poor clinical outcome. The relation between PDAC stroma and tumor subtypes is still unclear. Here, we aimed to identify how PDAC cells instruct the main cellular component of stroma, the pancreatic stellate cells (PSCs). We found in primary tissue that high-grade PDAC had reduced collagen deposition compared to low-grade PDAC. Xenografts and organotypic co-cultures established from mesenchymal-like PDAC cells featured reduced collagen and activated PSC content. Medium transfer experiments using a large set of PDAC cell lines revealed that mesenchymal-like PDAC cells consistently downregulated ACTA2 and COL1A1 expression in PSCs and reduced proliferation. We identified colony-stimulating factor 1 as the mesenchymal PDAC-derived ligand that deactivates PSCs, and inhibition of its receptor CSF1R was able to counteract this effect. In conclusion, high-grade PDAC features stroma that is low in collagen and activated PSC content, and targeting CSF1R offers direct options to maintain a tumor-restricting microenvironment.

Added value of 3T MRI and the MRI-halo sign in assessing resectability of locally advanced pancreatic cancer following induction chemotherapy (IMAGE-MRI): prospective pilot study.

BACKGROUND: Restaging of locally advanced pancreatic cancer (LAPC) after induction chemotherapy using contrast-enhanced computed tomography (CE-CT) imaging is imprecise in evaluating local tumor response. This study explored the value of 3 Tesla (3 T) contrast-enhanced (CE) and diffusion-weighted (DWI) magnetic resonance imaging (MRI) for local tumor restaging. METHODS: This is a prospective pilot study including 20 consecutive patients with LAPC with RECIST non-progressive disease on CE-CT after induction chemotherapy. Restaging CE-CT, CE-MRI, and DWI-MRI were retrospectively evaluated by two abdominal radiologists in consensus, scoring tumor size and vascular involvement. A halo sign was defined as replacement of solid perivascular (arterial and venous) tumor tissue by a zone of fatty-like signal intensity. RESULTS: Adequate MRI was obtained in 19 patients with LAPC after induction chemotherapy. Tumor diameter was non-significantly smaller on CE-MRI compared to CE-CT (26 mm vs. 30 mm; p = 0.073). An MRI-halo sign was seen on CE-MRI in 52.6% (n = 10/19), whereas a CT-halo sign was seen in 10.5% (n = 2/19) of patients (p = 0.016). An MRI-halo sign was not associated with resection rate (60.0% vs. 62.5%; p = 1.000). In the resection cohort, patients with an MRI-halo sign had a non-significant increased R0 resection rate as compared to patients without an MRI-halo sign (66.7% vs. 20.0%; p = 0.242). Positive and negative predictive values of the CE-MRI-halo sign for R0 resection were 66.7% and 66.7%, respectively. CONCLUSIONS: 3 T CE-MRI and the MRI-halo sign might be helpful to assess the effect of induction chemotherapy in patients with LAPC, but its diagnostic accuracy has to be evaluated in larger series.

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients.

PURPOSE: Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients. METHOD: In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. RESULTS: In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations. CONCLUSION: IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.

PURPOSE: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance. METHODS: In May 2011, 10 male volunteers (age range, 29-53 years; mean, 37) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T MR scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by 2 readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass correlation coefficients (ICCs) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using coefficients of variation (CVs). The fitting error was calculated based on simulated data, and the average fitting time of each method was recorded. RESULTS: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the 2 readers (ICCs between 50% and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods, but the networks may need to be retrained for different acquisition protocols or imaged anatomical regions. CONCLUSION: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available for download.

Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer.

BACKGROUND: Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy. MATERIAL AND METHODS: Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed. RESULTS: In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61-0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47-0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p = .027) and borderline significant in the validation dataset (p = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54-0.71) and 0.62 (95%CI 0.49-0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor. CONCLUSIONS: A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.

Addition of MRI for CT-based pancreatic tumor delineation: a feasibility study.

PURPOSE: To assess the effect of additional magnetic resonance imaging (MRI) alongside the planning computed tomography (CT) scan on target volume delineation in pancreatic cancer patients. MATERIAL AND METHODS: Eight observers (radiation oncologists) from six institutions delineated the gross tumor volume (GTV) on 3DCT, and internal GTV (iGTV) on 4DCT of four pancreatic cancer patients, while MRI was available in a second window (CT + MRI). Variations in volume, generalized conformity index (CIgen), and overall observer variation, expressed as standard deviation (SD) of the distances between delineated surfaces, were analyzed. CIgen is a measure of overlap of the delineated iGTVs (1 = full overlap, 0 = no overlap). Results were compared with those from an earlier study that assessed the interobserver variation by the same observers on the same patients on CT without MRI (CT-only). RESULTS: The maximum ratios between delineated volumes within a patient were 6.1 and 22.4 for the GTV (3DCT) and iGTV (4DCT), respectively. The average (root-mean-square) overall observer variations were SD = 0.41 cm (GTV) and SD = 0.73 cm (iGTV). The mean CIgen was 0.36 for GTV and 0.37 for iGTV. When compared to the iGTV delineated on CT-only, the mean volumes of the iGTV on CT + MRI were significantly smaller (32%, Wilcoxon signed-rank, p < .0005). The median volumes of the iGTV on CT + MRI were included for 97% and 92% in the median volumes of the iGTV on CT. Furthermore, CT + MRI showed smaller overall observer variations (root-mean-square SD = 0.59 cm) in six out of eight delineated structures compared to CT-only (root-mean-square SD = 0.72 cm). However, large local observer variations remained close to biliary stents and pathological lymph nodes, indicating issues with instructions and instruction compliance. CONCLUSIONS: The availability of MRI images during target delineation of pancreatic cancer on 3DCT and 4DCT resulted in smaller target volumes and reduced the interobserver variation in six out of eight delineated structures.

Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an MRI technique for quantifying perfusion that can be used in clinical applications for classification of tumours and other types of diseases. Conventionally, the non-linear least squares (NLLS) methods is used for tracer-kinetic modelling of DCE data. However, despite promising results, NLLS suffers from long processing times (minutes-hours) and noisy parameter maps due to the non-convexity of the cost function. In this work, we investigated physics-informed deep neural networks for estimating physiological parameters from DCE-MRI signal-curves. Three voxel-wise temporal frameworks (FCN, LSTM, GRU) and two spatio-temporal frameworks (CNN, U-Net) were investigated. The accuracy and precision of parameter estimation by the temporal frameworks were evaluated in simulations. All networks showed higher precision than the NLLS. Specifically, the GRU showed to decrease the random error on ve by a factor of 4.8 with respect to the NLLS for noise (SD) of 1/20. The accuracy was better for the prediction of the ve parameter in all networks compared to the NLLS. The GRU and LSTM worked with arbitrary acquisition lengths. The GRU was selected for in vivo evaluation and compared to the spatio-temporal frameworks in 28 patients with pancreatic cancer. All neural network approaches showed less noisy parameter maps than the NLLS. The GRU had better test-retest repeatability than the NLLS for all three parameters and was able to detect one additional patient with significant changes in DCE parameters post chemo-radiotherapy. Although the U-Net and CNN had even better test-retest characteristics than the GRU, and were able to detect even more responders, they also showed potential systematic errors in the parameter maps. Therefore, we advise using our GRU framework for analysing DCE data.

Phase I/II Study of LDE225 in Combination with Gemcitabine and Nab-Paclitaxel in Patients with Metastatic Pancreatic Cancer.

BACKGROUND: Desmoplasia is a central feature of the tumor microenvironment in pancreatic ductal adenocarcinoma (PDAC). LDE225 is a pharmacological Hedgehog signaling pathway inhibitor and is thought to specifically target tumor stroma. We investigated the combined use of LDE225 and chemotherapy to treat PDAC patients. METHODS: This was a multi-center, phase I/II study for patients with metastatic PDAC establishing the maximum tolerated dose of LDE225 co-administered with gemcitabine and nab-paclitaxel (phase I) and evaluating the efficacy and safety of the treatment combination after prior FOLFIRINOX treatment (phase II). Tumor microenvironment assessment was performed with quantitative MRI using intra-voxel incoherent motion diffusion weighted MRI (IVIM-DWI) and dynamic contrast-enhanced (DCE) MRI. RESULTS: The MTD of LDE225 was 200 mg once daily co-administered with gemcitabine 1000 mg/m2 and nab-paclitaxel 125 mg/m2. In phase II, six therapy-related grade 4 adverse events (AE) and three grade 5 were observed. In 24 patients, the target lesion response was evaluable. Three patients had partial response (13%), 14 patients showed stable disease (58%), and 7 patients had progressive disease (29%). Median overall survival (OS) was 6 months (IQR 3.9-8.1). Blood plasma fraction (DCE) and diffusion coefficient (IVIM-DWI) significantly increased during treatment. Baseline perfusion fraction could predict OS (>222 days) with 80% sensitivity and 85% specificity. CONCLUSION: LDE225 in combination with gemcitabine and nab-paclitaxel was well-tolerated in patients with metastatic PDAC and has promising efficacy after prior treatment with FOLFIRINOX. Quantitative MRI suggested that LDE225 causes increased tumor diffusion and works particularly well in patients with poor baseline tumor perfusion.

Soluble Compounds Released by Hypoxic Stroma Confer Invasive Properties to Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by abundant stroma and a hypoxic microenvironment. Pancreatic stellate cells (PSC) are activated by hypoxia and promote excessive desmoplasia, further contributing to the development of hypoxia. We aimed to explore how hypoxia and stroma interact to contribute to invasive growth in PDAC. [18F]HX4 PET/CT was found to be a feasible non-invasive method to assess tumor hypoxia in 42 patients and correlated with HIF1α immunohistochemistry in matched surgical specimens. [18F]HX4 uptake and HIF1α were strong prognostic markers for overall survival. Co-culture and medium transfer experiments demonstrated that hypoxic PSCs and their supernatant induce upregulation of mesenchymal markers in tumor cells, and that hypoxia-induced stromal factors drive invasive growth in hypoxic PDACs. Through stepwise selection, stromal MMP10 was identified as the most likely candidate responsible for this. In conclusion, hypoxia-activated PSCs promote the invasiveness of PDAC through paracrine signaling. The identification of PSC-derived MMP10 may provide a lead to develop novel stroma-targeting therapies.

Pathological validation and prognostic potential of quantitative MRI in the characterization of pancreas cancer: preliminary experience.

Patient stratification based on biological variation in pancreatic ductal adenocarcinoma (PDAC) subtypes could help to improve clinical outcome. However, noninvasive assessment of the entire tumor microenvironment remains challenging. In this study, we investigate the biological basis of dynamic contrast-enhanced (DCE), intravoxel incoherent motion (IVIM), and R2*-derived magnetic resonance imaging (MRI) parameters for the noninvasive characterization of the PDAC tumor microenvironment and evaluate their prognostic potential in PDAC patients. Patients diagnosed with treatment-naïve resectable PDAC underwent MRI. After resection, a whole-mount tumor slice was analyzed for collagen fraction, vessel density, and hypoxia and matched to the MRI parameter maps. MRI parameters were correlated to immunohistochemistry-derived tissue characteristics and evaluated for prognostic potential. Thirty patients were included of whom 21 underwent resection with whole-mount histology available in 15 patients. DCE Ktrans and ve , ADC, and IVIM D correlated with collagen fraction. DCE kep and IVIM f correlated with vessel density and R2* with tissue hypoxia. Based on MRI, two main PDAC phenotypes could be distinguished; a stroma-high phenotype demonstrating high vessel density and high collagen fraction and a stroma-low phenotype demonstrating low vessel density and low collagen fraction. Patients with the stroma-high phenotype (high kep and high IVIM D, n = 8) showed longer overall survival (not reached vs. 14 months, P = 0.001, HR = 9.1, P = 0.004) and disease-free survival (not reached vs. 2 months, P < 0.001, HR 9.3, P = 0.003) compared to the other patients (n = 22). Median follow-up was 41 (95% CI: 36-46) months. MRI was able to accurately characterize tumor collagen fraction, vessel density, and hypoxia in PDAC. Based on imaging parameters, a subgroup of patients with significantly better prognosis could be identified. These first results indicate that stratification-based MRI-derived biomarkers could help to tailor treatment and improve clinical outcome and warrant further research.

Rapid stromal remodeling by short-term VEGFR2 inhibition increases chemotherapy delivery in esophagogastric adenocarcinoma.

Anti-angiogenic agents combined with chemotherapy is an important strategy for the treatment of solid tumors. However, survival benefit is limited, urging the improvement of combination therapies. We aimed to clarify the effects of vascular endothelial growth factor receptor 2 (VEGFR2) targeting on hemodynamic function and penetration of drugs in esophagogastric adenocarcinoma (EAC). Patient-derived xenograft (PDX) models of EAC were subjected to long-term and short-term treatment with anti-VEGFR2 therapy followed by chemotherapy injection or multi-agent dynamic contrast-enhanced (DCE-) MRI and vascular casting. Long-term anti-VEGFR2-treated tumors showed a relatively lower flow and vessel density resulting in reduced chemotherapy uptake. On the contrary, short-term VEGFR2 targeting resulted in relatively higher flow, rapid vasodilation, and improved chemotherapy delivery. Assessment of the extracellular matrix (ECM) revealed that short-term anti-angiogenic treatment drastically remodels the tumor stroma by inducing nitric oxide synthesis and hyaluronan degradation, thereby dilating the vasculature and improving intratumoral chemotherapy delivery. These previously unrecognized beneficial effects could not be maintained by long-term VEGFR2 inhibition. As the identified mechanisms are targetable, they offer direct options to enhance the treatment efficacy of anti-angiogenic therapy combined with chemotherapy in EAC patients.

Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures.

BACKGROUND: Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. MATERIAL AND METHODS: A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. RESULTS: A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). CONCLUSION: The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.

Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images.

Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method.

Association between body composition, survival, and toxicity in advanced esophagogastric cancer patients receiving palliative chemotherapy.

BACKGROUND: Palliative systemic treatment in patients with advanced or metastatic esophagogastric cancer may result in improved overall survival and quality of life but can also lead to considerable toxicity. In various cancer types, severe muscle mass depletion (sarcopenia) and poor muscle strength are associated with decreased survival and increased chemotherapy-related toxicity. The aim of this study is to determine the impact of body composition on survival and chemotherapy toxicity in esophagogastric cancer patients treated with first-line palliative chemotherapy. METHODS: A total of 88 patients with advanced esophagogastric cancer treated with standard first-line palliative systemic therapy consisting of capecitabine and oxaliplatin (CapOx) between January 2010 and February 2017 were included. Skeletal muscle index (SMI), reflecting muscle mass, and skeletal muscle density (SMD), associated with muscle strength, were measured using pre-treatment of all patients and evaluation computed tomography scans after three treatment cycles of 65 patients and were used to determine sarcopenia and sarcopenic obesity (i.e. sarcopenia and body mass index >25 kg/m2 ). The associations between body composition (SMI, SMD, sarcopenia, and sarcopenic obesity) and survival and toxicity were assessed using univariable and multivariable Cox and logistic regression analyses, respectively. RESULTS: Of 88 patients, 75% was male, and median age was 63 (interquartile range 56-69) years. The majority of patients had an adenocarcinoma (83%). Before start of treatment, 49% of the patients were sarcopenic, and 20% had sarcopenic obesity. Low SMD was observed in 50% of patients. During three cycles CapOx, SMI significantly decreased, with a median decrease of 4% (interquartile range -8.6--0.4). Median progression-free and overall survival were 6.9 and 10.1 months. SMI, SMD, sarcopenia, and sarcopenic obesity (both pre-treatment and after three cycles) were neither associated with progression-free nor overall survival. Pre-treatment SMD was independently associated with grade 3-4 toxicity (odds ratio 0.94; 95% confidence interval 0.89-1.00) and sarcopenic obesity with grade 2-4 neuropathy (odds ratio 3.82; 95% confidence interval 1.20-12.18). CONCLUSIONS: Sarcopenia was not associated with survival or treatment-related toxicity in advanced esophagogastric cancer patients treated with CapOx. Pre-treatment sarcopenic obesity was independently associated with the occurrence of grade 2-4 neurotoxicity and skeletal muscle density with grade 3-4 toxicity.

Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients.

In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74-0.83) and 0.65 (95% ci: 0.57-0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83-0.90) and 0.79 (95% ci 0.72-0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings.

Repeatability and correlations of dynamic contrast enhanced and T2* MRI in patients with advanced pancreatic ductal adenocarcinoma.

BACKGROUND: In current oncological practice of pancreatic ductal adenocarcinoma (PDAC), there is a great demand for response predictors and markers for early treatment evaluation. In this study, we investigated the repeatability and the interaction of dynamic contrast enhanced (DCE) and T2* MRI in patients with advanced PDAC to enable for such evaluation using these techniques. MATERIALS & METHODS: 15 PDAC patients underwent two DCE, T2* and anatomical 3 T MRI sessions before start of treatment. Parametric maps were calculated for the transfer constant (Ktrans), rate constant (kep), extracellular extravascular space (ve) and perfusion fraction (vp). Quantitative R2* (1/T2*) maps were obtained from the multi-echo T2* images. Differences between normal and cancerous pancreas were determined using a Wilcoxon matched pairs test. Repeatability was obtained using Bland-Altman analysis and relations between DCE and T2*/R2* were observed by Spearman correlation and voxel-wise binned plots of tumor voxels. RESULTS: PDAC Ktrans (p = 0.007), kep (p < 0.001), vp (p = 0.035) were lower and ve (p < 0.001) was higher compared to normal pancreas. The coefficient of variation between sessions was 21.8% for Ktrans, 9.9% for kep, 19.3% for ve, 18.2% for vp and 18.7% for R2*. Variation between patients ranged from 20.2% for kep to 43.6% for Ktrans. In the tumor both Ktrans (r = 0.56, p = 0.030) and ve (r = 0.54, p = 0.037) showed a positive correlation with T2*. Voxel wise analysis showed a steep increase in R2* for tumor voxels with lower Ktrans and ve. CONCLUSION: We showed good repeatability of DCE and T2* related MRI parameters in advanced PDAC patients. Furthermore, we have illustrated the relation of DCE Ktrans and ve with tissue T2* and R2* indicating substantial value of these parameters for detecting tumor hypoxia in future studies. The results from our study pave the way for further response evaluation studies and patient selection based on DCE and T2* parameters.

Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.

The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman's rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.

Evaluation of Six Diffusion-weighted MRI Models for Assessing Effects of Neoadjuvant Chemoradiation in Pancreatic Cancer Patients.

PURPOSE: To compare 6 diffusion-weighted imaging (DWI) MRI models for response evaluation in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: DWI images were acquired at 3T for b = 0-600 s/mm2 in fourteen patients with advanced PDAC during 2 separate pretreatment sessions and 9 patients with (borderline) resectable PDAC pre and post neoadjuvant chemoradiation. Data was fitted with a mono-exponential (ADC), double mono-exponential to b = 0 and 100 s/mm2 (ADCfast), and b = 100 and 600 s/mm2 (ADCslow), IVIM model with D* free (D, f, D*) and fixed (D, f), tri-exponent (D, f1, f2), and stretched exponent model (DDC, α). Goodness of fit (adjusted R2), tumor to normal tissue contrast, repeatability (coefficient of variation), and parameter correlations (Spearman's rho) were assessed for the repeated measures. Treatment induced changes were assessed and compared to the repeatability. RESULTS: The mono-exponential model had the lowest goodness of fit in both tumor (R2 = 0.94) and normal-appearing pancreas (R2 = 0.88). Tumour to normal tissue contrast was higher for the 'non-diffusion' parameters (ADCfast, f, D*, f1, f2, α), with better repeatability for the diffusion parameters (ADC, ADCslow, D, DDC). Diffusion parameters were strongly correlated between the models (rho ≥0.81) and showed a general treatment associated increase. All models were able to identify individual treatment effects, showing a change greater than the repeatability in 5 out of 9 patients for at least one of the parameters. CONCLUSIONS: Individual treatment evaluation is possible with all investigated DWI models, with treatment associated changes exceeding the repeatability. The double monoexponential fit with ADCfast and ADCslow is able to discriminate between non-diffusion and diffusion related effects, is measured fast and can be performed on most commercial scanners, making it an attractive alternative for the more advanced multiparametric models in radiotherapy treatment evaluation.

Revisiting the Potential of Alternating Repetition Time Balanced Steady-State Free Precession Imaging of the Abdomen at 3 T.

OBJECTIVE: The aim was to investigate the value of optimized 3-dimensional alternating repetition time balanced steady-state free precession (ATR-SSFP), as an alternative to conventional segmented balanced steady-state free precession (bSSFP) with fat suppression prepulse (FS-bSSFP), in single breath-hold abdominal magnetic resonance imaging at 3 T. METHODS: Bloch simulations were performed to determine the optimal flip angle (FA = 1-90 degrees) and τ (1-3) with respect to signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) between abdominal organs for ATR-SSFP. These were corroborated by phantom measurements for different T1/T2 values (5-47) as well as in a healthy volunteer. In addition, fat suppression efficiency was studied using phantom and volunteer measurements. The effect of resolution on image quality was studied in a healthy volunteer. Using the optimal settings, ATR-SSFP images as well as FS-bSSFP images were obtained in 15 pancreatic cancer patients. For 10 structures of interest, the signal ratio with respect to the pancreas was computed and compared between both sequences. Finally, 10 items on image quality (fat suppression, artifacts, and sharpness) and tissue conspicuity (ducts, vessels, and duodenum) were scored by 2 abdominal radiologists for both image sequences. RESULTS: The results of simulations, phantom measurements, and volunteer measurements showed that, considering scan time, fat suppression, and clinical relevance, the ideal settings for ATR-SSFP were as follows: τ = 3; TR1 = 3.46 milliseconds; radiofrequency phase cycling 0, 180, 180, 0 degrees; and FA = 13-16 degrees (highest SNR) and 24-26 degrees (highest CNR). The optimized feasible additional settings implemented for patient scans were FA = 18 degrees and resolution = 1.4 × 1.4 × 1.4 mm. In patients, the signal ratios of both ATR-SSFP and FS-bSSFP were comparable and had a T2-like contrast behavior, although more accentuated in ATR-SSFP. The ATR-SSFP scored significantly higher than FS-bSSFP for 9 of 10 items scored. CONCLUSIONS: For single breath-hold abdominal imaging at 3 T, ATR-SSFP performs best with τ = 3 and an FA between 13 degrees (highest SNR) and 26 degrees (highest CNR). The scoring of both abdominal radiologists indicated that, at τ = 3, FA = 18 degrees, and 1.4 × 1.4 × 1.4 mm resolution, ATR-SSFP was preferred over conventional FS-bSSFP with similar settings.

Minimizing the Acquisition Time for Intravoxel Incoherent Motion Magnetic Resonance Imaging Acquisitions in the Liver and Pancreas.

OBJECTIVES: The aim of this study was to determine the combination of b-values and signal averages for diffusion-weighted image acquisitions that render the minimum acquisition time necessary to obtain values of the intravoxel incoherent motion (IVIM) model parameters in vivo in the pancreas or liver with acceptable reproducibility. MATERIALS AND METHODS: For 16 volunteers, diffusion-weighted images, with 14 b-values and 9 acquisitions per b-value, were acquired in 2 scan sessions. The IVIM model was fitted to data from lesion-sized regions of interest (ROIs) (1.7 cm(3)) as well as organ-sized ROIs in the pancreas and liver. By deleting data during analyzes, the IVIM model parameters, D and f, could be determined as a function of the number of b-values as well as the number of measurements per b-value taken along. For the IVIM model parameters, we examined the behavior reproducibility, in the form of the within-subject coefficient of variation (CVw), as a function of the amount of data taken along in the fits. Finally, we determined the minimum acquisition time required as a function of CVw. RESULT: For the lesion-sized ROI, the intersession CVws were 8%/46% and 13%/55% for D/f in the pancreas and liver, respectively, when all data were taken along. For 1.2 times larger CVws, acquisition in the pancreas could be done in 5:15 minutes using 9 acquisitions per b-value at b = 0, 30, 50, 65, 100, 375, and 500 mm(-2)s and for the liver in 2:15 using 9 acquisitions per b-value at b = 0, 40, and 500 mm(-2)s. CONCLUSIONS: Acquiring 7 b-values in the pancreas and 3 b-values in the liver only decreases the reproducibility by 20% compared with an acquisition with 14 b-values. The understanding of the behavior of reproducibility as a function of b-values and acquisitions per b-values scanned will help researchers select the shortest IVIM protocol.