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Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%.This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.

Background More accurate predictive biomarkers in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs) are needed. This study aims to investigate radiomics-based tumour phenotypes as a surrogate biomarker of the tumour vasculature and response prediction to antiangiogenic targeted agents in patients with GEP-NETs. Methods In this retrospective study, a radiomics signature was developed in patients with GEP-NETs and liver metastases receiving lenvatinib. Patients were selected from the multicentre phase II TALENT trial (NCT02678780) (development cohort). Radiomics variables were extracted from liver metastases in the pre-treatment CT-scans and selected using LASSO regression and minimum redundancy maximum relevance (mRMR). Logistic regression and Cox proportional-hazards models for radiomics and combined radiomics with clinical data were explored. The performance of the models was tested in an external cohort (test cohort). Associations between the radiomics score and vascularization factors in plasma were studied using hierarchical clustering and Mann-Whitney U test. Results A total of 89 patients were included in the study, 408 liver metastases where analysed. The CT-based radiomics signature was associated with clinical benefit in the development (training and validation sets) and test cohorts (AUC 0.75 [0.66-0.90], 0.67 [0.49-0.92] and 0.67 [0.43-0.91], respectively). The combined radiomics-clinical signature (including the radiomics score, Ki-67 index and primary tumour site) improved on radiomics-only signature performance (AUC 0.79 [95% CI 0.64-0.93]; PConclusions Radiomics-based phenotypes can provide valuable information about tumour characteristics, including vasculature, that are associated with response to antiangiogenics. Clinical Trial Registration This is a study of the Lenvatinib Efficacy in Metastatic Neuroendocrine Tumours (TALENT) phase II clinical trial (NCT02678780).

Non-invasive differential diagnosis of brain tumours is currently based on the assessment of tumour vascularity through magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, given its limited accuracy, reaching a definitive diagnosis often requires complex neurosurgical interventions that compromise the patients’ quality of life. We applied deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis or lymphoma, the three most common brain malignancies. The convolutional neural network trained on ~ 50,000 voxels from 40 patients provided intra-tumour probability maps that yielded clinical-grade diagnosis. Performance was tested in 400 additional cases and an external validation cohort (n = 128). The tool reached a three-way accuracy of 0.78, superior to standard diagnosis with cerebral blood volume (0.55) and percentage of signal recovery (0.59) perfusion metrics. Our open-access software, Brain Enhancing Region Radiological analysis (BERRY), demonstrates the potential of voxel-wise probability maps for differential diagnosis of brain tumours using standard-of-care MRI.

Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77

BACKGROUND AND PURPOSE: DSC-PWI has demonstrated promising results in the presurgical diagnosis of brain tumors. While most studies analyze specific parameters derived from time-intensity curves, very few have directly analyzed the whole curves. The aims of this study were the following: 1) to design a new method of postprocessing time-intensity curves, which renders normalized curves, and 2) to test its feasibility and performance on the diagnosis of primary central nervous system lymphoma. MATERIALS AND METHODS: Diagnostic MR imaging of patients with histologically confirmed primary central nervous system lymphoma were retrospectively reviewed. Correlative cases of glioblastoma, anaplastic astrocytoma, metastasis, and meningioma, matched by date and number, were retrieved for comparison. Time-intensity curves of enhancing tumor and normal-appearing white matter were obtained for each case. Enhancing tumor curves were normalized relative to normal-appearing white matter. We performed pair-wise comparisons for primary central nervous system lymphoma against the other tumor type. The best discriminatory time points of the curves were obtained through a stepwise selection. Logistic binary regression was applied to obtain prediction models. The generated algorithms were applied in a test subset. RESULTS: A total of 233 patients were included in the study: 47 primary central nervous system lymphomas, 48 glioblastomas, 39 anaplastic astrocytomas, 49 metastases, and 50 meningiomas. The classifiers satisfactorily performed all bilateral comparisons in the test subset (primary central nervous system lymphoma versus glioblastoma, area under the curve = 0.96 and accuracy = 93%; versus anaplastic astrocytoma, 0.83 and 71%; versus metastases, 0.95 and 93%; versus meningioma, 0.93 and 96%). CONCLUSIONS: The proposed method for DSC-PWI time-intensity curve normalization renders comparable curves beyond technical and patient variability. Normalized time-intensity curves performed satisfactorily for the presurgical identification of primary central nervous system lymphoma.

Imatges per ressonància magnètica; Neoplàsies rectals; Microambient tumoral Imagen de resonancia magnética; Neoplasias rectales; Microambiente tumoral Magnetic Resonance Imaging; Rectal neoplasms; Tumor Microenvironment Objective This study evaluated the correlation between intratumoural stroma proportion, expressed as tumour-stroma ratio (TSR), and apparent diffusion coefficient (ADC) values in patients with rectal cancer. Methods This multicentre retrospective study included all consecutive patients with rectal cancer, diagnostically confirmed by biopsy and MRI. The training cohort (LUMC, Netherlands) included 33 patients and the validation cohort (VHIO, Spain) 69 patients. Two observers measured the mean and minimum ADCs based on single-slice and whole-volume segmentations. The TSR was determined on diagnostic haematoxylin & eosin stained slides of rectal tumour biopsies. The correlation between TSR and ADC was assessed by Spearman correlation ( r s ). Results The ADC values between stroma-low and stroma-high tumours were not significantly different. Intra-class correlation (ICC) demonstrated a good level of agreement for the ADC measurements, ranging from 0.84–0.86 for single slice and 0.86–0.90 for the whole-volume protocol. No correlation was observed between the TSR and ADC values, with ADC mean r s = -0.162 ( p= 0.38) and ADC min r s = 0.041 ( p= 0.82) for the single-slice and r s = -0.108 ( p= 0.55) and r s = 0.019 ( p= 0.92) for the whole-volume measurements in the training cohort, respectively. Results from the validation cohort were consistent; ADC mean r s = -0.022 ( p= 0.86) and ADC min r s = 0.049 ( p= 0.69) for the single-slice and r s = -0.064 ( p= 0.59) and r s = -0.063 ( p= 0.61) for the whole-volume measurements. Conclusions Reproducibility of ADC values is good. Despite positive reports on the correlation between TSR and ADC values in other tumours, this could not be confirmed for rectal cancer. This study received financial support from “ Genootschap Landgoed Keukenhof .” Author R.P.L.’s work is supported by a PCF-Young Investigator Award . The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Objective To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications. Methods CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction. Results Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness–kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20). Conclusion CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy. Key Points • The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

Background: Mucinous colorectal carcinoma (CRC) is found in 10-20% of patients and is associated with worse prognosis and treatment resistance. The early identification of mucinous tumor component at baseline and monitoring resistant clones at follow-up is challenging in clinical practice, which hinders appropriate and timely treatment selection. At CT, being routinely acquired in clinical practice, mucinous tumors can be characterized by semantic features, such as hypoattenuation and more heterogeneous enhancement than the non-mucinous tumors (Wnorowski et al 2019). However, the diagnostic accuracy of such CT findings reaches at most 62% (Young et al 2007). This can be substantially improved by utilizing robust feature quantification using state-of-art machine learning and neural network techniques. Materials and Methods: 7 mucinous and 7 non-mucinous CRC CTs were included in the model development (80% training and 20% validation) and 2 mucinous and 2 non-mucinous independent patients were used to test the model performance. Multiple lesions (primary and metastatic) were semi-automatically segmented in 3D Slicer (N=32 development and N=12 test). Three different classification models were generated using CT images: (1) a logistic regression model based on a newly developed hypodense tissue connectivity (HTC) metric, (2) a logistic regression model using a set of automatically selected radiomics (RAD) features (shape, 1st order and 2nd order) and (3) a convolutional neural network model (CNN) based on the ResNet architecture and automatically selected features. HTC was computed as a ratio between the volume of the connected hypodense tissue (0 Citation Format: Kinga Bernatowicz, Raquel Perez Lopez, Hector Garcia Palmer, Elena Elez Fernandez, Jose Fernandez Navarro, Marta Ligero Hernandez, Alonso Garcia Ruiz, Manuel Escobar Amores. Humans cannot accurately detect mucinous colorectal carcinoma from CT images, can AI help? [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-021.

Background There are currently no good indicators of which patients with cancer will respond or not to immunotherapy. Novel computational analysis of computed tomography scans (CT) (i.e. radiomics) provides information about the tumour-infiltrating CD8 and predict response to immunotherapy. We aim to validate in an external cohort the VHIO CT-radiomics signature and to develop a combined radiomics-clinical signature that predicts the response to immune checkpoint inhibitors in patients with advanced solid tumours. Methods The VHIO CT-radiomics signature was developed in a population of 115 consecutive patients treated with immune checkpoint inhibitors (programmed-death protein 1 [PD-1] or programmed-death ligand 1 [PD-L1] inhibitors) monotherapy in phase I clinical trials (Cohort 1). The external validation included 62 consecutive patients with urinary bladder cancer treated with anti-PD-1 or PD-L1 monotherapy (Cohort 2). From the baseline CT, a target lesion per patient was delineated. Radiomics variables of first-order, shape, and texture were extracted. An elastic-net model combining radiomics and clinical features was implemented. The association between the radiomics score and changes in tumour shrinkage was assessed using Mann-Whitney analysis. Results In the Cohort 1 the CT-radiomics signature associates with response (area under the curve [AUC] of 0.81, p-value=2.74x10-5 and 0.72, p = 0.001 in the training and internal validation sets, respectively). In the external validation set (Cohort 2), the CT-radiomics signature predicts a response with an AUC of and 0.76 (p = 0.001). The model combining radiomics and clinical features has an AUC of 0.84 (p-value=5.04x10-9) for response prediction. Tumour homogeneity, hypodensity and spherical shape together with high lymphocytes and albumin and low neutrophils, corresponding to a high clinical-radiomics signature score, are indicators of tumour response. A higher CT-radiomics signature score is associated with a larger tumour shrinkage (p Conclusions CT-radiomics signature at baseline predicts the response to immune checkpoint inhibitors. Integrating radiomics and clinical data improved the response prediction capacity. Legal entity responsible for the study The authors. Funding This study was supported by the Banco Bilbao Vizcaya Argentaria and Fundacio La Caixa. RPL is supported by a Prostate Cancer Foundation Young Investigator award. Disclosure J. Martin Liberal: Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Roche; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Novartis; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: MSD; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Pfizer; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Ipsen; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Pierre Fabre; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Astellas; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Bristol-Myers Squibb. R. Morales Barrera: Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Sanofi Aventis; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Bayer; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Janssen; Advisory / Consultancy, Speaker Bureau / Expert testimony: AstraZeneca; Advisory / Consultancy, Speaker Bureau / Expert testimony, Travel / Accommodation / Expenses: Merck Sharp & Dohme; Advisory / Consultancy, Speaker Bureau / Expert testimony: Asofarm; Travel / Accommodation / Expenses: Roche; Travel / Accommodation / Expenses: Astellas; Travel / Accommodation / Expenses: Pharmacyclics; Travel / Accommodation / Expenses: Clovis Oncology; Travel / Accommodation / Expenses: Lilly. E. Elez: Travel / Accommodation / Expenses: Merck; Travel / Accommodation / Expenses: Sanofi; Travel / Accommodation / Expenses: Roche; Travel / Accommodation / Expenses: Servier and Amge ; Research grant / Funding (self): Merck. E. Felip: Honoraria (self): AbbVie; Honoraria (self): AstraZeneca; Honoraria (self): Blue Print Medicines; Honoraria (self): Boehringer Ingelheim; Honoraria (self): Bristol-Myers Squibb; Honoraria (self): Celgene; Honoraria (self): Eli Lilly; Honoraria (self): Guardant Health; Honoraria (self): Janssen; Honoraria (self): Medscape; Honoraria (self): Merck KGaA; Honoraria (self): MSD; Honoraria (self): Novartis; Honoraria (self): Pfizer; Honoraria (self): Takeda; Honoraria (self): Touchtime. J. Tabernero: Advisory / Consultancy: Array Biopharma; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Bayer; Advisory / Consultancy: BeiGene; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy: Chugai; Advisory / Consultancy: Genentech, Inc; Advisory / Consultancy: Genmab A/S; Advisory / Consultancy: Halozyme; Advisory / Consultancy: Imugene Limited; Advisory / Consultancy: Inflection Biosciences Limited; Advisory / Consultancy: Ipsen; Advisory / Consultancy: Kura Oncology; Advisory / Consultancy: Lilly; Advisory / Consultancy: MSD; Advisory / Consultancy: Menarini; Advisory / Consultancy: Merck Serono; Advisory / Consultancy: Merus; Advisory / Consultancy: Molecular Partners; Advisory / Consultancy: Novartis; Advisory / Consultancy: Peptomyc; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Pharmacyclics; Advisory / Consultancy: ProteoDesign SL; Advisory / Consultancy: F. Hoffmann-La Roche Ltd; Advisory / Consultancy: Sanofi; Advisory / Consultancy: SeaGen; Advisory / Consultancy: Seattle Genetics; Advisory / Consultancy: Servier; Advisory / Consultancy: Symphogen; Advisory / Consultancy: Taiho; Advisory / Consultancy: VCN Biosciences; Advisory / Consultancy: Biocartis; Advisory / Consultancy: Foundation Medicine; Advisory / Consultancy: HalioDX SAS. R. Dienstmann: Advisory / Consultancy, Speaker Bureau / Expert testimony: Roche; Speaker Bureau / Expert testimony: Symphogen; Speaker Bureau / Expert testimony: Ipsen; Speaker Bureau / Expert testimony: Amgen; Speaker Bureau / Expert testimony: Sanofi; Speaker Bureau / Expert testimony: MSD; Speaker Bureau / Expert testimony: Servier; Research grant / Funding (self): Merck. All other authors have declared no conflicts of interest.

448 Background: Hyperprogressive disease (HPD) is a new pattern of progression in cancer patients (pts) treated with immune checkpoint inhibitors (ICI). The rate and outcome of HDP in pts with metastatic renal carcinoma (RCC) and urothelial carcinoma (UC) are unknown. Here, we report the percentage of HPD in a cohort of pts with GU malignancies treated at our center and explore associations with clinical variables. Methods: Medical records from pts treated in phase I-III clinical trials with ICI alone or in combination between July 2013 and June 2018 were retrospectively reviewed. We defined HPD according to the radiologic VHIO´s criteria (ASCO 2018). Associations between HPD and categorical or continuous variables were evaluated using Fisher exact test and Wilcoxon test respectively. OS were estimated with the Kaplan-Meir method. Statistical analyses were performed using the R statistical software (R version 3.5.0). Results: Overall, 104 pts received therapy with ICI. Of these patients, 16 were not included for the analysis (6 pts with absence of measurable disease, 6 pts did not have CT scan available after the clinical progression and 4 pts treated with ICI plus chemotherapy). Thus, 88 pts were included for the analysis, 29 (33%) with RCC and 59 (67%) with UC. Median follow-up was 7.4 months. Median age was 66.5 years (range 29-91 y).Twenty-three pts (26%) were treated with ICI monotherapy and 65 pts (74%) in combination (anti-CTL4, antiangiogenics, PARP inhibitors, FGFR inhibitors). Forty-seven pts (53%) received ICI as second-line therapy or later. By RECIST v1.1, 26 (30%), 19 (21%) and 43(49%) pts had a best response of progressive disease, stable disease or partial+complete response, respectively. We identified 9 pts (10%) who meet the HPD criteria, 2 pts with RCC and 7 with UC. HPD was associated with anemia at baseline (p = 0.058). Pts with HPD had a trend toward lower overall survival (OS) compared with pts with non-HPD (8.87 vs 4.77 months; p = 0.065). Conclusions: These findings demonstrate that HPD is a phenomenon seen in a significant proportion of pts with RCC and UC and should be taken in account. We found that HPD is associated with poor OS and the anemia at baseline was correlated with HPD.