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Primary sclerosis cholangitis (PSC) predisposes individuals to liver failure, but it is challenging for radiologists examining radiologic images to predict which patients with PSC will ultimately develop liver failure. Motivated by algebraic topology, a topological data analysis - inspired framework was adopted in the study of the imaging pattern between the “Early Decompensation” and “Not Early” groups. The results demonstrate that the proposed methodology discriminates “Early Decompensation” and “Not Early” groups. Our study is the first attempt to provide a topological representation-based method into early hepatic decompensation and not early groups.

Minimizing bias is critical to adoption and implementation of machine learning (ML) in clinical practice. Systematic mathematical biases produce consistent and reproducible differences between the observed and expected performance of ML systems, resulting in suboptimal performance. Such biases can be traced back to various phases of ML development: data handling, model development, and performance evaluation. This report presents 12 suboptimal practices during data handling of an ML study, explains how those practices can lead to biases, and describes what may be done to mitigate them. Authors employ an arbitrary and simplified framework that splits ML data handling into four steps: data collection, data investigation, data splitting, and feature engineering. Examples from the available research literature are provided. A Google Colaboratory Jupyter notebook includes code examples to demonstrate the suboptimal practices and steps to prevent them. Keywords: Data Handling, Bias, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Computer-aided Diagnosis (CAD) © RSNA, 2022

Early prediction of COVID-19 in-hospital mortality relies usually on patients’ preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p, Graphical abstract Image 1

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Considering the complications of the tissue-based methods, an imaging-based approach is preferred. This study aimed to compare three different deep learning-based approaches for predicting MGMT promoter methylation status. We obtained 576 T2WI with their corresponding tumor masks, and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32 × 32 × 32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch's coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction, then for final prediction, we used majority voting. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, and voxel-wise, accuracy was 65.42% (SD 3.97%), 61.37% (SD 1.48%), and 56.84% (SD 4.38%), respectively.

Purpose Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time-consuming. We present a fully automated segmentation method based on the three-dimensional (3D) U-Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels. Whole abdomen segmentation is capable of quantifying exposure beyond a handful of organs-at-risk to all tissues within the abdomen. Methods Sixty-six (66) CT examinations of 64 individuals were included in the training and validation sets and 18 CT examinations from 16 individuals were included in the test set. All pixels in each examination were segmented by image analysts (with physician correction) and assigned one of 33 labels. Segmentation was performed with a 3D U-Net variant architecture which included residual blocks, and model performance was quantified on 18 test cases. Human interobserver variability (using semiautomated segmentation) was also reported on two scans, and manual interobserver variability of three individuals was reported on one scan. Model performance was also compared to several of the best models reported in the literature for multiple organ segmentation. Results The accuracy of the 3D U-Net model ranges from a Dice coefficient of 0.95 in the liver, 0.93 in the kidneys, 0.79 in the pancreas, 0.69 in the adrenals, and 0.51 in the renal arteries. Model accuracy is within 5% of human segmentation in eight of 19 organs and within 10% accuracy in 13 of 19 organs. Conclusions The CNN approaches the accuracy of human tracers and on certain complex organs displays more consistent prediction than human tracers. Fully automated deep learning-based segmentation of CT abdomen has the potential to improve both the speed and accuracy of radiotherapy dose prediction for organs-at-risk.

INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. Despite multimodal treatment, its median overall survival ranges between 16 and 20 months. The standard treatment regimen consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. Despite temozolomide’s effectiveness, it may cause the clinical challenge of treatment-related progression also known as pseudoprogression(PsP). Usually, PSP resolves or stabilizes without further treatment, whereas a true progression (TP) requires more aggressive management. Identifying PSP from TP will affect the patient’s treatment plan. Conventional magnetic resonance imaging (MRI) reading techniques cannot distinguish these entities. This study investigated the feasibility of using deep learning to distinguish PsP from TP. METHOD We included GBM patients who met our inclusion criteria. We evaluated all cases to see if they had a new enhancing lesion within the original radiation field or an increase in the size of an existing lesion. The challenging MRIs were collected. Clinical notes regarding tumor and recurrence location, clinical history, and medication were collected. We labeled the ones who stayed stable or improved in the imaging and clinical situation as PSP and those with further imaging and clinical deterioration as TP. We coregistered Contrast-enhanced-T1 MRIs with T2-weighted images for each patient. We performed five-fold cross-validation to generalize the performance. We trained A 3-D Densenet121 model to establish the prediction. We selected the best models with the highest accuracy. RESULT After reviewing 1000 patients, we included 124 patients whose imaging showed suspicious progression and their medicational histories were completely retrievable; 63 PsP, and 61 TP. We developed a deep learning model based on the whole dataset. The 5-fold cross-validation revealed that the mean area under the curve (AUC) was 0.81. CONCLUSION We report the development of a deep learning model that diagnoses PsP from TP in patients who received temozolomide. Further refinement and external validation are required prior to widespread adoption in clinical practice.

GBM is the most common primary malignant brain tumor in adults. O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides the treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Given the limitations and complications of histology-based methods, an imaging-based approach for non-invasively MGMT promoter methylation status prediction is beneficial. This study aimed to develop and compare three different deep-learning-based approaches for predicting MGMT promoter methylation status non-invasively. We obtained 576 T2 weighted images (T2WIs) with their corresponding tumor masks and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. Dataset was split into five folds at the patient's level stratified by MGMT promoter methylation status to perform a 5-fold cross-validation. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32x32x32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch's coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction; then, we used majority voting for the final prediction. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, voxel-wise, accuracy was 65.42%(SD 3.97%), 61.37%(SD 1.48%), and 56.84%(SD 4.38%) respectively. We found that across the whole-brain, slice-wise, and voxel-wise deep learning approaches, the whole-brain approach is the most effective approach for MGMT methylation status prediction on the BraTS 2021 dataset.

GBMs are highly aggressive tumors, evidenced by their rapid growth. Despite maximal treatment, median overall survival ranges between 16 and 20 months. Several studies use patients' diagnosis time(pre-surgical) imaging to predict their survival, but having a model that can predict the days till death for the patients who received the first standard step treatments (Stupp protocol) and now showing secondary progression signs is highly needed. In this study, we aimed to predict the overall survival of Glioblastoma(GBM) patients by developing a multimodal deep learning model using post-operative magnetic resonance imaging(MRI) and patients' age. We included GBM patients who met the following inclusion criteria: having maximum surgical resection followed by concurrent chemoradiation and showing secondary progression signs in follow-up imaging while being on adjuvant temozolomide treatment. We obtained contrast-enhanced T1(CET1) and T2-weighted(T2W) MRIs at progression time and patients' age and death dates. We calculated the days of survival from the day of the MRI. Dataset was split into train, validation, and test set at the patient level. CET1 and T2W MRIs were skull-stripped and coregistered for each patient. To mitigate data imbalance, we used the deep imbalance regression method for training a 3-D Densenet121 model with the concatenated CET1 and T2W as inputs. One hundred features were extracted from the optimized model and concatenated with the normalized age of each patient; the resulting output vectors were passed through the second dense neural network for final prediction. We included 110 patients who met the inclusion criteria. With the overall survival ranging from 16 to 4500 days, a mean of 582.4 and a standard deviation of 694.3, we achieved the test set's root mean square error of 261 days. Further refinement with more data, including clinical data such as comorbidities and external validation, is required prior to adoption in clinical practice.

Missing MRI sequences are common in radiology due to differences in protocols, time constraints, and image artifacts. Missing sequences are a nuisance for subsequent algorithms, such as automated segmentation algorithms requiring multiple MRI sequences (e.g., HD-GLIO, which requires T2, T1, post-contrast T1 (T1c), and FLAIR). Accurate tumor segmentation is a fundamental step in extracting radiomics analysis to use, for example, to predict the IDH status of glioma or their overall survival. We validated a conditional (pix2pix) and super-resolution GAN (SOUP-GAN) for imputing MRI sequences on 285 adult glioma with complete MRI sequences available. We simulated missing T1 sequences and subsequently imputed them from T1c using pix2pix and from sagittal T1 using SOUP-GAN. Using HD-GLIO, we obtained the true segmentations from the complete MRI sequences. HD-GLIO was also used after replacing the original T1 sequence with the one imputed with pix2pix and SOUP-GAN. Thus, there were three sets of segmentations for each subject. We compared T2 and T1c radiomics features extracted with Pyradiomics using segmentations obtained with complete data with features extracted from segmentation obtained with the imputed T1 sequences using intraclass correlation coefficient (ICC) and Bland-Altman (BA) analysis. Median (interquartile range) ICC for shape features extracted from T2 and T1c were 0.82(0.76-0.92) using pix2pix and 0.89(0.86-0.94) using SOUP-GAN. Median ICC for first-order-statistics features were 0.90(0.83-0.93) for T2 and 0.90(0.88-0.92) for T1c using pix2pix, and 0.61(0.22-0.82) for T2 and 0.42(0.34-0.57) for T1c using SOUP-GAN. Median ICC for texture features were 0.86(0.82-0.87) for T2 and 0.87(0.79-0.90) for T1c using pix2pix, and 0.69(0.47-0.81) for T2 and 0.76(0.65-0.83) for T1c using SOUP-GAN. We show that T2 and T1c shape features (e.g., volumetric analysis) can be adequately extracted from segmentations obtained with an algorithm requiring multiple MRI sequences using T1 sequences imputed with GANs; however, the effect of imputation differed for first-order-statistics and texture features.

INTRODUCTION Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease. METHOD We included newly diagnosed GBM patients who met the inclusion criteria, including a new or increasing enhancing lesion size within the original radiation field and who had clinical, medication, and any histopathology available. The interpretation of the MR images at the image inclusion time point had to be indeterminate. We labeled those who subsequently were stable or improved on imaging and clinically as PSP and those with clinical and imaging deterioration as TP. A subset of subjects underwent a second resection. We labeled these subjects as PSP or TP based on the histological diagnosis. We performed skull stripping, coregistered contrast-enhanced T1 MRIs with T2-weighted images for each patient and used them as input to a 3-D Densenet121 model. We used several augmentation techniques and five-fold cross-validation to achieve more robust predictions. RESULT We included 124 patients who met the criteria, and of those, 63 were PsP and 61 were TP. We trained a deep learning model that achieved 76.4% (ranged 70%-84%, SD 5.122) mean accuracy over the 5 folds, 0.7560(ranged 0.6553-0.8535, SD 0.069) mean AUROCC, 88.72% (SD 6.86) mean sensitivity, and 62.05%(SD 9.11) mean specificity. CONCLUSION We report the development of a deep learning model that distinguishes PsP from TP in GBM patients treated per the Stupp protocol. Further refinement and external validation are required prior to widespread adoption in clinical practice.

PURPOSE Though patients with glioblastoma generally have poor overall survival (OS), OS estimates range significantly. We sought to develop a radiomics-based model predictive of OS in glioblastoma using preoperative MRI scans to identify these differences and guide clinical care. METHODS The primary endpoint was OS, determined from the date of surgery to death or last follow-up. T1, FLAIR, T2, and post-contrast T1 (T1c) MRI sequences were obtained for all patients. Using HD-GLIO-AUTO, lesion segmentation yielded three masks corresponding to the regions of contrast enhancement, non-enhancement, and total volume. Radiomics features were extracted from each region from the T2 and T1c sequences independently (i.e. either from T2 or T1c) and in combination. Age at surgery and gender were also considered. The data was split into training (75%) and test (25%) sets, stratifying for age at surgery. Kaplan-Meier analysis and the Coxnet model were used. For Coxnet, Elastic Net was used as the penalty, with optimization of the L1 coefficient using 5-fold cross-validation and evaluation via the concordance index (C-index). RESULTS From 3 institutions, 383 patients were included. Median OS was 395 days: 346 patients died, and 37 were right-censored. Age > 65 years was predictive of decreased OS (p < 0.005), but gender was not (p=0.16). For the Coxnet model, the C-index was 0.67 (standard deviation: 0.01) considering T2 and T1c sequences for the total lesion volume, with a C-index of 0.65 for the test set. For the enhancing volume, the model had a C-index of 0.71 for the test set using T1c sequences. Overall, C-indices ranged from 0.62-0.71. CONCLUSIONS We present a novel radiomics-based model predictive of OS in glioblastoma across a multi-institutional dataset. Ongoing efforts will incorporate immediate postoperative imaging and IDH and MGMT status into the proposed model to further improve prognostic ability and help guide clinical decision-making.

Patients with primary central nervous system lymphoma (PCNSL) are treated with high-dose methotrexate-based chemotherapy, which requires hospitalization and extensive expertise to manage related toxicity. The use of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) could overcome these difficulties, but blood-brain barrier (BBB) penetration of related drugs is poor. Tumor necrosis factor-α coupled with NGR (NGR-hTNF), a peptide targeting CD13+ vessels, induces endothelial permeabilization and improves tumor access of cytostatics. We tested the hypothesis that NGR-hTNF can break the BBB, thereby improving penetration and activity of R-CHOP in patients with relapsed/refractory PCNSL (NCT03536039). Patients received six R-CHOP21 courses, alone at the first course and preceded by NGR-hTNF (0.8 μg/m2) afterward. This trial included 2 phases: an “explorative phase” addressing the effect of NGR-hTNF on drug pharmacokinetic parameters and on vessel permeability, assessed by dynamic contrast-enhanced magnetic resonance imaging and 99mTc-diethylene-triamine-pentacetic acid–single-photon emission computed tomography, and the expression of CD13 on tumor tissue; and an “expansion phase” with overall response rate as the primary end point, in which the 2-stage Simon Minimax design was used. At the first stage, if ≥4 responses were observed among 12 patients, the study accrual would have continued (sample size, 28). Herein, we report results of the explorative phase and the first-stage analysis (n = 12). CD13 was expressed in tumor vessels of all cases. NGR-hTNF selectively increased vascular permeability in tumoral/peritumoral areas, without interfering with drug plasma/cerebrospinal fluid concentrations. The NGR-hTNF/R-CHOP combination was well tolerated: there were only 2 serious adverse events, and grade 4 toxicity was almost exclusively hematological, which were resolved without dose reductions or interruptions. NGR-hTNF/R-CHOP was active, with 9 confirmed responses (75%; 95% confidence interval, 51-99), 8 of which were complete. In conclusion, NGR-hTNF/R-CHOP was safe in these heavily pretreated patients. NGR-hTNF enhanced vascular permeability specifically in tumoral/peritumoral areas, which resulted in fast and sustained responses.

Background and purposeEndocannabinoids are hypothesized to have anti-inflammatory and neuroprotective properties and hold therapeutic potential in the acute phase response mechanisms during acute cerebral ischemia and closed head injury. We set to describe the plasma levels of endocannabinoids and related ethanolamides during acute and subacute phases of cerebral ischemia.MethodsWe conducted a prospective observational study of plasma endocannabinoid levels in patients with acute ischemic stroke or transient ischemic attack. Two blood samples were collected: T1 (24 hours from symptom onset). N-arachidonoylethanolamine (AEA), 2-arachidonoylglycerol (2-AG), palmitoylethanolamide (PEA) and oleoylethanolamide (OEA) were quantified by liquid chromatography mass spectrometry.ResultsTwenty-three patients met inclusion criteria. Median (interquartile range): Age – 76 years (60-81); body mass index - 25.6 (23.6-30.4); National Institutes of Health Stroke Scale score-5(3-13); infarct volume - 1.4 cm3 (0.5-8.6). Higher 2-AG levels at T1 were correlated with smaller infarct volumes (Spearman ƿ=-0.48, p=0.0206). Levels of 2-AG were elevated at T2 compared to T1 in 48% of patients (median difference - 310.3nM, 95% CI 194.1-497.3; p=0.001); AEA, PEA and OEA did not differ between T1 and T2, p>0.05. Patients with elevated 2-AG at T2 had larger infarct volumes, p=0.0178, lower frequency of embolectomy performed, p=0.0373, but no difference in neurological disability 90 days after the ischemic event compared to patients without 2-AG elevation.Conclusion2-AG increases significantly in early phases of ischemic stroke. The final mechanistic role of 2-AG in acute ischemic stroke is to be determined in further studies.

BACKGROUND: Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. PURPOSE: To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for the use of a DL model for brain lesion segmentation that requires T1-weighted images, postcontrast T1-weighted images, fluid-attenuated inversion recovery (FLAIR) images, and T2-weighted images. MATERIALS AND METHODS: In this retrospective study, brain MRI scans obtained between 2011 and 2019 were collected, and scenarios were simulated in which the T1-weighted images and FLAIR images were missing. Two GANs were trained, validated, and tested using 210 glioblastomas (GBMs) (Multimodal Brain Tumor Image Segmentation Benchmark [BRATS] 2017) to generate T1-weighted images from postcontrast T1-weighted images and FLAIR images from T2-weighted images. The quality of the generated images was evaluated with mean squared error (MSE) and the structural similarity index (SSI). The segmentations obtained with the generated scans were compared with those obtained with the original MRI scans using the dice similarity coefficient (DSC). The GANs were validated on sets of GBMs and central nervous system lymphomas from the authors’ institution to assess their generalizability. Statistical analysis was performed using the Mann-Whitney, Friedman, and Dunn tests. RESULTS: Two hundred ten GBMs from the BRATS data set and 46 GBMs (mean patient age, 58 years ± 11 [standard deviation]; 27 men [59%] and 19 women [41%]) and 21 central nervous system lymphomas (mean patient age, 67 years ± 13; 12 men [57%] and nine women [43%]) from the authors’ institution were evaluated. The median MSE for the generated T1-weighted images ranged from 0.005 to 0.013, and the median MSE for the generated FLAIR images ranged from 0.004 to 0.103. The median SSI ranged from 0.82 to 0.92 for the generated T1-weighted images and from 0.76 to 0.92 for the generated FLAIR images. The median DSCs for the segmentation of the whole lesion, the FLAIR hyperintensities, and the contrast-enhanced areas using the generated scans were 0.82, 0.71, and 0.92, respectively, when replacing both T1-weighted and FLAIR images; 0.84, 0.74, and 0.97 when replacing only the FLAIR images; and 0.97, 0.95, and 0.92 when replacing only the T1-weighted images. CONCLUSION: Brain MRI scans generated using generative adversarial networks can be used as deep learning model inputs in case MRI sequences are missing. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Zhong in this issue. An earlier incorrect version of this article appeared online. This article was corrected on April 12, 2021.

BACKGROUND To investigate the correlation between 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET data and hypoxia immunohistochemical markers in patients with high-grade glioma (HGG). PATIENTS AND METHODS Prospective study including 20 patients with brain MRI suggestive for HGG and undergoing 18F-FAZA PET/CT before treatment for hypoxia assessment. For each 18F-FAZA PET scan SUVmax, SUVmean and 18F-FAZA tumour volume (FTV) at 40, 50 and 60% threshold of SUVmax were calculated; hypoxic volume was estimated by applying different thresholds (1.2, 1.3 and 1.4) to tumour/blood ratio. Seventeen patients were analysed. The immunohistochemical analysis assessed the following parameters: hypoxia-inducible factor 1α, carbonic anhydrase IX (CA-IX), glucose transporter-1, tumour vascularity and Ki-67. RESULTS 18F-FAZA PET showed a single lesion in 15/17 patients and multiple lesions in 2/17 patients. Twelve/17 patients had grade IV glioma and 5/17 with grade III glioma. Bioptic and surgical samples have been analysed separately. In the surgical subgroup (n = 7) a positive correlation was observed between CA-IX and SUVmax (P = 0.0002), SUVmean40 (P = 0.0058), SUVmean50 (P = 0.009), SUVmean60 (P = 0.0153), FTV-40-50-60 (P = 0.0424) and hypoxic volume1.2-1.3-1.4 (P = 0.0058). In the bioptic group (n = 10) tumour vascularisation was inversely correlated with SUVmax (P = 0.0094), SUVmean40 (P = 0.0107), SUVmean50 (P = 0.0094) and SUVmean60 (P = 0.0154). CONCLUSIONS The correlation of 18F-FAZA PET parameters with CD31 and CA-IX represents a reliable method for assessing tumour hypoxia in HGG. The inverse correlation between tumour vascularisation, SUVmax and SUVmean suggest that highly vascularized tumours might present more oxygen supply than hypoxia.

Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is the standard treatment of diffuse large B-cell lymphoma (DLBCL). Primary DLBCL of the central nervous system (CNS) (primary central nervous system lymphoma [PCNSL]) is an exception because of the low CNS bioavailability of related drugs. NGR–human tumor necrosis factor (NGR-hTNF) targets CD13+ vessels, enhances vascular permeability and CNS access of anticancer drugs, and provides the rationale for the treatment of PCNSL with R-CHOP. Herein, we report activity and safety of R-CHOP preceded by NGR-hTNF in patients with PCNSL relapsed/refractory to high-dose methotrexate-based chemotherapy enrolled in a phase 2 trial. Overall response rate (ORR) was the primary endpoint. A sample size of 28 patients was considered necessary to demonstrate improvement from 30% to 50% ORR. NGR-hTNF/R-CHOP would be declared active if ≥12 responses were recorded. Treatment was well tolerated; there were no cases of unexpected toxicities, dose reductions or interruptions. NGR-hTNF/R-CHOP was active, with confirmed tumor response in 21 patients (75%; 95% confidence interval, 59%-91%), which was complete in 11. Seventeen of the 21 patients with response to treatment received consolidation (ASCT, WBRT, and/or lenalidomide maintenance). At a median follow-up of 21 (range, 14-31) months, 5 patients remained relapse-free and 6 were alive. The activity of NGR-hTNF/R-CHOP is in line with the expression of CD13 in both pericytes and endothelial cells of tumor vessels. High plasma levels of chromogranin A, an NGR-hTNF inhibitor, were associated with proton pump inhibitor use and a lower remission rate, suggesting that these drugs should be avoided during TNF-based therapy. Further research on this innovative approach to CNS lymphomas is warranted. The trial was registered as EudraCT: 2014-001532-11.

Abnormalities on brain MRI may be identified in ~15% of individuals aged 50-66. It can be difficult to discriminate glioma, CNS lymphoma, Inflammatory Demyelinating Disease (CNSIDD) and solitary metastasis, and misdiagnosis may expose patients to unnecessary anxiety, surgery, or radiotherapy. CNS lymphoma requires only biopsy, solitary metastasis may be resected and radiated or radiated empirically, and high-grade glioma requires maximal safe resection followed by chemoradiation. CNSIDD should only be biopsied when diagnostic uncertainty requires it, and resection and radiotherapy are unnecessary, introducing unwarranted morbidity. Polygenic risk models can identify patients at the highest risk of developing glioma; we hypothesized that these polygenic models could help with differential diagnosis of indeterminate brain lesions. We also hypothesized that race would be an important contributing factor in the models. In the initial discovery and validation European (EUR) cohorts the mean probability of glioma for IDHmut non-codeleted glioma was 0.55 and 0.52, respectively, and in healthy controls was 0.19 and 0.21, respectively. To further evaluate sensitivity, we analyzed additional genotype data from 867 gliomas (764 EUR, 54 AFR, 24 AMR, 15 EAS, 10 SAS) from The Cancer Genome Atlas (TCGA). Across 764 EUR IDHmut non-codeleted glioma, the mean probability 0.53, whereas across 54 AFR and 24 AMR the mean was 0.22 and 0.32, respectively. To evaluate specificity, we analyzed 3200 TCGA patients with primary tumor types that commonly metastasize to the brain (2676 EUR, 365 AFR, 46 AMR, 95 EAS, 18 SAS), and 236 AFR healthy controls. For patients with non-CNS primary tumors, the mean probability ranged from 0.09-0.18 for EUR and 0.04-0.07 for AFR. For AFR healthy controls, the mean was 0.05. Overall, race is a significant factor for polygenic risk models. Further work entails evaluating polygenic risk models in IDHwt glioma, CNS lymphoma and CNSIDD cohorts as well as in additional races.

Objective To investigate the role of quantitative muscle biomarkers assessed with skeletal muscle index at the third lumbar vertebra (L3-SMI) and temporal muscle thickness (TMT) in predicting progression-free and overall survival in patients with primary central nervous system lymphoma (PCNSL) undergoing first-line high-dose methotrexate-based chemotherapy. Methods L3-SMI and TMT were calculated on abdominal CT and brain high-resolution 3D-T1-weighted MR images, respectively, using predefined validated methods. Standardized sex-specific cut-off values were used to divide patients in different risk categories. Kaplan-Meier plots were calculated, and survival analysis was performed using log-rank tests, univariate, and multivariable Cox-regression models, calculating hazard ratios (HR) and 95% confidence intervals (CI), also adjusting for potential confounders (age, sex, and performance status). Results Forty-three patients were included in this study. Median follow-up was 23 months (interquartile range 12–40); at median follow-up, rates of progression-free and overall survival for the cohort were 46% and 57%, respectively. Thirteen (30%) and 11 (26%) patients showed L3-SMI or TMT values below the predefined cut-offs. In Cox-regression multivariable analysis patients with low L3-SMI or TMT showed significantly worse progression-free (HR 4.40, 95% CI 1.66–11.61, p = 0.003; HR 4.40, 95% CI 1.68–11.49, p = 0.003, respectively) and overall survival (HR 3.16, 95% CI 1.09–9.11, p = 0.034; HR 4.93, 95% CI 1.78–13.65, p = 0.002, respectively) compared to patients with high L3-SMI or TMT. Conclusions Quantitative muscle mass evaluation assessed by both L3-SMI and TMT is a promising tool to identify PCNSL patients at high risk of negative outcome. Confirmatory studies on larger independent series are warranted.

BACKGROUND Appropriate patient stratification is of paramount importance in patients with primary central nervous system lymphomas (PCNSL) in order to improve therapeutic choices and to reduce treatment-related neurotoxicity. Age and performance status, two widely used prognostic indicators, may not be the most appropriate parameters for an optimal patient stratification, as age seldom reveals the true biological frailty of a patient and performance status scores are subject to inter-rater variability. Quantitative muscle biomarkers such as the skeletal-muscle-index at the third lumbar vertebra (L3-SMI) and temporal muscle thickness (TMT) are associated with worse prognosis in several oncological diseases. We aim to evaluate the role of these biomarkers in predicting survival in patients with PCNSL undergoing high-dose methotrexate-based chemotherapy. MATERIAL AND METHODS L3-SMI and TMT were calculated on abdominal CT and brain high-resolution 3D-T1-weighted-MR images, respectively, using predefined validated methods. Standardized sex-specific cut-offs were used to divide patients in different risk categories. Kaplan-Meier plots were calculated, and survival analysis was performed using log-rank tests, univariate, and multivariable Cox-regression models, calculating hazard ratios (HR) and 95% confidence intervals (CI), also adjusting for potential confounders (age, sex, and performance status). RESULTS Forty-three patients were included in this study. Median follow-up was 23 months (interquartile range 12–40); at median follow-up, rates of progression-free and overall survival for the cohort were 46% and 57%, respectively. Thirteen (30%) and 11 (26%) patients showed L3-SMI or TMT values below the predefined cut-offs. Subgroup analyses showed a significant association between quantitative muscle mass biomarkers and progression-free and overall survival. One-year progression free and overall survival rates were 8% and 21% for the 13 patients with L3-SMI below the standard cut-off value, respectively, compared to 66% and 68% for the 30 patients with L3-SMI above the cut-off values. Likewise, one-year progression free and overall survival rates were 10% and 15% for the 11 patients with low TMT, respectively, compared to 61% and 70% for the 32 patients with high TMT. In Cox-regression multivariable analysis patients with low L3-SMI or TMT showed significantly worse progression-free (HR 4.40, 95%CI 1.66–11.61, p = 0.003; HR 4.40, 95%CI 1.68–11.49, p=0.003, respectively) and overall survival (HR 3.16, 95%CI 1.09–9.11, p = 0.034; HR 4.93, 95%CI 1.78–13.65, p=0.002, respectively) compared to patients with high L3-SMI or TMT. CONCLUSION Quantitative muscle mass evaluation assessed by both L3-SMI and TMT is a promising tool to identify PCNSL patients at high risk of negative outcome. Confirmatory studies on larger independent series are warranted.

BackgroundMachine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.Methods333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.ResultsFeatures from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1.ConclusionDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.Key points-On highly heterogenous, multi-centre data, machine learning on DSC-MRI features can correctly predict glioma IDH subtyping in 71% of cases and glioma grade II-IV in 53% of the cases (87% -Shape features distinguish best grade II from grade III gliomas.-Texture and distribution features distinguish best grade III from grade IV tumours.Importance of studyThis work illustrates the diagnostic value of combining machine learning and dynamic susceptibility contrast-enhanced MRI (DSC-MRI) radiomics in classifying gliomas into WHO grades II-IV as well as across their isocitrate dehydrogenase (IDH) mutation status. Despite the data heterogeneity inherent to the multi-centre design of the studied cohort (333 subjects, 6 centres) that greatly increases the theoretical challenges of machine learning frameworks, good classification performance (accuracy of 53% across grades (87% With its strong generalisability property, its ability to further incorporate participating centres and its possible use to identify borderline cases, the proposed machine learning framework has the potential to contribute to the clinical translation of machine-learning assisted diagnostic tools in neuro-oncology.

Introduction Tumefactive multiple sclerosis (MS) and glioblastoma (GBM) can be difficult to differentiate. Clinical presentations are nonspecific and may occur in patients with both diseases. Magnetic resonance imaging (MRI) scans are routinely used to generate an initial differential diagnosis. Although imaging features can help distinguish these entities, there are no pathognomonic signs, neither on traditional nor advanced MRI. Moreover, the ability to differentiate these entities depends on the radiologists’ experience. Ensuring the correct diagnosis has important therapeutic and prognostic implications, as the management of these diseases is very different. To date, a brain biopsy often represents the only option to obtain a diagnosis, but it may be inconclusive and could add unnecessary morbidity. We hypothesize that a machine-learning tool based on routinely performed MRI scans could guarantee a more reliable, fast, and reproducible diagnosis. Methods Only patients with a histological diagnosis were included in the study. Patients that underwent any form of radiation therapy before the surgery were excluded to avoid confounding factors due to treatment-related effects. A total of 99 MS and 257 GBMs were included, and preoperative T2-weighted images were collected. For each patient, the lesions' segmentation was performed, including both the contrast-enhancing and non-enhancing areas of the lesions. The segmentation masks were used to extract radiomic features from the T2-weighted sequences. All MRI images were standardized to the same mean and standard deviation before feature extraction and resampled to a voxel size of 1.0 × 1.0 × 1.0 mm. Features extraction was performed using Pyradiomics (v. 3.0.1) and included first order, shape, and texture features, totaling 100 features. The classifier consisted of a support vector machine (SVM) model. Patients were randomly assigned to the training and test sets with an 80-20% proportion for each class. To select the best hyperparameters for the SVM classifier, a grid-search analysis was performed on the training set using a nested cross-validation scheme (k=5). All experiments were performed using Python (v. 3.5) and the Scikit-learn library (v. 0.23.1). Results The mean accuracy and area under the ROC curve for the best set of hyperparameters on the validation set were 0.83 and 0.86, respectively. The weighted-average precision, recall, and F1 scores on the test set were 0.78, 0.76, and 0.77, respectively. Conclusions Radiomic features extracted from a routinely acquired MRI sequence showed promising results in differentiating tumefactive multiple sclerosis from glioblastoma, opening the possibility to obtain a reliable diagnosis without the need for a brain biopsy. Future experiments Future experiments will focus on using post-contrast T1-weighted images for the radiomic features extractions, testing multiple machine learning models and features selection algorithms, and including more diagnostic classes (i.e., metastasis, lymphomas). Citation Format: Gian Marco Conte, Jeanette E. Eckel-Passow, W. Oliver Tobin, Paul Decker, Daniel H. Lachance, Robert B. Jenkins, Bradley J. Erickson. Differentiation of tumefactive multiple sclerosis and glioblastoma using radiomics features extracted from magnetic resonance imaging and machine learning [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-023.

Introduction Dynamic susceptibility contrast (DSC)-MRI is a perfusion technique with high diagnostic accuracy for glioma grading, despite limitations due to inherent susceptibility effects. Dynamic contrast-enhanced (DCE)-MRI has been proposed as an alternative technique able to overcome the DSC-MRI shortcomings. This pilot study aimed at comparing the diagnostic accuracy of DSC and DCE-MRI for glioma grading by evaluating two estimates of blood volume, the DCE-derived plasma volume (Vp) and the DSC-derived relative cerebral blood volume (rCBV), and a measure of vessel permeability, the DCE-derived volume transfer constant K trans . Methods Twenty-six newly diagnosed glioma patients underwent 3T-MR DCE and DSC imaging. Parametric maps of CBV, Vp and K trans were calculated and the region of highest value ( hotspot ) was measured on each map. Histograms of rCBV, Vp and K trans values were calculated for the tumor volume. Statistical differences according to WHO grade were assessed. The diagnostic accuracy for tumor grading of the two techniques was determined by ROC analysis. Results rCBV, Vp and K trans measures differed significantly between high and low-grade gliomas. Hotspot analysis showed the highest correlation with grading. K trans hotspots co-localized with Vp hotspots only in 56% of enhancing gliomas. For differentiating high from low-grade gliomas the AUC was 0.987 for rCBV max , and 1.000 for Vp max and K trans max . Combination of DCE-derived Vp and K trans parameters improved the diagnostic performance of the histogram method. Conclusion This initial experience of DCE-derived Vp evaluation shows that this parameter is as accurate as the well-established DSC-derived rCBV for glioma grading. DCE-derived K trans is equally useful for grading, providing different informations with respect to Vp.

PURPOSE: To develop a deep learning model that segments intracranial structures on head CT scans. MATERIALS AND METHODS: In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27–95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. The dataset was split into 40 scans for training, 10 for validation, and 12 for testing. After initial training, eight model configurations were evaluated on the validation dataset and the highest performing model was evaluated on the test dataset. Interobserver variability was reported using multirater consensus labels obtained from the test dataset. To ensure that the model learned generalizable features, it was further evaluated on two secondary datasets containing 12 volumes with idiopathic normal pressure hydrocephalus (iNPH) and 30 normal volumes from a publicly available source. Statistical significance was determined using categorical linear regression with P < .05. RESULTS: Overall Dice coefficient on the primary test dataset was 0.84 ± 0.05 (standard deviation). Performance ranged from 0.96 ± 0.01 (brainstem and cerebrum) to 0.74 ± 0.06 (internal capsule). Dice coefficients were comparable to expert annotations and exceeded those of existing segmentation methods. The model remained robust on external CT scans and scans demonstrating ventricular enlargement. The use of within-network normalization and class weighting facilitated learning of underrepresented classes. CONCLUSION: Automated segmentation of CT neuroanatomy is feasible with a high degree of accuracy. The model generalized to external CT scans as well as scans demonstrating iNPH. Supplemental material is available for this article. © RSNA, 2020

To compare dynamic contrast-enhanced MRI (DCE-MRI) data obtained using different prebolus T1 values in glioma grading and molecular profiling. We retrospectively reviewed 83 cases of gliomas: 46 lower-grade gliomas (LGG; grades II and III) and 37 high-grade gliomas (HGG; grade IV). DCE-MRI maps of plasma volume fraction (Vp), extravascular-extracellular volume fraction (Ve), and tracer transfer constant from plasma to tissue (Ktrans) were obtained using a fixed T1 value of 1400 ms and a measured T1 obtained with variable flip angle (VFA). Tumour segmentations were performed and first-order histogram parameters were extracted from volumes of interest (VOIs) after co-registration with the perfusion maps. The two methods were compared using Wilcoxon matched-pairs signed-rank test and Bland-Altman analysis. Diagnostic accuracy was obtained and compared using ROC curve analysis and DeLong’s test. Perfusion parameters obtained with the fixed T1 value were significantly higher than those obtained with the VFA. As regards diagnostic accuracy, there were no significant differences between the two methods both for glioma grading and molecular classification, except for few parameters of both methods. DCE-MRI data obtained with different prebolus T1 are not comparable and the definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI. • DCE-MRI data obtained with different prebolus T1 are significantly different, thus not comparable. • The definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI for glioma grading. • The use of a fixed T1 value represents a valid alternative to T1 mapping for DCE-MRI analysis.

Purpose To evaluate the feasibility of a standardized protocol for acquisition and analysis of dynamic contrast material-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance (MR) imaging in a multicenter clinical setting and to verify its accuracy in predicting glioma grade according to the new World Health Organization 2016 classification. Materials and Methods The local research ethics committees of all centers approved the study, and informed consent was obtained from patients. One hundred patients with glioma were prospectively examined at 3.0 T in seven centers that performed the same preoperative MR imaging protocol, including DCE and DSC sequences. Two independent readers identified the perfusion hotspots on maps of volume transfer constant (Ktrans), plasma (vp) and extravascular-extracellular space (ve) volumes, initial area under the concentration curve, and relative cerebral blood volume (rCBV). Differences in parameters between grades and molecular subtypes were assessed by using Kruskal-Wallis and Mann-Whitney U tests. Diagnostic accuracy was evaluated by using receiver operating characteristic curve analysis. Results The whole protocol was tolerated in all patients. Perfusion maps were successfully obtained in 94 patients. An excellent interreader reproducibility of DSC- and DCE-derived measures was found. Among DCE-derived parameters, vpand vehad the highest accuracy (are under the receiver operating characteristic curve [Az] = 0.847 and 0.853) for glioma grading. DSC-derived rCBV had the highest accuracy (Az= 0.894), but the difference was not statistically significant (P > .05). Among lower-grade gliomas, a moderate increase in both vpand rCBV was evident in isocitrate dehydrogenase wild-type tumors, although this was not significant (P > .05). Conclusion A standardized multicenter acquisition and analysis protocol of DCE and DSC MR imaging is feasible and highly reproducible. Both techniques showed a comparable, high diagnostic accuracy for grading gliomas.©RSNA, 2018 Online supplemental material is available for this article.

Introduction Fractionated radiotherapy in brain tumors is commonly associated with several detrimental effects, largely related to the higher radiosensitivity of the white matter (WM) with respect to gray matter. However, no dose constraints are applied to preserve WM structures at present. Magnetic Resonance (MR) Tractography is the only technique that allows to visualize in vivo the course of WM eloquent tracts in the brain. In this study, the feasibility of integrating MR Tractography in tomotherapy treatment planning has been investigated, with the aim to spare eloquent WM regions from the dose delivered during treatment. Methods Nineteen high grade glioma patients treated with fractionated radiotherapy were enrolled. All the patients underwent pre-treatment MR imaging protocol including Diffusion Tensor Imaging (DTI) acquisitions for MR Tractography analysis. Bilateral tracts involved in several motor, language, cognitive functions were reconstructed and these fiber bundles were integrated into the Tomotherapy Treatment planning system. The original plans without tracts were compared with the optimized plans incorporating the fibers, to evaluate doses to WM structures in the two differently optimized plans. Results No significant differences were found between plans in terms of planning target volume (PTV) coverage between the original plans and the optimized plans incorporating fiber tracts. Comparing the mean as well as the maximal dose (Dmean and Dmax), a significant dose reduction was found for most of the tracts. The dose sparing was more relevant for contralateral tracts (P Conclusion The integration of MR Tractography into radiotherapy planning is feasible and beneficial to preserve important WM structures without reducing the clinical goal of radiation treatment.

Purpose: Hypoxia can influence response to chemo and radiotherapy in different tumours, including non-small cell lung cancer (NSCLC) and high-grade gliomas (HGG). PET/CT can non-invasively investigate hypoxia and18F-FAZA seems to be the most promising radiotracer. However, there are still controversial issues related to image analysis and data interpretation hampering the effective possibility to translate the use of this imaging modality in clinical applications. The aim of the present pictorial review is to provide insights and answers on the open issues related to the potential clinical applicability of18F-FAZA PET/CT for hypoxia delineation. Methods: From Pubmed and Scopus databases, a literature research has been performed with the following research filters for both NSCLC and HGG: (1) time frame: last 10 years; (2) language: English; (3) species: human. Applied searching keywords were “hypoxia” or “hypoxic” and “PET” and “lung cancer” for NSCLS and “hypoxia”, “PET” and “glioma” for HGG. Papers not strictly matching the imposed search filters have not been considered. Results: The literature search led to 76 papers for NSCLC, but for the purpose of the review only 10 have been considered, after double-checking and exclusion if not matching the imposed search filters. For HGG, the selected filters lead to 19 papers but only 8 have been considered for data analysis. Results on the use of18F-FAZA PET/CT in two different settings are reported in the present pictorial essay and methodological suggestions for clinical practice are presented through the description of addressed representative case reports. Conclusions: Based on literature evidence and presentation of relevant clinical case reports, issues related to18F-FAZA PET/CT in clinical settings have been addressed, providing possible solutions that may help the reliable use of this imaging method in clinical practice.

A 57 year-old man underwent MRI with dynamic susceptibility contrast and dynamic contrast-enhanced perfusion for neurological symptoms suggesting the diagnosis of high-grade glioma. A F-FAZA PET/CT was performed because of the enrollment in a prospective clinical trial. Subsequent radiotherapy treatment has been planned based on conventional imaging; moreover, a F-FAZA PET/CT-guided treatment planning highlighting hypoxic regions has been simulated. After radiotherapy treatment, the man underwent MRI and F-FAZA PET/CT, showing partial response.

Purpose Radiotherapy in brain tumors is associated with several detrimental effects. White-matter (WM) regions are associated with higher radiosensitivity respect to grey-matter but no dose constraints are used [1] . Magnetic Resonance(MR)-Tractography is the only technique that allows to in-vivo visualize WM eloquent tracts adjacent/inside brain tumors [2] . In this work feasibility of MR-Tractography integration in treatment planning is investigated aiming to spare eloquent regions from dose delivered during RT-treatment. Methods 19 patients with high-grade-gliomas underwent MR acquisitions before RT (1.5 T scanner), including structural images and DTI sequence (32 or 15 directions, b-value = 1000 s/mm2) and structural imaging for Target and OAR definition. RT-treatment was delivered with helical tomotherapy. Bilateral tracts considered are: corticospinal tracts (CST), superior-longitudinal fascicle (SLF), inferior fronto-occipital fascicle (IFOF) and uncinate fascicle (UNC). Tracts were then converted in binary masks and then imported in TPS for automatic contouring. The original plans were compared with optimized plans including WM-tracts. Results No significant differences were found for PTV coverage between plans (p = 0.29). Comparing mean-dose and Dmax significant dose reduction for all tracts was found (results for mean-dose are reported in table for contralateral and omolateral-tracts). DVH analyses showed that low doses are reduced for contralateral-tracts and V30 for all tracts except for CST (p = 0.28). For omolateral-tracts significantly reduction was achieved for SLF and IFOF (p Conclusions Integration of MR-Tractography into RT-planning is feasible and beneficial to spare important WM structures without reducing clinical goal of RT-treatment.

Dynamic susceptibility contrast MRI (DSC) and dynamic contrast-enhanced MRI (DCE) are useful tools in the diagnosis and follow-up of brain gliomas; nevertheless, both techniques leave the open issue of data reproducibility. We evaluated the reproducibility of data obtained using two different commercial software for perfusion maps calculation and analysis, as one of the potential sources of variability can be the software itself. DSC and DCE analyses from 20 patients with gliomas were tested for both the intrasoftware (as intraobserver and interobserver reproducibility) and the intersoftware reproducibility, as well as the impact of different postprocessing choices [vascular input function (VIF) selection and deconvolution algorithms] on the quantification of perfusion biomarkers plasma volume (Vp), volume transfer constant (K trans) and rCBV. Data reproducibility was evaluated with the intraclass correlation coefficient (ICC) and Bland–Altman analysis. For all the biomarkers, the intra- and interobserver reproducibility resulted in almost perfect agreement in each software, whereas for the intersoftware reproducibility the value ranged from 0.311 to 0.577, suggesting fair to moderate agreement; Bland–Altman analysis showed high dispersion of data, thus confirming these findings. Comparisons of different VIF estimation methods for DCE biomarkers resulted in ICC of 0.636 for K trans and 0.662 for Vp; comparison of two deconvolution algorithms in DSC resulted in an ICC of 0.999. The use of single software ensures very good intraobserver and interobservers reproducibility. Caution should be taken when comparing data obtained using different software or different postprocessing within the same software, as reproducibility is not guaranteed anymore.

Background: Almost all PCNSL belong to the category of DLBCL. However, affected pts are treated with high-dose methotrexate-based combinations that are not currently used in other DLBCL and require hospitalization and extensive expertise to manage toxicity. The use of R-CHOP could overcome these difficulties, but CNS availability of related drugs is poor. TNF induces BBB permeabilization and enhances CNS access of anticancer drugs. Coupling TNF with NGR, a peptide that targets CD13+ tumor vessels, improves its biological effects and therapeutic index. Thus, we tested the hypothesis that this conjugate (called NGR-hTNF) can break the BBB, thereby improving CNS access and activity of R-CHOP in pts with r/rPCNSL enrolled in a phase II trial (NCT03536039). Herein, we report results of activity, safety and BBB permeabilization. Methods: HIV-neg adults with PCNSL failed after methotrexate-based chemo and measurable disease were enrolled and treated with 1 course of R-CHOP, followed by 5 courses of R-CHOP21 preceded by NGR-hTNF (0.8 μg/m², 1-h inf). Overall response rate (ORR) was the primary endpoint. The two-stage Simon Minimax design was used; sample size estimated to demonstrate an improvement from 30% ORR (P0) to 50% (P1) (one-sided test; α 10%; β 90%) was 28 pts. NGR-hTNF/RCHOP would be declared active if ≥12 responses were recorded. As secondary endpoints, changes in vessel permeability, CD13 expression and anticancer drugs pharmacokinetics were assessed in the first 10 pts. Changes in BBB permeability were assessed by Dynamic Contrast Enhanced MRI (DCE-MRI) and 99mTc-DTPA-SPECT in tumor lesions, perilesional areas and normal appearing brain. DCE-MRI findings recorded before/after the 1st course (RCHOP alone - baseline) and before/after the 2nd and 6th courses (NGR-hTNF/RCHOP) were compared to establish the effect of TNF, and results were expressed as Ktrans values normalized using contralateral white matter. SPECT was performed before and after the 3rd course, and results were expressed as changes in the volume of ≥30% 99mTc-DTPA uptake (cm3). CD13 expression was assessed by immunohistochemistry on diagnostic tissue samples. Rituximab, cyclophosphamide and doxorubicin concentrations on matched CSF/plasma samples collected before/after the 1st, 2nd and 6th courses were tested to exclude a non-specific effect of NGR-hTNF on pharmacokinetics. Results: 22 pts (median age: 58 yo, range 26-78; 11 males) were enrolled; 18 pts had intermediate-high IELSG score. Pts were heavily pretreated: 13 had received ASCT, WBRT or both; 13 had refractory disease. Twenty pts were evaluable for the primary endpoint. NGR-hTNF/RCHOP combination was active: the predetermined activity threshold (≥12 responses) was achieved, with confirmed tumor response in 13 pts (65%; 95%CI= 45-85%), which was complete in 9. At a median follow-up of 8 months (2-26), 9 pts remain relapse free and 12 pts are alive. Treatment was well tolerated; toxicities were quickly solved without dose reductions or interruptions. G4 toxicities were neutropenia (49% of courses) and thrombocytopenia (15%), anemia (1%), and FN (1%). Ten SAEs were recorded in 8 pts: g2 seizures (n= 2), g2 DVT (2), g3 infections (2), g3 syncope (2), g4 FN and g2 LVEF reduction. There were 3 cases of g1-2 TNF infusion reaction. DCE-MRI studies showed an increase of vascular permeability after NGR-hTNF infusion as median (range) Ktrans of tumor and perilesional areas raised from baseline values of 23.5 (6.8-98.8) and 2.5 (0.4-3.9) to 35.3 (23.9-887.7; p= 0.39) and 4.7 (2.2-37.7; p= 0.01), respectively. Likewise, SPECT studies showed a significant enlargement of the volume ≥30% 99mTc-DTPA uptake, with median (range) values before and after NGR-hTNF infusion of 26 cm3 (5 - 67) and 40 cm3 (10 - 92), respectively (p= 0.02), with a median volume increase of 45% (14-87%). As important features supporting the specificity of the effect of NGR-hTNF, CD13 was expressed in all diagnostic samples, and drug levels in CSF/plasma samples were not influenced by NGR-hTNF. Conclusions: NGR-hTNF/RCHOP is active and safe in pts with r/rPCNSL. NGR-hTNF enhances vascular permeability specifically in tumor lesions and perilesional areas, which was consistently demonstrated by DCE-MRI, SPECT and plasma/CSF pharmacokinetics studies and was in line with CD13 expression. Accrual completion is warranted. This innovative approach deserves to be addressed as first-line treatment in PCNSL pts. Disclosures Angelucci: Novartis: Honoraria, Other: Chair Steering Comiittee TELESTO Protocol; Vertex Pharmaceuticals Incorporated (MA) and CRISPR CAS9 Therapeutics AG (CH): Other: Chair DMC; Jazz Pharmaceuticals Italy: Other: Local ( national) advisory board; Roche Italy: Other: Local (national) advisory board; Celgene: Honoraria, Other: Chair DMC.

7575Background: PCNSL pts are treated with HDMTX-based chemo, which requires hospitalization and extensive expertise to manage toxicity. R-CHOP could overcome these troubles, but CNS access of rela...