Mixed histiocytic neoplasms: A multicentre series revealing diverse somatic mutations and responses to targeted therapy

Histiocytic neoplasms are diverse clonal haematopoietic disorders, and clinical disease is mediated by tumorous infiltration as well as uncontrolled systemic inflammation. Individual subtypes include Langerhans cell histiocytosis (LCH), Rosai-Dorfman-Destombes disease (RDD) and Erdheim-Chester disease (ECD), and these have been characterized with respect to clinical phenotypes, driver mutations and treatment paradigms. Less is known about patients with mixed histiocytic neoplasms (MXH), that is two or more coexisting disorders. This international collaboration examined patients with biopsy-proven MXH with respect to component disease subtypes, oncogenic driver mutations and responses to conventional (chemotherapeutic or immunosuppressive) versus targeted (BRAF or MEK inhibitor) therapies. Twenty-seven patients were studied with ECD/LCH (19/27), ECD/RDD (6/27), RDD/LCH (1/27) and ECD/RDD/LCH (1/27). Mutations previously undescribed in MXH were identified, including KRAS, MAP2K2, MAPK3, non-V600-BRAF, RAF1 and a BICD2-BRAF fusion. A repeated-measure generalized estimating equation demonstrated that targeted treatment was statistically significantly (1) more likely to result in a complete response (CR), partial response (PR) or stable disease (SD) (odds ratio [OR]: 17.34, 95% CI: 2.19-137.00, p = 0.007), and (2) less likely to result in progression (OR: 0.08, 95% CI: 0.03-0.23, p < 0.0001). Histiocytic neoplasms represent an entity with underappreciated clinical and molecular diversity, poor responsiveness to conventional therapy and exquisite sensitivity to targeted therapy.

Hypoxia-Directed Treatment of Human Papillomavirus-Related Oropharyngeal Carcinoma

PURPOSE

Standard curative-intent chemoradiotherapy for human papillomavirus (HPV)-related oropharyngeal carcinoma results in significant toxicity. Since hypoxic tumors are radioresistant, we posited that the aerobic state of a tumor could identify patients eligible for de-escalation of chemoradiotherapy while maintaining treatment efficacy.

METHODS

We enrolled patients with HPV-related oropharyngeal carcinoma to receive de-escalated definitive chemoradiotherapy in a phase II study (ClinicalTrials.gov identifier: NCT03323463). Patients first underwent surgical removal of disease at their primary site, but not of gross disease in the neck. A baseline 18F-fluoromisonidazole positron emission tomography scan was used to measure tumor hypoxia and was repeated 1-2 weeks intratreatment. Patients with nonhypoxic tumors received 30 Gy (3 weeks) with chemotherapy, whereas those with hypoxic tumors received standard chemoradiotherapy to 70 Gy (7 weeks). The primary objective was achieving a 2-year locoregional control (LRC) of 95% with a 7% noninferiority margin.

RESULTS

One hundred fifty-eight patients with T0-2/N1-N2c were enrolled, of which 152 patients were eligible for analyses. Of these, 128 patients met criteria for 30 Gy and 24 patients received 70 Gy. The 2-year LRC was 94.7% (95% CI, 89.8 to 97.7), meeting our primary objective. With a median follow-up time of 38.3 (range, 22.1-58.4) months, the 2-year progression-free survival (PFS) and overall survival (OS) rates were 94% and 100%, respectively, for the 30-Gy cohort. The 70-Gy cohort had similar 2-year PFS and OS rates at 96% and 96%, respectively. Acute grade 3-4 adverse events were more common in 70 Gy versus 30 Gy (58.3% v 32%; P = .02). Late grade 3-4 adverse events only occurred in the 70-Gy cohort, in which 4.5% complained of late dysphagia.

CONCLUSION

Tumor hypoxia is a promising approach to direct dosing of curative-intent chemoradiotherapy for HPV-related carcinomas with preserved efficacy and substantially reduced toxicity that requires further investigation.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images

Objectives

Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients.

Methods

This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant.

Results

No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively.

Conclusions

The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC.

Advances in knowledge

First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI

OBJECTIVES

While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

METHODS

In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation.

RESULTS

Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001.

CONCLUSIONS

Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

KEY POINTS

• A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

Artificial Intelligence in CT and MR Imaging for Oncological Applications

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Qualitative and Quantitative Performance of Magnetic Resonance Image Compilation (MAGiC) Method: An Exploratory Analysis for Head and Neck Imaging

The present exploratory study investigates the performance of a new, rapid, synthetic MRI method for diagnostic image quality assessment and measurement of relaxometry metric values in head and neck (HN) tumors and normal-appearing masseter muscle. The multi-dynamic multi-echo (MDME) sequence was used for data acquisition, followed by synthetic image reconstruction on a 3T MRI scanner for 14 patients (3 untreated and 11 treated). The MDME enables absolute quantification of physical tissue properties, including T1 and T2, with a shorter scan time than the current state-of-the-art methods used for relaxation measurements. The vendor termed the combined package MAGnetic resonance imaging Compilation (MAGiC). In total, 48 regions of interest (ROIs) were analyzed, drawn on normal-appearing masseter muscle and tumors in the HN region. Mean T1 and T2 values obtained from normal-appearing muscle were 880 ± 52 ms and 46 ± 3 ms, respectively. Mean T1 and T2 values obtained from tumors were 1930 ± 422 ms and 77 ± 13 ms, respectively, for the untreated group, 1745 ± 410 ms and 107 ± 61 ms, for the treated group. A total of 1552 images from both synthetic MRI and conventional clinical imaging were assessed by the radiologists to provide the rating for T1w and T2w image contrasts. The synthetically generated qualitative T2w images were acceptable and comparable to conventional diagnostic images (93% acceptability rating for both). The acceptability ratings for MAGiC-generated T1w, and conventional images were 64% and 100%, respectively. The benefit of MAGiC in HN imaging is twofold, providing relaxometry maps in a clinically feasible time and the ability to generate a different combination of contrast images in a single acquisition.

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model. Materials and Methods In this retrospective study, the cancer center PACS was mined for brain MRI scans acquired between January 2012 and December 2017 and included all annotated axial T1 postcontrast images. Line annotations were converted to boxes, excluding boxes shorter than 1 cm or longer than 7 cm. The resulting boxes were used for supervised training of object detection models using RetinaNet and Mask region-based convolutional neural network (R-CNN) architectures. The best-performing model trained from the mined data set was used to detect unannotated tumors on training images themselves (self-labeling), automatically correcting many of the missing labels. After self-labeling, new models were trained using this expanded data set. Models were scored for precision, recall, and F1 using a held-out test data set comprising 754 manually labeled images from 100 patients (403 intra-axial and 56 extra-axial enhancing tumors). Model F1 scores were compared using bootstrap resampling. Results The PACS query extracted 31 150 line annotations, yielding 11 880 boxes that met inclusion criteria. This mined data set was used to train models, yielding F1 scores of 0.886 for RetinaNet and 0.908 for Mask R-CNN. Self-labeling added 18 562 training boxes, improving model F1 scores to 0.935 (P < .001) and 0.954 (P < .001), respectively. Conclusion The application of semisupervised learning to mined image annotations significantly improved tumor detection performance, achieving an excellent F1 score of 0.954. This development pipeline can be extended for other imaging modalities, repurposing unused data silos to potentially enable automated tumor detection across radiologic modalities. © RSNA, 2022 Online supplemental material is available for this article.

Prognostic value of [18F]FDG PET/CT in patients with CNS lymphoma receiving ibrutinib-based therapies

PURPOSE

Current clinical and imaging tools remain suboptimal for predicting treatment response and prognosis in CNS lymphomas. We investigated the prognostic value of baseline [18F]FDG PET in patients with CNS lymphoma receiving ibrutinib-based treatments.

METHODS

Fifty-three patients enrolled in a prospective clinical trial and underwent brain PET before receiving single-agent ibrutinib or ibrutinib in combination with methotrexate with or without rituximab. [18F]FDG uptake in these lesions was quantified by drawing PET volumes of interest around up to five [18F]FDG-avid lesions per patient (with uptake greater than surrounding brain). We measured standardized uptake values (SUVmax), metabolic tumor volumes, total lesion glycolysis (TLG), and the sum thereof in these lesions. We analyzed the relationship between PET parameters and mutation status, overall response rates, and progression-free survival (PFS).

RESULTS

Thirty-eight patients underwent single-agent therapy and 15 received combination therapy. On PET, 15/53 patients had no measurable disease. In the other 38 patients, a total of 71 lesions were identified on PET. High-intensity [18F]FDG uptake and a larger volume of [18F]FDG-avid disease were inversely related to treatment outcome (p ≤ 0.005). In univariable analysis, PFS was linearly correlated with all PET parameters, with stronger association when sum-values were used. A multivariable model showed that risk of progression increased by 9% for every 5-unit increase in sumSUVmax (hazard ratio = 1.09 [95% CI: 1.04 to 1.14]).

CONCLUSION

Higher lesional metabolic parameters are inversely related to outcome in patients undergoing ibrutinib-based therapies, and sumSUVmax emerged as a strong independent prognostic factor.

TRIAL REGISTRATION

NCT02315326; https://clinicaltrials.gov/ct2/show/NCT02315326?term=NCT02315326&draw=2&rank=1.

Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas

The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including 18F-FDG-PET/CT, 18F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) were investigated using the CDA based on a "spin-glass model" coupled with the Spearman's rank, ρ, analysis. Mean MRI T2 weighted tumor volumes and SULmean values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SULmean (ρ ≤ -0.42, p < 0.03 for both). K1 and Ktrans were positively correlated (ρ = 0.48, p = 0.01). In contrast, Ktrans and k3max were negatively correlated (ρ = -0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, Ktrans, and K1 had 8, 7, and 6 edges in the network, respectively. After validation in a larger population, the CDA approach may aid in identifying useful biomarkers for developing individual patient care in HNSCC.

Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis

We developed and tested the feasibility of computational fluid modeling (CFM) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantitative estimation of interstitial fluid pressure (IFP) and velocity (IFV) in patients with head and neck (HN) cancer with locoregional lymph node metastases. Twenty-two patients with HN cancer, with 38 lymph nodes, underwent pretreatment standard MRI, including DCE-MRI, on a 3-Tesla scanner. CFM simulation was performed with the finite element method in COMSOL Multiphysics software. The model consisted of a partial differential equation (PDE) module to generate 3D parametric IFP and IFV maps, using the Darcy equation and Ktrans values (min⁻¹, estimated from the extended Tofts model) to reflect fluid influx into tissue from the capillary microvasculature. The Spearman correlation (ρ) was calculated between total tumor volumes and CFM estimates of mean tumor IFP and IFV. CFM-estimated tumor IFP and IFV mean ± standard deviation for the neck nodal metastases were 1.73 ± 0.39 (kPa) and 1.82 ± 0.9 × (10⁻⁷ m/s), respectively. High IFP estimates corresponds to very low IFV throughout the tumor core, but IFV rises rapidly near the tumor boundary where the drop in IFP is precipitous. A significant correlation was found between pretreatment total tumor volume and CFM estimates of mean tumor IFP (ρ = 0.50, P = 0.004). Future studies can validate these initial findings in larger patients with HN cancer cohorts using CFM of the tumor in concert with DCE characterization, which holds promise in radiation oncology and drug-therapy clinical trials.

Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering

Purpose: The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. Approach: We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients. The largest common network component between the two networks was tested on phantom data consisting of five cancer samples. To further investigate whether radiomic features found can identify phenotypes, we propose a k -means clustering algorithm coupled with the optimal mass transport theory. This approach following the regularized partial correlation network analysis was tested on CT scans from 77 head and neck squamous cell carcinoma (HNSCC) patients in the Cancer Imaging Archive (TCIA) and validated using an independent dataset. Results: A set of common radiomic features was found in relatively large network components between the resultant two partial correlation networks resulting from a cohort of lung cancer patients. The reliability and reproducibility of those radiomic features were further validated on phantom data using the Wasserstein distance. Further analysis using the network-based Wasserstein k -means algorithm on the TCIA HNSCC data showed that the resulting clusters separate tumor subsites as well as HPV status, and this was validated on an independent dataset. Conclusion: We showed that a network-based analysis enables identifying reproducible radiomic features and use of the selected set of features can enhance clustering results.

Neurologic and oncologic features of Erdheim-Chester disease: a 30-patient series

BACKGROUND

Erdheim-Chester disease (ECD) is a rare histiocytic neoplasm characterized by recurrent alterations in the MAPK (mitogen-activating protein kinase) pathway. The existing literature about the neuro-oncological spectrum of ECD is limited.

METHODS

We present retrospective clinical, radiographic, pathologic, molecular, and treatment data from 30 patients with ECD neurohistiocytic involvement treated at a tertiary center.

RESULTS

Median age was 52 years (range, 7-77), and 20 (67%) patients were male. Presenting symptoms included ataxia in 19 patients (63%), dysarthria in 14 (47%), diabetes insipidus in 12 (40%), cognitive impairment in 10 (33%), and bulbar affect in 9 (30%). Neurosurgical biopsy specimens in 8 patients demonstrated varied morphologic findings often uncharacteristic of typical ECD lesions. Molecular analysis revealed mutations in BRAF (18 patients), MAP2K1 (5), RAS isoforms (2), and 2 fusions involving BRAF and ALK. Conventional therapies (corticosteroids, immunosuppressants, interferon-alpha [IFN-α], cytotoxic chemotherapy) led to partial radiographic response in 8/40 patients (20%) by MRI with no complete responses, partial metabolic response in 4/16 (25%), and complete metabolic response in 1/16 (6%) by 18F-fluorodeoxyglucose (FDG)-PET scan. In comparison, targeted (kinase inhibitor) therapies yielded partial radiographic response in 10/27 (37%) and complete radiographic response in 14/27 (52%) by MRI, and partial metabolic response in 6/25 (24%) and complete metabolic response in 17/25 (68%) by FDG-PET scan.

CONCLUSIONS

These data highlight underrecognized symptomatology, heterogeneous neuropathology, and robust responses to targeted therapies across the mutational spectrum in ECD patients with neurological involvement, particularly when conventional therapies have failed.

18F-FDG PET/CT versus anatomic imaging for evaluating disease extent and clinical trial eligibility in Erdheim-Chester disease: results from 50 patients in a registry study

OBJECTIVES

The aim of this study was to [1] characterize distribution of Erdheim-Chester Disease (ECD) by 18F-FDG PET/CT and [2] determine the utility of metabolic (18F-FDG PET/CT) imaging versus anatomic imaging (CT or MRI) in evaluating ECD patients for clinical trial eligibility.

METHODS

18F-FDG PET/CT and corresponding CT or MRI studies for ECD patients enrolled in a prospective registry study were reviewed. Sites of disease were classified as [1] detectable by 18F-FDG PET only, CT/MRI only, or both and as [2] measurable by modified PERCIST (mPERCIST) only, RECIST only, or both. Descriptive analysis was performed and paired t test for between-group comparisons.

RESULTS

Fifty patients were included (mean age 51.5 years; range 18-70 years). Three hundred thirty-three disease sites were detected among all imaging modalities, 188 (56%) by both 18F-FDG PET and CT/MRI, 67 (20%) by 18F-FDG PET only, 75 (23%) by MRI brain only, and 3 (1%) by CT only. Of 178 disease sites measurable by mPERCIST or RECIST, 40 (22%) were measurable by both criteria, 136 (76%) by mPERCIST only, and 2 (1%) by RECIST only. On the patient level, 17 (34%) had mPERCIST and RECIST measurable disease, 30 (60%) had mPERCIST measurable disease only, and 0 had RECIST measurable disease only (p < 0.0001).

CONCLUSION

Compared with anatomic imaging, 18F-FDG PET/CT augments evaluation of disease extent in ECD and increases identification of disease sites measurable by formal response criteria and therefore eligibility for clinical trials. Complementary organ-specific anatomic imaging offers the capacity to characterize sites of disease in greater anatomic detail.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03329274.

Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure

The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (D, D*, f, and K) models. The metrics Ktrans, ve, and τi were estimated using the FXR model. Cumulative incidence (CI) analysis and Fine-Gray (FG) modeling were performed considering death as a competing risk. Mean ve values were significantly different between patients with and without LRF (p = 0.03). Mean f values showed a trend towards the difference between the groups (p = 0.08). Histograms exhibited inter primary tumor heterogeneity. The CI curves showed significant differences for the dichotomized cutoff value of ADC ≤ 0.68 × 10-3 (mm2/s), D ≤ 0.74 × 10-3 (mm2/s), and f ≤ 0.18 (p < 0.05). τi ≤ 0.89 (s) cutoff value showed borderline significance (p = 0.098). FG's modeling showed a significant difference for the K cutoff value of ≤0.86 (p = 0.034). Results suggest that the role of pre-TX NGIVIM DW- and FXR DCE-MRI-derived metrics for predicting LRF in NPC than alone.

Glioma-Induced Disruption of Resting-State Functional Connectivity and Amplitude of Low-Frequency Fluctuations in the Salience Network

BACKGROUND AND PURPOSE

Cognitive challenges are prevalent in survivors of glioma, but their neurobiology is incompletely understood. The purpose of this study was to investigate the effect of glioma presence and tumor characteristics on resting-state functional connectivity and amplitude of low-frequency fluctuations of the salience network, a key neural network associated with cognition.

MATERIALS AND METHODS

Sixty-nine patients with glioma (mean age, 48.74 [SD, 14.32] years) who underwent resting-state fMRI were compared with 31 healthy controls (mean age, 49.68 [SD, 15.54] years). We identified 4 salience network ROIs: left/right dorsal anterior cingulate cortex and left/right anterior insula. Average salience network resting-state functional connectivity and amplitude of low-frequency fluctuations within the 4 salience network ROIs were computed.

RESULTS

Patients with gliomas showed decreased overall salience network resting-state functional connectivity (P = .001) and increased amplitude of low-frequency fluctuations in all salience network ROIs (P < .01) except in the left dorsal anterior cingulate cortex. Compared with controls, patients with left-sided gliomas showed increased amplitude of low-frequency fluctuations in the right dorsal anterior cingulate cortex (P = .002) and right anterior insula (P < .001), and patients with right-sided gliomas showed increased amplitude of low-frequency fluctuations in the left anterior insula (P = .002). Anterior tumors were associated with decreased salience network resting-state functional connectivity (P < .001) and increased amplitude of low-frequency fluctuations in the right anterior insula, left anterior insula, and right dorsal anterior cingulate cortex. Patients with high-grade gliomas had decreased salience network resting-state functional connectivity compared with healthy controls (P < .05). The right anterior insula showed increased amplitude of low-frequency fluctuations in patients with grade II and IV gliomas compared with controls (P < .01).

CONCLUSIONS

By demonstrating decreased resting-state functional connectivity and an increased amplitude of low-frequency fluctuations related to the salience network in patients with glioma, this study adds to our understanding of the neurobiology underpinning observable cognitive deficits in these patients. In addition to more conventional functional connectivity, amplitude of low-frequency fluctuations is a promising functional-imaging biomarker of tumor-induced vascular and neural pathology.

Precision Radiotherapy: Reduction in Radiation for Oropharyngeal Cancer in the 30 ROC Trial

BACKGROUND

Patients with human papillomavirus-related oropharyngeal cancers have excellent outcomes but experience clinically significant toxicities when treated with standard chemoradiotherapy (70 Gy). We hypothesized that functional imaging could identify patients who could be safely deescalated to 30 Gy of radiotherapy.

METHODS

In 19 patients, pre- and intratreatment dynamic fluorine-18-labeled fluoromisonidazole positron emission tomography (PET) was used to assess tumor hypoxia. Patients without hypoxia at baseline or intratreatment received 30 Gy; patients with persistent hypoxia received 70 Gy. Neck dissection was performed at 4 months in deescalated patients to assess pathologic response. Magnetic resonance imaging (weekly), circulating plasma cell-free DNA, RNA-sequencing, and whole-genome sequencing (WGS) were performed to identify potential molecular determinants of response. Samples from an independent prospective study were obtained to reproduce molecular findings. All statistical tests were 2-sided.

RESULTS

Fifteen of 19 patients had no hypoxia on baseline PET or resolution on intratreatment PET and were deescalated to 30 Gy. Of these 15 patients, 11 had a pathologic complete response. Two-year locoregional control and overall survival were 94.4% (95% confidence interval = 84.4% to 100%) and 94.7% (95% confidence interval = 85.2% to 100%), respectively. No acute grade 3 radiation-related toxicities were observed. Microenvironmental features on serial imaging correlated better with pathologic response than tumor burden metrics or circulating plasma cell-free DNA. A WGS-based DNA repair defect was associated with response (P = .02) and was reproduced in an independent cohort (P = .03).

CONCLUSIONS

Deescalation of radiotherapy to 30 Gy on the basis of intratreatment hypoxia imaging was feasible, safe, and associated with minimal toxicity. A DNA repair defect identified by WGS was predictive of response. Intratherapy personalization of chemoradiotherapy may facilitate marked deescalation of radiotherapy.

Diffusion and Perfusion MRI Predicts Response Preceding and Shortly After Radiosurgery to Brain Metastases: A Pilot Study

BACKGROUND AND PURPOSE

To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS).

METHODS

In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria.

RESULTS

We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, v_e , and volume transfer coefficient, K^trans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041).

CONCLUSIONS

Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.

Head-to-Head Evaluation of 18F-FES and 18F-FDG PET/CT in Metastatic Invasive Lobular Breast Cancer

Invasive lobular carcinoma (ILC) demonstrates lower conspicuity on ¹⁸F-FDG PET than the more common invasive ductal carcinoma. Other molecular imaging methods may be needed for evaluation of this malignancy. As ILC is nearly always (95%) estrogen receptor (ER)-positive, ER-targeting PET tracers such as 16α-¹⁸F-fluoroestradiol (¹⁸F-FES) may have value. We reviewed prospective trials at Memorial Sloan Kettering Cancer Center using ¹⁸F-FES PET/CT to evaluate metastatic ILC patients with synchronous ¹⁸F-FDG and ¹⁸F-FES PET/CT imaging, which allowed a head-to-head comparison of these 2 PET tracers. Methods: Six prospective clinical trials using ¹⁸F-FES PET/CT in patients with metastatic breast cancer were performed at Memorial Sloan Kettering Cancer Center from 2008 to 2019. These trials included 92 patients, of whom 14 (15%) were of ILC histology. Seven of 14 patients with ILC had ¹⁸F-FDG PET/CT performed within 5 wk of the research ¹⁸F-FES PET/CT and no intervening change in management. For these 7 patients, the ¹⁸F-FES and ¹⁸F-FDG PET/CT studies were analyzed to determine the total number of tracer-avid lesions, organ systems of involvement, and SUV_max of each organ system for both tracers. Results: In the 7 comparable pairs of scans, there were a total of 254 ¹⁸F-FES-avid lesions (SUV_max, 2.6-17.9) and 111 ¹⁸F-FDG-avid lesions (SUV_max, 3.3-9.9) suggestive of malignancy. For 5 of 7 (71%) ILC patients, ¹⁸F-FES PET/CT detected more metastatic lesions than ¹⁸F-FDG PET/CT. In the same 5 of 7 patients, the SUV_max of ¹⁸F-FES-avid lesions was greater than the SUV_max of ¹⁸F-FDG-avid lesions. One patient had ¹⁸F-FES-avid metastases with no corresponding ¹⁸F-FDG-avid metastases. There were no patients with ¹⁸F-FDG-avid distant metastases without ¹⁸F-FES-avid distant metastases, although in one patient liver metastases were evident on ¹⁸F-FDG but not on ¹⁸F-FES PET. Conclusion: ¹⁸F-FES PET/CT compared favorably with ¹⁸F-FDG PET/CT for detection of metastases in patients with metastatic ILC. Larger prospective trials of ¹⁸F-FES PET/CT in ILC should be considered to evaluate ER-targeted imaging for clinical value in patients with this histology of breast cancer.

Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma

PURPOSE

To identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA).

METHODS

77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes. Genomic data was extracted from the matched patients in the TCGA database. We explored relationships between radiomic features and molecular profiles of tumors, including the tumor immune microenvironment. A machine learning method was used to build a model predictive of CD8 + T-cells. An independent cohort of 83 HNSCC patients was used to validate the radiomic clusters.

RESULTS

We initially extracted 104 two-dimensional radiomic features, and after feature stability tests and removal of volume dependent features, reduced this to 67 features for subsequent analysis. Consensus clustering based on these features resulted in two distinct clusters. The radiomic clusters differed by primary tumor subsite (p = 0.0096), HPV status (p = 0.0127), methylation-based clustering results (p = 0.0025), and tumor immune microenvironment. A random forest model using radiomic features predicted CD8 + T-cells independent of HPV status with R² = 0.30 (p < 0.0001) on cross validation. Consensus clustering on the validation cohort resulted in two distinct clusters that differ in tumor subsite (p = 1.3 × 10^-7) and HPV status (p = 4.0 × 10^-7).

CONCLUSION

Radiomic analysis can identify biologic features of tumors such as HPV status and T-cell infiltration and may be able to provide other information in the near future to help with patient stratification.

Diffusion-Weighted Echo Planar Imaging using MUltiplexed Sensitivity Encoding and Reverse Polarity Gradient in Head and Neck Cancer: An Initial Study

We aimed to compare the geometric distortion (GD) correction performance and apparent diffusion coefficient (ADC) measurements of single-shot diffusion-weighted echo-planar imaging (SS-DWEPI), multiplexed sensitivity encoding (MUSE)-DWEPI, and MUSE-DWEPI with reverse-polarity gradient (RPG) in phantoms and patients. We performed phantom studies at 3T magnetic resonance imaging (MRI) using the American College of Radiology phantom and Quantitative Imaging Biomarker Alliance DW-MRI ice-water phantom to assess GD and effect of distortion in the measurement of ADC, respectively. Institutional review board approved the prospective clinical component of this study. DW-MRI data were obtained from 11 patients with head and neck cancer using these three DW-MRI methods. Wilcoxon signed-rank (WSR) and Kruskal–Wallis (KW) tests were used to compare ADC values, and qualitative rating by radiologist between three DW-MRI methods. In the ACR phantom, GD of 0.17% was observed for the b = 0 s/mm² image of the MUSE-DWEPI with RPG method compared with that of 1.53% and 2.1% of MUSE-DWEPI and SS-DWEPI, respectively; The corresponding methods root-mean-square errors were 0.58, 3.37, and 5.07 mm. WSR and KW tests showed no significant difference in the ADC measurement between these three DW-MRI methods for both healthy masseter muscles and neoplasms (P > .05). We observed improvement in spatial accuracy for MUSE-DWEPI with RPG in the head and neck region with a higher correlation (R² = 0.791) compared with that for SS-DWEPI (R² = 0.707) and MUSE-DWEPI (R² = 0.745). MUSE-DWEPI with RPG significantly reduces the distortion compared with MUSE-DWEPI or conventional SS-DWEPI techniques, and the ADC values were similar.

Identification of HER2-Positive Metastases in Patients with HER2-Negative Primary Breast Cancer by Using HER2-targeted 89Zr-Pertuzumab PET/CT

Background Human epidermal growth factor receptor 2 (HER2)-targeted therapies are successful in patients with HER2-positive malignancies; however, spatial and temporal heterogeneity of HER2 expression may prevent identification of optimal patients for these therapies. Purpose To determine whether imaging with the HER2-targeted PET tracer zirconium 89 (⁸⁹Zr)-pertuzumab can depict HER2-positive metastases in women with HER2-negative primary breast cancer. Materials and Methods From January to June 2019, women with biopsy-proven HER2-negative primary breast cancer and biopsy-proven metastatic disease were enrolled in a prospective clinical trial (ClinicalTrials.gov NCT02286843) and underwent ⁸⁹Zr-pertuzumab PET/CT for noninvasive whole-biopsy evaluation of potential HER2-positive metastases. ⁸⁹Zr-pertuzumab-avid foci that were suspicious for HER2-positive metastases were tissue sampled and examined by pathologic analysis to document HER2 status. Results Twenty-four women (mean age, 55 years ± 11 [standard deviation]) with HER2-negative primary breast cancer were enrolled. Six women demonstrated foci at ⁸⁹Zr-pertuzumab PET/CT that were suspicious for HER2-positive disease. Of these six women, three had biopsy-proven HER2-positive metastases, two had pathologic findings that demonstrated HER2-negative disease, and one had a fine-needle aspirate with inconclusive results. Conclusion Human epidermal growth factor receptor 2 (HER2)-targeted imaging with zirconium 89-pertuzumab PET/CT was successful in detecting HER2-positive metastases in women with HER2-negative primary breast cancer. This demonstrates the ability of targeted imaging to identify patients for targeted therapies that might not otherwise be considered. © RSNA, 2020 Online supplemental material is available for this article. See the editorial by Mankoff and Pantel in this issue.

Computational Modeling of Interstitial Fluid Pressure and Velocity in Non-small Cell Lung Cancer Brain Metastases Treated With Stereotactic Radiosurgery

Background: Early imaging-based treatment response assessment of brain metastases following stereotactic radiosurgery (SRS) remains challenging. The aim of this study is to determine whether early (within 12 weeks) intratumoral changes in interstitial fluid pressure (IFP) and velocity (IFV) estimated from computational fluid modeling (CFM) using dynamic contrast-enhanced (DCE) MRI can predict long-term outcomes of lung cancer brain metastases (LCBMs) treated with SRS.

Methods: Pre- and post-treatment T₁-weighted DCE-MRI data were obtained in 41 patients treated with SRS for intact LCBMs. The imaging response was assessed using RANO-BM criteria. For each lesion, extravasation of contrast agent measured from Extended Tofts pharmacokinetic Model (volume transfer constant, K^trans) was incorporated into a computational fluid model to estimate tumor IFP and IFV. Estimates of mean IFP and IFV and heterogeneity (skewness and kurtosis) were calculated for each lesion from pre- and post-SRS imaging. The Wilcoxon rank-sum test was utilized to assess for significant differences in IFP, IFV, and IFP/IFV change (Δ) between response groups.

Results: Fifty-three lesions from 41 patients were included. Median follow-up time after SRS was 11 months. The objective response (OR) rate (partial or complete response) was 79%, with 21% demonstrating stable disease (SD) or progressive disease (PD). There were significant response group differences for multiple posttreatment and Δ CFM parameters: post-SRS IFP skewness (mean −0.405 vs. −0.691, p = 0.022), IFP kurtosis (mean 2.88 vs. 3.51, p = 0.024), and IFV mean (5.75e-09 vs. 4.19e-09 m/s, p = 0.027); and Δ IFP kurtosis (mean −2.26 vs. −0.0156, p = 0.017) and IFV mean (1.91e-09 vs. 2.38e-10 m/s, p = 0.013). Posttreatment and Δ thresholds predicted non-OR with high sensitivity (sens): post-SRS IFP skewness (−0.432, sens 84%), kurtosis (2.89, sens 84%), and IFV mean (4.93e-09 m/s, sens 79%); and Δ IFP kurtosis (−0.469, sens 74%) and IFV mean (9.90e-10 m/s, sens 74%).

Conclusions: Objective response was associated with lower post-treatment tumor heterogeneity, as represented by reductions in IFP skewness and kurtosis. These results suggest that early post-treatment assessment of IFP and IFV can be used to predict long-term response of lung cancer brain metastases to SRS, allowing a timelier treatment modification.

Temporal Lobe Necrosis in Head and Neck Cancer Patients after Proton Therapy to the Skull Base

PURPOSE

To demonstrate temporal lobe necrosis (TLN) rate and clinical/dose-volume factors associated with TLN in radiation-naïve patients with head and neck cancer treated with proton therapy where the field of radiation involved the skull base.

MATERIALS AND METHODS

Medical records and dosimetric data for radiation-naïve patients with head and neck cancer receiving proton therapy to the skull base were retrospectively reviewed. Patients with <3 months of follow-up, receiving <45 GyRBE or nonconventional fractionation, and/or no follow-up magnetic resonance imaging (MRI) were excluded. TLN was determined using MRI and graded using Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Clinical (gender, age, comorbidities, concurrent chemotherapy, smoking, radiation techniques) and dose-volume parameters were analyzed for TLN correlation. The receiver operating characteristic curve and area under the curve (AUC) were performed to determine the cutoff points of significant dose-volume parameters.

RESULTS

Between 2013 and 2019, 234 patients were included. The median follow-up time was 22.5 months (range = 3.2-69.3). Overall TLN rates of any grade, ≥ grade 2, and ≥ grade 3 were 5.6% (N = 13), 2.1%, and 0.9%, respectively. The estimated 2-year TLN rate was 4.6%, and the 2-year rate of any brain necrosis was 6.8%. The median time to TLN was 20.9 months from proton completion. Absolute volume receiving 40, 50, 60, and 70 GyRBE (absolute volume [aV]); mean and maximum dose received by the temporal lobe; and dose to the 0.5, 1, and 2 cm3 volume receiving the maximum dose (D0.5cm3, D1cm3, and D2cm3, respectively) of the temporal lobe were associated with greater TLN risk while clinical parameters showed no correlation. Among volume parameters, aV50 gave maximum AUC (0.921), and D2cm3 gave the highest AUC (0.935) among dose parameters. The 11-cm3 cutoff value for aV50 and 62 GyRBE for D2cm3 showed maximum specificity and sensitivity.

CONCLUSION

The estimated 2-year TLN rate was 4.6% with a low rate of toxicities ≥grade 3; aV50 ≤11 cm3, D2cm3 ≤62 GyRBE and other cutoff values are suggested as constraints in proton therapy planning to minimize the risk of any grade TLN. Patients whose temporal lobe(s) unavoidably receive higher doses than these thresholds should be carefully followed with MRI after proton therapy.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test–retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland–Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (D_app × 10⁻³ mm²/s) and kurtosis (K_app, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, D_app and K_app mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.

Quantitative Non-Gaussian Intravoxel Incoherent Motion Diffusion-Weighted Imaging Metrics and Surgical Pathology for Stratifying Tumor Aggressiveness in Papillary Thyroid Carcinomas

We assessed a priori aggressive features using quantitative diffusion-weighted imaging metrics to preclude an active surveillance management approach in patients with papillary thyroid cancer (PTC) with tumor size 1-2 cm. This prospective study enrolled 24 patients with PTC who underwent pretreatment multi-b-value diffusion-weighted imaging on a GE 3 T magnetic resonance imaging scanner. The apparent diffusion coefficient (ADC) metric was calculated from monoexponential model, and the perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), and diffusion kurtosis coefficient (K) metrics were estimated using the non-Gaussian intravoxel incoherent motion model. Neck ultrasonography examination data were used to calculate tumor size. The receiver operating characteristic curve assessed the discriminative specificity, sensitivity, and accuracy between PTCs with and without features of tumor aggressiveness. Multivariate logistic regression analysis was performed on metrics using a leave-1-out cross-validation method. Tumor aggressiveness was defined by surgical histopathology. Tumors with aggressive features had significantly lower ADC and D values than tumors without tumor-aggressive features (P < .05). The absolute relative change was 46% in K metric value between the 2 tumor types. In total, 14 patients were in the critical size range (1-2 cm) measured by ultrasonography, and the ADC and D were significantly different and able to differentiate between the 2 tumor types (P < .05). ADC and D can distinguish tumors with aggressive histological features to preclude an active surveillance management approach in patients with PTC with tumors measuring 1-2 cm.

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers

The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

MR Perfusion and MR Spectroscopy of Brain Neoplasms

Advances in imaging techniques, such as MR perfusion and spectroscopy, are increasingly indispensable in the management and treatment plans of brain neoplasms: from diagnosing, molecular/genetic typing and grading neoplasms, augmenting biopsy results and improving accuracy, to ultimately directing and monitoring treatment and response. New developments in treatment methods have resulted in new diagnostic challenges for conventional MR imaging, such as pseudoprogression, where MR perfusion has the widest current application. MR spectroscopy is showing increasing promise in noninvasively determining genetic subtypes and, potentially, susceptibility to molecular targeted therapies.

Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis

PURPOSE

Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows.

METHOD

Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( Φ F ) and backward flux ( Φ B ). OMT-derived Φ F was compared with the volume transfer constant for CA, K trans , derived from the Extended Tofts Model (ETM).

RESULTS

The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min - 1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min - 1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( Φ F ) is comparable with the ETM-derived K trans .

CONCLUSIONS

This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.

Leptomeningeal metastases in glioma: The Memorial Sloan Kettering Cancer Center experience

OBJECTIVE

To perform a retrospective analysis examining the incidence and prognosis of glioma patients with leptomeningeal disease (LMD) at Memorial Sloan Kettering Cancer Center over a 15-year period and correlate these findings with clinicopathologic characteristics.

METHODS

We conducted a retrospective review of glioma patients with LMD at Memorial Sloan Kettering Cancer Center diagnosed from 2001 to 2016. Patients were identified through a keyword search of their electronic medical record and by ICD-9 codes.

RESULTS

One hundred three patients were identified with disseminated LMD and 85 patients with subependymal spread of disease, 4.7% of all patients with glioma. These cohorts were analyzed separately for time to development of disseminated LMD/subependymal LMD, median overall survival, and survival from LMD diagnosis. Patients were pooled for subsequent analyses (n = 188) because of comparable clinical behavior. LMD was present at glioma diagnosis in 10% of patients. In the remaining 90% of patients diagnosed at recurrence, time to LMD diagnosis, survival after LMD diagnosis, and overall survival varied by original histology. Patients with oligodendroglioma had a median survival of 10.8 (range 1.8-67.7) months, astrocytoma 6.5 (0.1-28.5) months, and glioblastoma 3.8 (0.1-32.6) months after LMD diagnosis. In addition, we found that treatment of LMD was associated with superior performance status and increased survival.

CONCLUSION

Patients with LMD diagnosed at relapse may not have decreased overall survival as compared to historical controls with parenchymal relapse and may benefit from treatment.

Clinicopathologic and genomic characterization of parenchymal brain metastases (BM) in prostate cancer (PCa)

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Background: BM are rarely seen with PCa, but the incidence may be increasing with contemporary therapies. We sought to evaluate the clinical phenotypes associated with BM and their outcomes. Methods: The MSKCC institutional clinical database from 01/2001 through 06/2018 was reviewed to identify pts with PCa and (1) secondary malignant neoplasm of the brain / spinal cord per ICD-9 (198.3), (2) radiation treatment plan targeting the brain, (3) craniotomy, or (3) notes containing the terms “brain metastasis”. Medical charts were reviewed to confirm diagnosis (dx) and extract data. The logrank statistic and the Cox proportional hazards model were used to determine correlations with overall survival (OS). Results: Of 575 pts who met search criteria, 43 had BM and prostate adenocarcinoma. At PCa dx, median age was 60 years (range: 44 –77 years) and 51% had metastasis (mets). Median time from PCa dx to BM was 4.2 years. At the time of BM dx, 36 had metastatic castration-resistant PCa (mCRPC), 55% had liver mets and 66% had lung mets. 45% had ≥3 lines of therapy for mCRPC prior to BM dx (abiraterone/enzalutamide: 47%, chemotherapy: 91%). The median OS from BM dx was 5.8 months (Table). 4/10 pts with sequencing data had germline mutations ( ATM, BRCA1, BRIP1, MUTYH). To date, 4 BM were sequenced, and 3 showed PTEN loss. Conclusions: BM associated with a poor prognosis and occurred after prolonged treatment. Presence of liver/lung mets, 3 or more BM, and surgical resection (SR) prognosticate for OS in univariate analysis; SR in multivariate analysis. Further analysis is needed to determine if germline mutations and/or PTEN loss associate with BM. [Table: see text]

Early posttreatment assessment of MRI perfusion biomarkers can predict long-term response of lung cancer brain metastases to stereotactic radiosurgery

Background

Imaging criteria to evaluate the response of brain metastases to stereotactic radiosurgery (SRS) in the early posttreatment period remains a crucial unmet need. The aim of this study is to correlate early (within 12 wk) posttreatment perfusion MRI changes with long-term outcomes after treatment of lung cancer brain metastases with SRS.

Methods

Pre- and posttreatment perfusion MRI scans were obtained in patients treated with SRS for intact non-small cell lung cancer brain metastases. Time-dependent leakage (Ktrans), blood plasma volume (Vp), and extracellular extravascular volume (Ve) were calculated for each lesion. Patients were followed longitudinally with serial MRI until death, progression, or intervention (whole brain radiation or surgery).

Results

We included 53 lesions treated with SRS from 41 total patients. Median follow-up after treatment was 11 months. Actuarial local control at one year was 85%. Univariate analysis demonstrated a significant difference (P = 0.032) in posttreatment Ktrans SD between patients with progressive disease (mean = 0.0317) and without progressive disease (mean = 0.0219). A posttreatment Ktrans SD cutoff value of 0.017 was highly sensitive (89%) for predicting progressive disease and no progressive disease. Early posttreatment volume change was not associated with outcome (P = 0.941).

Conclusion

Posttreatment Ktrans SD may be used as an early posttreatment imaging biomarker to help predict long-term response of lung cancer brain metastases to SRS. This can help identify patients who will ultimately fail SRS and allow for timelier adjustment in treatment approach. These data should be prospectively validated in larger patient cohorts and other histologies.

Resting-State Functional Connectivity of the Middle Frontal Gyrus Can Predict Language Lateralization in Patients with Brain Tumors

BACKGROUND AND PURPOSE

A recent study using task-based fMRI demonstrated that the middle frontal gyrus is comparable with Broca's area in its ability to determine language laterality using a measure of verbal fluency. This study investigated whether the middle frontal gyrus can be used as an indicator for language-hemispheric dominance in patients with brain tumors using task-free resting-state fMRI. We hypothesized that no significant difference in language lateralization would occur between the middle frontal gyrus and Broca area and that the middle frontal gyrus can serve as a simple and reliable means of measuring language laterality.

MATERIALS AND METHODS

Using resting-state fMRI, we compared the middle frontal gyrus with the Broca area in 51 patients with glial neoplasms for voxel activation, the language laterality index, and the effect of tumor grade on the laterality index. The laterality index derived by resting-state fMRI and task-based fMRI was compared in a subset of 40 patients.

RESULTS

Voxel activations in the left middle frontal gyrus and left Broca area were positively correlated (r = 0.47, P < .001). Positive correlations were seen between the laterality index of the Broca area and middle frontal gyrus regions (r = 0.56, P < .0005). Twenty-seven of 40 patients (67.5%) showed concordance of the laterality index based on the Broca area using resting-state fMRI and the laterality index based on a language task. Thirty of 40 patients (75%) showed concordance of the laterality index based on the middle frontal gyrus using resting-state fMRI and the laterality index based on a language task.

CONCLUSIONS

The middle frontal gyrus is comparable with the Broca area in its ability to determine hemispheric dominance for language using resting-state fMRI. Our results suggest the addition of resting-state fMRI of the middle frontal gyrus to the list of noninvasive modalities that could be used in patients with gliomas to evaluate hemispheric dominance of language before tumor resection. In patients who cannot participate in traditional task-based fMRI, resting-state fMRI offers a task-free alternate to presurgically map the eloquent cortex.

Frequency of Brain Metastases and Multikinase Inhibitor Outcomes in Patients With RET-Rearranged Lung Cancers

INTRODUCTION

In ret proto-oncogene (RET)-rearranged lung cancers, data on the frequency of brain metastases and, in particular, the outcomes of multikinase inhibitor therapy in patients with intracranial disease are not well characterized.

METHODS

A global, multi-institutional registry (cohort A, n = 114) and a bi-institutional data set (cohort B, n = 71) of RET-rearranged lung cancer patients were analyzed. Patients were eligible if they had stage IV lung cancers harboring a RET rearrangement by local testing. The incidence of brain metastases and outcomes with multikinase inhibitor therapy were determined.

RESULTS

The frequency of brain metastases at the time of diagnosis of stage IV disease was 25% (95% confidence interval [CI]: 18%-32%) in all patients from both cohorts. The lifetime prevalence of brain metastasis in stage IV disease was 46% (95% CI: 34%-58%) in patients for whom longitudinal data was available. The cumulative incidence of brain metastases was significantly different (p = 0.0039) between RET-, ROS1-, and ALK receptor tyrosine kinase (ALK)-rearranged lung cancers, with RET intermediate between the other two groups. Although intracranial response data was not available in cohort A, the median progression-free survival of multikinase inhibitor therapy (cabozantinib, vandetanib, or sunitinib) in patients with brain metastases was 2.1 months (95% CI: 1.3-2.9 months, n = 10). In cohort B, an intracranial response was observed in 2 of 11 patients (18%) treated with cabozantinib, vandetanib (± everolimus), ponatinib, or alectinib; the median overall progression-free survival (intracranial and extracranial) was 3.9 months (95% CI: 2.0-4.9 months).

CONCLUSIONS

Brain metastases occur frequently in RET-rearranged lung cancers, and outcomes with multikinase inhibitor therapy in general are suboptimal. Novel RET-directed targeted therapy strategies are needed.

Ibrutinib Unmasks Critical Role of Bruton Tyrosine Kinase in Primary CNS Lymphoma

Bruton tyrosine kinase (BTK) links the B-cell antigen receptor (BCR) and Toll-like receptors with NF-κB. The role of BTK in primary central nervous system (CNS) lymphoma (PCNSL) is unknown. We performed a phase I clinical trial with ibrutinib, the first-in-class BTK inhibitor, for patients with relapsed or refractory CNS lymphoma. Clinical responses to ibrutinib occurred in 10 of 13 (77%) patients with PCNSL, including five complete responses. The only PCNSL with complete ibrutinib resistance harbored a mutation within the coiled-coil domain of CARD11, a known ibrutinib resistance mechanism. Incomplete tumor responses were associated with mutations in the B-cell antigen receptor-associated protein CD79B. CD79B-mutant PCNSLs showed enrichment of mammalian target of rapamycin (mTOR)-related gene sets and increased staining with PI3K/mTOR activation markers. Inhibition of the PI3K isoforms p110α/p110δ or mTOR synergized with ibrutinib to induce cell death in CD79B-mutant PCNSL cells.Significance: Ibrutinib has substantial activity in patients with relapsed or refractory B-cell lymphoma of the CNS. Response rates in PCNSL were considerably higher than reported for diffuse large B-cell lymphoma outside the CNS, suggesting a divergent molecular pathogenesis. Combined inhibition of BTK and PI3K/mTOR may augment the ibrutinib response in CD79B-mutant human PCNSLs. Cancer Discov; 7(9); 1018-29. ©2017 AACR.See related commentary by Lakshmanan and Byrd, p. 940This article is highlighted in the In This Issue feature, p. 920.

Baseline frequency of brain metastases and outcomes with multikinase inhibitor therapy in patients with RET-rearranged lung cancers

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Background: In phase 2 trials, multikinase inhibitors with activity against RET are active in a subset of patients (pts) with RET-rearranged lung cancers (response rate of 28%, phase 2 study of cabozantinib; Drilon et al Lancet Oncol 2016). Data on the incidence of brain metastases and outcomes with multikinase inhibitor therapy in pts with intracranial disease have not previously been reported. Methods: The frequency of brain metastases at diagnosis of metastatic disease was evaluated in pts accrued to a global registry of RET-rearranged lung cancer pts identified by a multicenter network of thoracic oncologists (Gautschi et al JCO 2017). A proportion of pts were treated with 9 multikinase inhibitors including cabozantinib, vandetanib, lenvatinib, alectinib, and ponatinib. On a prospective phase 2 trial (NCT01639508), patients with asymptomatic brain metastases were eligible. Intracranial response to cabozantinib (RECIST v1.1) was evaluated in an exploratory fashion. Results: 114 registry pts with RET-rearranged lung cancers had metastatic disease at diagnosis. Baseline brain metastases were identified in 27% (95%CI 18-34%, n = 20/75) of pts with available information. No differences (p > 0.05) in age, smoking history, or upstream fusion partner ( KIF5B100% vs 84%, with and without brain metastases, p = 0.53) were noted. In 37 pts treated with multikinase inhibitors with activity against RET, there were no significant differences in median PFS (2.1 vs 2.1 months, p = 0.41) or median OS (3.9 vs 7.0 months, p = 0.10) in pts with (n = 10) and without (n = 27) brain metastases. On a phase 2 trial of cabozantinib, baseline untreated brain metastases were present in 5 pts. Intracranial disease control (stable disease; -34% and -1% in 2 pts with measurable disease) was achieved in 4 of 4 pts with measurable or evaluable intracranial disease with time to treatment discontinuation ranging from 2.4 months to 2.9 years. Conclusions: Brain metastases are present in a substantial proportion of RET-rearranged lung cancer pts. Intracranial disease control can be achieved in select pts by a multikinase inhibitor. Novel RET-directed targeted therapy strategies should address intracranial disease. Clinical trial information: NCT01639508.

Updated results of single-agent ibrutinib in recurrent/refractory primary (PCNSL) and secondary CNS lymphoma (SCNSL)

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Background: PCNSL is an aggressive primary brain tumor with median progression free survival (PFS) after upfront methotrexate-based chemotherapy of 2-3 years. Outcome and treatment options are poor for recurrent/refractory (r/r) disease. Ibrutinib has shown promising clinical response in Mantle cell lymphoma, CLL, Marginal Zone, and Waldenström. This trial investigates Ibrutinib in patients with r/r PCNSL and SCNSL. Methods: Eligible patients had r/r PCNSL or SCNSL, age≥18, ECOG≤2, normal end-organ function, and unrestricted number of CNS directed prior therapies. In patients with SCNSL disease, systemic disease needed to be absent. Results: Twenty-five patients were enrolled (3 at 560 mg; 22 at 840 mg). Median age was 68 (range 21-85); 15 were women. Median ECOG was 1 (0: 2, 1: 15, 2: 8). 64% had PCNSL and 36% SCNSL; 68% had recurrent disease. Seventeen had parenchymal disease, 3 isolated cerebrospinal fluid (CSF) involvement and 5 both. Seven grade 4 adverse events were observed in 7 patients neutropenia (in 3 patients), lymphopenia (2), sepsis (1), and ALT elevation (1). Fourteen patients developed 20 grade 3 toxicities, including lymphopenia in 5 patients, hyperglycemia in 3, ALT elevation in 2, thrombocytopenia in 2, lung infection in 2, AST elevation in 1, neutropenia in 1, urinary tract infection in 1, colitis in 1, febrile neutropenia in 1 and fungal encephalitis in 1. The most common toxicities at any grade were hyperglycemia, thrombocytopenia and anemia of which most were grade 1/2. No grade 5 events have been observed. After a median follow-up of 414 days (range 289-674), 22/25 patients were evaluated for response (3 did not complete at least 15 days of drug treatment). Over all response was 68% (17/22; 77% (17/22) in patient that completed at least 15 days of drug treatment) with 10 CR, 7 PR, 2 SD and 3 PD as best response. The median PFS is 4.6 months (5.4 months in patients that completed at least 15 days of drug treatment; longest: 15.3 months). The median overall survival has not been reached. Conclusions: Patients with CNS lymphoma tolerate Ibrutinib with manageable adverse events. Clinical response was seen in 68% of CNS lymphoma patients. Clinical trial information: NCT02315326.

A personalized approach using hypoxia resolution to guide curative-intent radiation dose-reduction to 30 Gy: A novel de-escalation paradigm for HPV-associated oropharynx cancers (OPC)

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Background: We conducted a pilot study using functional imaging to guide reduction in radiation (RT) to 30Gy with concurrent chemotherapy in patients with HPV+ OPC. Methods: 19 patients were enrolled prospectively from 7/2015-10/2016. Primary tumors were excised and analyzed for DNA repair foci ex-vivo. A pre-RT dynamic 18F-FMISO (fluoromisonidazole) PET was then used to assess tumor hypoxia (defined as > 1.2 tumor to muscle SUV ratio) in cervical lymph nodes. Patients without hypoxia on baseline or repeat scan done 5-10 days after initiation of chemoRT received 30Gy (57% reduction) over 3 weeks to the tumor bed and neck with 2 cycles of concurrent chemotherapy (high-dose cisplatin or carboplatin/5-FU). Patients with persistent hypoxia received the standard dose of 70Gy over 7 weeks with chemo. Neck dissection (ND) was done 4-months post chemoRT. Weekly DWI MRI, ctDNA, whole exome & RNA sequencing were performed. Results: 19 patients (11 tonsil, 5 BoT, 3 unknown primaries) were enrolled. Staging: 11 T1, 5 T2, 3 Tx; 5 N1, 3 N2a, 11 N2b; all M0. On pre-RT 18F-FMISO scans, 13 were positive and 6 were negative for hypoxia. Of the 12 intra-treatment 18F-FMISO scans (1 not done due to intermittent illness, this patient received 70Gy), 3 were positive and these patients received 70Gy chemoRT. 15 patients were de-escalated to 30Gy. To date, analysis showed complete pathologic response in 8 of 9 patients (all 15 expected to have ND by April 2017. The one positive case received only 1 cycle of cisplatin. To date, 18 of 19 patients (95%-6 pending ND) remain disease free. Correlative analysis with sequencing, DNA repair foci, ctDNA, and results from pathologic and intra-treatment imaging response will be presented. Conclusions: This is the first report of apersonalized approach to a major decrease in RT dosing for definitive treatment of HPV+ oropharyngeal carcinoma guided by patient-specific imaging-based treatment response. De-escalation to 30Gy informed by intra-treatment imaging for hypoxia appears feasible, safe and efficacious. A multi-center trial to validate these pilot results is planned. Clinical trial information: NCT00606294.

Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases

Brain metastases originating from different primary sites overlap in appearance and are difficult to differentiate with conventional MRI. Dynamic contrast-enhanced (DCE)-MRI can assess tumor microvasculature and has demonstrated utility in characterizing primary brain tumors. Our aim was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (K^trans ) derived from DCE-MRI in distinguishing between melanoma and nonsmall cell lung cancer (NSCLC) brain metastases. Forty-seven NSCLC and 23 melanoma brain metastases were retrospectively assessed with DCE-MRI. Regions of interest were manually drawn around the metastases to calculate Vp_mean and Kmeantrans. The Mann-Whitney U test and receiver operating characteristic analysis (ROC) were performed to compare perfusion parameters between the two groups. The Vp_mean of melanoma brain metastases (4.35, standard deviation [SD] = 1.31) was significantly higher (P = 0.03) than Vp_mean of NSCLC brain metastases (2.27, SD = 0.96). The Kmeantrans values were higher in melanoma brain metastases, but the difference between the two groups was not significant (P = 0.12). Based on ROC analysis, a cut-off value of 3.02 for Vp_mean (area under curve = 0.659 with SD = 0.074) distinguished between melanoma brain metastases and NSCLC brain metastases (P < 0.01) with 72% specificity. Our data show the DCE-MRI parameter Vp_mean can differentiate between melanoma and NSCLC brain metastases. The ability to noninvasively predict tumor histology of brain metastases in patients with multiple malignancies can have important clinical implications.

Multimodality functional imaging using DW-MRI and 18F-FDG-PET/CT during radiation therapy for human papillomavirus negative head and neck squamous cell carcinoma: Meixoeiro Hospital of Vigo Experience

AIM

To noninvasively investigate tumor cellularity measured using diffusion-weighted magnetic resonance imaging (DW-MRI) and glucose metabolism measured by ¹⁸F-labeled fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) during radiation therapy (RT) for human papillomavirus negative (HPV-) head and neck squamous cell carcinoma (HNSCC).

METHODS

In this prospective study, 6 HPV- HNSCC patients underwent a total of 34 multimodality imaging examinations (DW-MRI at 1.5 T Philips MRI scanner [(n = 24) pre-, during- (2-3 wk), and post-treatment (Tx), and ¹⁸F-FDG PET/CT pre- and post-Tx (n = 10)]. All patients received RT. Monoexponential modeling of the DW-MRI data yielded the imaging metric apparent diffusion coefficient (ADC) and the mean of standardized uptake value (SUV) was measured from ¹⁸F-FDG PET uptake. All patients had a clinical follow-up as the standard of care and survival status was documented at 1 year.

RESULTS

There was a strong negative correlation between the mean of pretreatment ADC (ρ = -0.67, P = 0.01) and the pretreatment ¹⁸F-FDG PET SUV. The percentage (%) change in delta (∆) ADC for primary tumors and neck nodal metastases between pre- and Wk_2-3 Tx were as follows: 75.4% and 61.6%, respectively, for the patient with no evidence of disease, 27.5% and 32.7%, respectively, for those patients who were alive with disease, and 26.9% and 7.31%, respectively, for those who were dead with disease.

CONCLUSION

These results are preliminary in nature and are indicative, and not definitive, trends rendered by the imaging metrics due to the small sample size of HPV- HNSCC patients in a Meixoeiro Hospital of Vigo Experience.

Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma

BACKGROUND AND PURPOSE

Glioblastoma and primary CNS lymphoma dictate different neurosurgical strategies; it is critical to distinguish them preoperatively. However, current imaging modalities do not effectively differentiate them. We aimed to examine the use of DWI and T1-weighted dynamic contrast-enhanced-MR imaging as potential discriminative tools.

MATERIALS AND METHODS

We retrospectively reviewed 18 patients with primary CNS lymphoma and 36 matched patients with glioblastoma with pretreatment DWI and dynamic contrast-enhanced-MR imaging. VOIs were drawn around the tumor on contrast-enhanced T1WI and FLAIR images; these images were transferred onto coregistered ADC maps to obtain the ADC and onto dynamic contrast-enhanced perfusion maps to obtain the plasma volume and permeability transfer constant. Histogram analysis was performed to determine the mean and relative ADC_mean and relative 90th percentile values for plasma volume and the permeability transfer constant. Nonparametric tests were used to assess differences, and receiver operating characteristic analysis was performed for optimal threshold calculations.

RESULTS

The enhancing component of primary CNS lymphoma was found to have significantly lower ADC_mean (1.1 × 10^-3 versus 1.4 × 10^-3; P < .001) and relative ADC_mean (1.5 versus 1.9; P < .001) and relative 90th percentile values for plasma volume (3.7 versus 5.0; P < .05) than the enhancing component of glioblastoma, but not significantly different relative 90th percentile values for the permeability transfer constant (5.4 versus 4.4; P = .83). The nonenhancing portions of glioblastoma and primary CNS lymphoma did not differ in these parameters. On the basis of receiver operating characteristic analysis, mean ADC provided the best threshold (area under the curve = 0.83) to distinguish primary CNS lymphoma from glioblastoma, which was not improved with normalized ADC or the addition of perfusion parameters.

CONCLUSIONS

ADC was superior to dynamic contrast-enhanced-MR imaging perfusion, alone or in combination, in differentiating primary CNS lymphoma from glioblastoma.