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. 940. This article is highlighted in the In This Issue feature, p. 920

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.

The aim of this study was to assess the effectiveness of fluorine-18 fluorodeoxyglucose (FDG) PET-CT and dynamic contrast-enhanced (DCE) MRI in differentiating tumor progression and radiation injury in patients with indeterminate enhancing lesions after radiation therapy (RT) for brain malignancies. Patients with indeterminate enhancing brain lesions on conventional MRI after RT underwent brain DCE-MRI and PET-CT in a prospective trial. Informed consent was obtained. Lesion outcomes were determined by histopathology and/or clinical and imaging follow-up. Metrics obtained included plasma volume (Vp) and volume transfer coefficient (K^trans) from DCE-MRI, and maximum standardized uptake value (SUV_max) from PET-CT; lesion-to-normal brain ratios of all metrics were calculated. The Wilcoxon rank sum test and receiver operating characteristic analysis were performed. The study included 53 patients (29 treated for 29 gliomas and 24 treated for 26 brain metastases). Progression was determined in 38/55 (69%) indeterminate lesions and radiation injury in 17 (31%). Vp_ratio (V_{P lesion}/V_{P normal brain}, P < .001), K^trans_ratio (P = .002), and SUV_ratio (P = .002) correlated significantly with diagnosis of progression versus radiation injury. Progressing lesions exhibited higher values of all 3 metrics compared with radiation injury. Vp_ratio had the highest accuracy in determining progression (area under the curve = 0.87), with 92% sensitivity and 77% specificity using the optimal, retrospectively determined threshold of 2.1. When Vp_ratio was combined with K^trans_ratio (optimal threshold 3.6), accuracy increased to 94%. Vp_ratio was the most effective metric for distinguishing progression from radiation injury. Adding K^trans_ratio to Vp_ratio further improved accuracy. DCE-MRI is an effective imaging technique for evaluating nonspecific enhancing intracranial lesions after RT.

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.