• FCR-treated chronic lymphocytic leukemia patients with mutated IGHV gene achieve long-term PFS, with a plateau on the PFS curve.• MRD-negativity posttreatment is highly predictive of longterm PFS, particularly in patients with mutated IGHV gene.Accurate identification of patients likely to achieve long-progression-free survival (PFS) after chemoimmunotherapy is essential given the availability of less toxic alternatives, such as ibrutinib. Fludarabine, cyclophosphamide, and rituximab (FCR) achieved a high response rate, but continued relapses were seen in initial reports. We reviewed the original 300 patient phase 2 FCR study to identify long-term disease-free survivors. Minimal residual disease (MRD) was assessed posttreatment by a polymerase chain reaction-based ligase chain reaction assay (sensitivity 0.01%). At the median follow-up of 12.8 years, PFS was 30.9% (median PFS, 6.4 years). The 12.8-year PFS was 53.9% for patients with mutated immunoglobulin heavy chain variable (IGHV) gene (IGHV-M) and 8.7% for patients with unmutated IGHV (IGHV-UM). 50.7% of patients with IGHV-M achieved MRD-negativity posttreatment; of these, PFS was 79.8% at 12.8 years. A plateau was seen on the PFS curve in patients with IGHV-M, with no relapses beyond 10.4 years in 42 patients (total follow-up 105.4 patient-years). On multivariable analysis, IGHV-UM (hazard ratio, 3.37 [2.18-5.21]; P < .001) and del(17p) by conventional karyotyping (hazard ratio, 7.96 [1.02-61.92]; P 5 .048) were significantly associated with inferior PFS. Fifteen patients with IGHV-M had 4-color MRD flow cytometry (sensitivity 0.01%) performed in peripheral blood, at a median of 12.8 years posttreatment (range, 9.5-14.7). All were MRD-negative. The high rate of very long-term PFS in patients with IGHV-M after FCR argues for the continued use of chemoimmunotherapy in this patient subgroup outside clinical trials; alternative strategies may be preferred in patients with IGHV-UM, to limit long-term toxicity. (Blood. 2016;127(3):303-309)

Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.

Key Points Within MDS/MPN, the WHO 2008 criteria for aCML identify a subgroup of patients with aggressive clinical features distinct from MDS/MPN-U. The MDS/MPN-U category is heterogeneous, and patient risk can be further stratified by a number of clinicopathological parameters.

In this paper, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions.

Three commercial metal artifact reduction methods were evaluated for use in computed tomography (CT) imaging in the presence of clinically realistic metal implants: Philips O-MAR, GE's monochromatic Gemstone Spectral Imaging (GSI) using dual-energy CT, and GSI monochromatic imaging with metal artifact reduction software applied (MARs). Each method was evaluated according to CT number accuracy, metal size accuracy, and streak artifact severity reduction by using several phantoms, including three anthropomorphic phantoms containing metal implants (hip prosthesis, dental fillings, and spinal fixation rods). All three methods showed varying degrees of success for the hip prosthesis and spinal fixation rod cases, while none were particularly beneficial for dental artifacts. Limitations of the methods were also observed. MARs underestimated the size of metal implants and introduced new artifacts in imaging planes beyond the metal implant when applied to dental artifacts, and both the O-MAR and MARs algorithms induced artifacts for spinal fixation rods in a thoracic phantom. Our findings suggest that all three artifact mitigation methods may benefit patients with metal implants, though they should be used with caution in certain scenarios.