Adaptable cascaded registration for personalized maxilla completion and cleft defect volume estimation.

BACKGROUND: Cone-beam computed tomography (CBCT) images provide high-resolution insights into the underlying craniofacial anomaly in patients with cleft lip and palate (CLP), requiring non-negligible annotation costs to measure the cleft defect for the guidance of the clinical secondary alveolar bone graft procedures. Considering the cumbersome volumetric image acquisition, there is a lack of paired CLP CBCTs and normal CBCTs for learning-based anatomical structure restoration models. Nowadays, the registration-based method relieves the annotation burden, though one-shot registration and the regular mask are limited to handling fine-grained shape variations and harmony between restored bony tissues and the defected maxilla. PURPOSE: This study aimed to design and evaluate a novel method for deformable partial registration of the CLP CBCTs and normal CBCTs, enabling personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. METHODS: We proposed an adaptable deep registration framework for personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. The key ingredient was a cascaded partial registration to exploit the maxillary morphology prior and attribute transfer. Cascaded registration with coarse-to-fine registration fields handled morphological variations of cleft defects and fine-grained maxillary restoration. We designed an adaptable cleft defect mask and volumetric Boolean operators for reliable voxel filling of the defected maxilla. A total of 36 clinically obtained CLP CBCTs were used to train and validate the proposed model, among which 22 CLP CBCTs were used to generate a training dataset with 440 synthetic CBCTs by B-spline deformation-based data augmentation and the remaining for testing. The proposed model was evaluated on maxilla completion and cleft defect volume prediction from clinically obtained unilateral and bilateral CLP CBCTs. RESULTS: Extensive experiments demonstrated the effectiveness of the adaptable cleft defect mask and the cascaded partial registration on maxilla completion and cleft defect volume prediction. The proposed method achieved state-of-the-art performances with the Dice similarity coefficient of 0.90 ± $\pm$ 0.02 on the restored maxilla and 0.84 ± $\pm$ 0.04 on the estimated cleft defect, respectively. The average Hausdorff distance between the estimated cleft defect and the manually annotated ground truth was 0.30 ± $\pm$ 0.08 mm. The relative volume error of the cleft defect was 0.09 ± $0.09\pm$ 0.08. The proposed model allowed for the prediction of cleft defect maps that were in line with the ground truth in the challenging unilateral and bilateral CLP CBCTs. CONCLUSIONS: The results suggest that the proposed adaptable deep registration model enables patient-specific maxilla completion and automatic annotation of cleft defects, relieving tedious voxel-wise annotation and image acquisition burdens.

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography.

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

Dense correspondence of deformable volumetric images via deep spectral embedding and descriptor learning.

Deformable image correspondence plays an essential role in a variety of medical image analysis tasks. Most existing deep learning-based registration and correspondence techniques exploit metric space alignments in the spatial domain and learn a nonlinear voxel-wise mapping function between volumetric images and displacement fields, agnostic to intrinsic structure correspondence. When confronted with high-frequency perturbations of patients' poses and anatomical structural variations, they relied on prior rigid and affine transformations, as well as additional segmentation masks and landmark annotations for reliable registration. This paper presents a data-driven spectral mapping-based correspondence framework to handle the intrinsic correspondence of anatomical structures. At the core of our approach lies a deep convolutional framework that approximates spectral bases and optimizes volumetric descriptors. The multi-path graph convolutional network-based spectral embedding approximation module relieves the computationally expensive eigendecomposition-based embedding of volumetric images. The deep descriptor learning module surpasses the prior hand-crafted descriptors and the descriptor selection. We showcase the efficacy of the core modules, i.e., the spectral embedding approximation and descriptor learning, for volumetric image correspondence and the atlas-based registration on two volumetric image datasets. The proposed method achieves comparable correspondence accuracy with the state-of-the-art deep registration models, resilient to pose and shape perturbations.

Automated assessment of mandibular shape asymmetry in 3-dimensions.

INTRODUCTION: This study aimed to develop an automatic pipeline for analyzing mandibular shape asymmetry in 3-dimensions. METHODS: Forty patients with skeletal Class I pattern and 80 patients with skeletal Class III pattern were used. The mandible was automatically segmented from the cone-beam computed tomography images using a U-net deep learning network. A total of 17,415 uniformly sampled quasi-landmarks were automatically identified on the mandibular surface via a template mapping technique. After alignment with the robust Procrustes superimposition, the pointwise surface-to-surface distance between original and reflected mandibles was visualized in a color-coded map, indicating the location of asymmetry. The degree of overall mandibular asymmetry and the asymmetry of subskeletal units were scored using the root-mean-squared-error between the left and right sides. These asymmetry parameters were compared between the skeletal Class I and skeletal Class III groups. RESULTS: The mandible shape was significantly more asymmetrical in patients with skeletal Class III pattern with positional asymmetry. The condyles were identified as the most asymmetric region in all groups, followed by the coronoid process and the ramus. CONCLUSIONS: This automated approach to quantify mandibular shape asymmetry will facilitate high-throughput image processing for big data analysis. The spatially-dense landmarks allow for evaluating mandibular asymmetry over the entire surface, which overcomes the information loss inherent in conventional linear distance or angular measurements. Precise quantification of the asymmetry can provide important information for individualized diagnosis and treatment planning in orthodontics and orthognathic surgery.

Deep Volumetric Descriptor Learning for Dense Correspondence of Cone-Beam Computed Tomography via Spectral Maps.

The deep neural network has achieved great success in 3D volumetric correspondence. These methods infer the dense displacement or velocity fields directly from the extracted volumetric features without addressing the intrinsic structure correspondence, being prone to shape and pose variations. On the other hand, the spectral maps address the intrinsic structure matching in the low dimensional embedding space, remain less involved in volumetric image correspondence. This paper presents an unsupervised deep volumetric descriptor learning neural network via the low dimensional spectral maps to address the dense volumetric correspondence. The neural network is optimized by a novel criterion on descriptor alignments in the spectral domain regarding the supervoxel graph. Aside from the deep convolved multi-scale features, we explicitly address the supervoxel-wise spatial and cross-channel dependencies to enrich deep descriptors. The dense volumetric correspondence is formulated as the low-dimensional spectral mapping. The proposed approach has been applied to both synthetic and clinically obtained cone-beam computed tomography images to establish dense supervoxel-wise and up-scaled voxel-wise correspondences. Extensive series of experimental results demonstrate the contribution of the proposed approach in volumetric descriptor extraction and consistent correspondence, facilitating attribute transfer for segmentation and landmark location. The proposed approach performs favorably against the state-of-the-art volumetric descriptors and the deep registration models, being resilient to pose or shape variations and independent of the prior transformations.

Progressive Feature Learning for Facade Parsing With Occlusions.

Existing deep models for facade parsing often fail in classifying pixels in heavily occluded regions of facade images due to the difficulty in feature representation of these pixels. In this paper, we solve facade parsing with occlusions by progressive feature learning. To this end, we locate the regions contaminated by occlusions via Bayesian uncertainty evaluation on categorizing each pixel in these regions. Then, guided by the uncertainty, we propose an occlusion-immune facade parsing architecture in which we progressively re-express the features of pixels in each contaminated region from easy to hard. Specifically, the outside pixels, which have reliable context from visible areas, are re-expressed at early stages; the inner pixels are processed at late stages when their surroundings have been decontaminated at the earlier stages. In addition, at each stage, instead of using regular square convolution kernels, we design a context enhancement module (CEM) with directional strip kernels, which can aggregate structural context to re-express facade pixels. Extensive experiments on popular facade datasets demonstrate that the proposed method achieves state-of-the-art performance.

Deep Visual Odometry With Adaptive Memory.

We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a pure tracking problem via recovering camera poses from image snippets, leading to severe error accumulation. Global information is crucial for alleviating accumulated errors. However, it is challenging to effectively preserve such information for end-to-end systems. To deal with this challenge, we design an adaptive memory module, which progressively and adaptively saves the information from local to global in a neural analogue of memory, enabling our system to process long-term dependency. Benefiting from global information in the memory, previous results are further refined by an additional refining module. With the guidance of previous outputs, we adopt a spatial-temporal attention to select features for each view based on the co-visibility in feature domain. Specifically, our architecture consisting of Tracking, Remembering and Refining modules works beyond tracking. Experiments on the KITTI and TUM-RGBD datasets demonstrate that our approach outperforms state-of-the-art methods by large margins and produces competitive results against classic approaches in regular scenes. Moreover, our model achieves outstanding performance in challenging scenarios such as texture-less regions and abrupt motions, where classic algorithms tend to fail.

Face Restoration via Plug-and-Play 3D Facial Priors.

State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g., face super-resolution or deblurring). In this paper, we propose cross-tasks and cross-models plug-and-play 3D facial priors to explicitly embed the network with the sharp facial structures for general face restoration tasks. Our 3D priors are the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are very efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, for better exploiting this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content), a spatial attention module is designed for the image restoration problems. Extensive face restoration experiments including face super-resolution and deblurring demonstrate that the proposed 3D priors achieve superior face restoration results over the state-of-the-art algorithms.

MOUNT: Learning 6DoF Motion Prediction Based on Uncertainty Estimation for Delayed AR Rendering.

The delay of rendering on AR devices requires prediction of head motion using sensor data acquired tens of even one hundred milliseconds ago to avoid misalignment between the virtual content and the physical world, where the misalignment will lead to a sense of time latency and dizziness for users. To solve the problem, we propose a method for the 6DoF motion prediction to compensate for the time latency. Compared with traditional hand-crafted methods, our method is based on deep learning, which has better motion prediction ability to deal with complex human motion. In particular, we propose a MOtion UNcerTainty encode decode network (MOUNT) that estimates the uncertainty of input data and predicts the uncertainty of output motion to improve the prediction accuracy and smoothness. Experiments on the EuRoC and our collected dataset demonstrate that our method significantly outperforms the traditional method and greatly improves AR visual effects.

Deformable registration of lateral cephalogram and cone-beam computed tomography image.

PURPOSE: This study aimed to design and evaluate a novel method for the registration of 2D lateral cephalograms and 3D craniofacial cone-beam computed tomography (CBCT) images, providing patient-specific 3D structures from a 2D lateral cephalogram without additional radiation exposure. METHODS: We developed a cross-modal deformable registration model based on a deep convolutional neural network. Our approach took advantage of a low-dimensional deformation field encoding and an iterative feedback scheme to infer coarse-to-fine volumetric deformations. In particular, we constructed a statistical subspace of deformation fields and parameterized the nonlinear mapping function from an image pair, consisting of the target 2D lateral cephalogram and the reference volumetric CBCT, to a latent encoding of the deformation field. Instead of the one-shot registration by the learned mapping function, a feedback scheme was introduced to progressively update the reference volumetric image and to infer coarse-to-fine deformations fields, accounting for the shape variations of anatomical structures. A total of 220 clinically obtained CBCTs were used to train and validate the proposed model, among which 120 CBCTs were used to generate a training dataset with 24k paired synthetic lateral cephalograms and CBCTs. The proposed approach was evaluated on the deformable 2D-3D registration of clinically obtained lateral cephalograms and CBCTs from growing and adult orthodontic patients. RESULTS: Strong structural consistencies were observed between the deformed CBCT and the target lateral cephalogram in all criteria. The proposed method achieved state-of-the-art performances with the mean contour deviation of 0.41 ± 0.12 mm on the anterior cranial base, 0.48 ± 0.17 mm on the mandible, and 0.35 ± 0.08 mm on the maxilla, respectively. The mean surface mesh ranged from 0.78 to 0.97 mm on various craniofacial structures, and the LREs ranged from 0.83 to 1.24 mm on the growing datasets regarding 14 landmarks. The proposed iterative feedback scheme handled the structural details and improved the registration. The resultant deformed volumetric image was consistent with the target lateral cephalogram in both 2D projective planes and 3D volumetric space regarding the multicategory craniofacial structures. CONCLUSIONS: The results suggest that the deep learning-based 2D-3D registration model enables the deformable alignment of 2D lateral cephalograms and CBCTs and estimates patient-specific 3D craniofacial structures.

Essential Tensor Learning for Multi-View Spectral Clustering.

Recently, multi-view clustering attracts much attention, which aims to take advantage of multi-view information to improve the performance of clustering. However, most recent work mainly focuses on the self-representation-based subspace clustering, which is of high computation complexity. In this paper, we focus on the Markov chain-based spectral clustering method and propose a novel essential tensor learning method to explore the high-order correlations for multi-view representation. We first construct a tensor based on multi-view transition probability matrices of the Markov chain. By incorporating the idea from the robust principle component analysis, tensor singular value decomposition (t-SVD)-based tensor nuclear norm is imposed to preserve the low-rank property of the essential tensor, which can well capture the principle information from multiple views. We also employ the tensor rotation operator for this task to better investigate the relationship among views as well as reduce the computation complexity. The proposed method can be efficiently optimized by the alternating direction method of multipliers (ADMM). Extensive experiments on seven real-world datasets corresponding to five different applications show that our method achieves superior performance over other state-of-the-art methods.

Spatially Consistent Supervoxel Correspondences of Cone-Beam Computed Tomography Images.

Establishing dense correspondences of cone-beam computed tomography (CBCT) images is a crucial step for the attribute transfer and morphological variation assessment in clinical orthodontics. In this paper, a novel method, unsupervised spatially consistent clustering forest, is proposed to tackle the challenges for automatic supervoxel-wise correspondences of CBCT images. A complexity analysis of the proposed method with respect to the clustering hypotheses is provided with a data-dependent learning guarantee. The learning bound considers both the sequential tree traversals determined by questions stored in branch nodes and the clustering compactness of leaf nodes. A novel tree-pruning algorithm, guided by the learning bound, is also proposed to remove locally inconsistent leaf nodes. The resulting forest yields spatially consistent affinity estimations, thanks to the pruning penalizing trees with inconsistent leaf assignments and the combinational contextual feature channels used to learn the forest. A forest-based metric is utilized to derive the pairwise affinities and dense correspondences of CBCT images. The proposed method has been applied to the label propagation of clinically captured CBCT images. In the experiments, the method outperforms variants of both supervised and unsupervised forest-based methods and state-of-the-art label-propagation methods, achieving the mean dice similarity coefficients of 0.92, 0.89, 0.94, and 0.93 for the mandible, the maxilla, the zygoma arch, and the teeth data, respectively.

Novel tumor markers provide improved prediction of survival after diagnosis of human immunodeficiency virus (HIV)-related diffuse large B-cell lymphoma.

Existing prognostic tools for HIV + diffuse large B-cell lymphoma (DLBCL) fail to accurately predict patient outcomes. To develop a novel prognostic algorithm incorporating molecular tumor characteristics and HIV disease factors, we included 80 patients with HIV-related DLBCL diagnosed between 1996 and 2007. Immunohistochemistry staining was used to analyze the expression of 26 tumor markers. Clinical data were collected from medical records. Logistic regression and bootstrapping were used to select and assess stability of the prognostic model, respectively. Of the tumor markers examined, expression of cMYC, Ki 67, CD44, EBV, SKP2, BCL6, p53, CD20 and IgM were associated with two-year mortality. The final prognostic model, confirmed in bootstrapped samples, included IPI, circulating CD4 cell count, history of clinical AIDS, and expression of CD44, p53, IgM and EBV. This model incorporating HIV disease history and tumor markers, achieved better prediction for two-year mortality [AUC = 0.87, 95% CI: 0.78-0.96] compared with IPI alone [AUC = 0.63 (0.51-0.75)].

Robust Matrix Factorization by Majorization Minimization.

-norm based low rank matrix factorization in the presence of missing data and outliers remains a hot topic in computer vision. Due to non-convexity and non-smoothness, all the existing methods either lack scalability or robustness, or have no theoretical guarantee on convergence. In this paper, we apply the Majorization Minimization technique to solve this problem. At each iteration, we upper bound the original function with a strongly convex surrogate. By minimizing the surrogate and updating the iterates accordingly, the objective function has sufficient decrease, which is stronger than just being non-increasing that other methods could offer. As a consequence, without extra assumptions, we prove that any limit point of the iterates is a stationary point of the objective function. In comparison, other methods either do not have such a convergence guarantee or require extra critical assumptions. Extensive experiments on both synthetic and real data sets testify to the effectiveness of our algorithm. The speed of our method is also highly competitive.

Superimposition of Cone-Beam Computed Tomography Images by Joint Embedding.

OBJECTIVE: The superimposition of cone-beam computed tomography (CBCT) images is an essential step to evaluate shape variations of pre and postorthodontic operations due to pose variations and the bony growth. The aim of this paper is to present and discuss the latest accomplishments in voxel-based craniofacial CBCT superimpositions along with structure discriminations. METHODS: We propose a CBCT superimposition method based on joint embedding of subsets extracted from CBCT images. The subset is defined at local extremes of the first-order difference of Gaussian-smoothed volume images to reduce the data involved in the computation. A rotation-invariant integral operator is proposed as the context-aware textural descriptor of subsets. We cope with subset correspondences by joint embedding with matching identifications in manifolds, which take into account the structure of subsets as a whole to avoid mapping ambiguities. Once given subset correspondences, the rigid transformations, as well as the superimposition of volume images, are obtained. Our system allows users to specify the structure-of-interest based on a semisupervised label propagation technique. RESULTS: The performance of the proposed method is evaluated on ten pairs of pre and postoperative CBCT images of adult patients and ten pairs of growing patients, respectively. The experiments demonstrate that the craniofacial CBCT superimposition can be performed effectively, and outperform state of the arts. CONCLUSION: The integration of sparse subsets with context-aware spherical intensity integral descriptors and correspondence establishment by joint embedding enables the reliable and efficient CBCT superimposition. SIGNIFICANCE: The potential of CBCT superimposition techniques discussed in this paper is highlighted and related challenges are addressed.

The Shape Interaction Matrix-Based Affine Invariant Mismatch Removal for Partial-Duplicate Image Search.

Mismatch removal is a key step in many computer vision problems. In this paper, we handle the mismatch removal problem by adopting shape interaction matrix (SIM). Given the homogeneous coordinates of the two corresponding point sets, we first compute the SIMs of the two point sets. Then, we detect the mismatches by picking out the most different entries between the two SIMs. Even under strong affine transformations, outliers, noises, and burstiness, our method can still work well. Actually, this paper is the first non-iterative mismatch removal method that achieves affine invariance. Extensive results on synthetic 2D points matching data sets and real image matching data sets verify the effectiveness, efficiency, and robustness of our method in removing mismatches. Moreover, when applied to partial-duplicate image search, our method reaches higher retrieval precisions with shorter time cost compared with the state-of-the-art geometric verification methods.

EBV-negative Aggressive NK-cell Leukemia/Lymphoma: Clinical, Pathologic, and Genetic Features.

Aggressive natural killer cell leukemia (ANKL) is a systemic NK-cell neoplasm, almost always associated with Epstein-Barr virus (EBV). Rare cases of EBV-negative ANKL have been described, and some reports suggested more indolent behavior. We report the clinicopathologic, immunophenotypic, and molecular characteristics of 7 EBV-negative ANKL. All patients were adults, with a median age of 63 years (range 22 to 83 y) and an M:F ratio of 2.5:1. Five patients were White, 1 Black, and 1 Asian. All patients presented acutely, with fever (6/7), cytopenias (6/7), and splenomegaly (4/7). Four patients had lymphadenopathy, 4 had extranodal disease. Bone marrow involvement was present in 5, with hemophagocytosis in 3. Peripheral blood was involved in 5 with the neoplastic cells containing prominent azurophilic granules. By immunohistochemistry and/or flow cytometry, the tumor cells lacked surface CD3 and were positive for CD56 (7/7), CD2 (5/5), CD8 (3/7), CD30 (4/5), and granzyme-B (6/6). They were negative for CD4, CD5, ßF1, TCRγ, LMP1, and EBV-encoded RNA. Polymerase chain reaction for TCRG clonality was polyclonal. Mutational analysis revealed missense mutations in the STAT3 gene in both cases studied. Median survival was 8 weeks from the onset of disease. One patient received allogeneic bone marrow transplant and is alive with no disease (follow-up 15 mo). EBV-negative ANKL exists but is rare. It tends to occur in older patients and is indistinguishable clinically and pathologically from EBV-positive ANKL, with a similar fulminant clinical course. The high prevalence of Asian patients seen with EBV-positive disease seems less evident with EBV-negative cases.

3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images.

PURPOSE: Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. METHODS: The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. RESULTS: The proposed method was applied for tooth segmentation of twenty clinically captured CBCT images. Three metrics, including the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the mean surface deviation (MSD), were used to quantitatively analyze the segmentation of anterior teeth including incisors and canines, premolars, and molars. The segmentation of the anterior teeth achieved a DSC up to 98%, a JSC of 97%, and an MSD of 0.11 mm compared with manual segmentation. For the premolars, the average values of DSC, JSC, and MSD were 98%, 96%, and 0.12 mm, respectively. The proposed method yielded a DSC of 95%, a JSC of 89%, and an MSD of 0.26 mm for molars. Aside from the interactive definition of label priors by the user, automatic tooth segmentation can be achieved in an average of 1.18 min. CONCLUSIONS: The proposed technique enables an efficient and reliable tooth segmentation from CBCT images. This study makes it clinically practical to segment teeth from CBCT images, thus facilitating pre- and interoperative uses of dental morphologies in maxillofacial and orthodontic treatments.

Stromal immune infiltration in HIV-related diffuse large B-cell lymphoma is associated with HIV disease history and patient survival.

OBJECTIVE: Understanding tumor microenvironment and its impact on prognosis of HIV-related lymphomas may provide insight into novel therapeutic strategies. DESIGN: We characterized the relationship between infiltrating immune cells with tumor characteristics, HIV disease history and survival in 80 patients with HIV-related diffuse large B-cell lymphoma (DLBCL) diagnosed in the era of combined antiretroviral therapy (1996-2007) at Kaiser Permanente California. Eighty patients with HIV-unrelated DLBCL were included for comparison. METHODS: Data on patients' clinical history were obtained from Kaiser Permanente's electronic health records. The density of stromal CD4, CD8 and FOXP3 T cells and CD68 macrophages, as well as tumor molecular characteristics were examined using immunohistochemistry. The associations between stromal immune infiltration and patient's clinical history or tumor characteristics were examined using Kruskal-Wallis tests or Pearson's correlation coefficient. The effect of stromal immune infiltration on 2-year mortality was evaluated in multivariable logistic regression. RESULTS: Compared with HIV-unrelated DLBCL, patients with HIV-related DLBCL had significantly reduced stromal CD4 and FOXP3 T cells, but increased density of macrophages. Increased density of stromal macrophages was correlated with lower circulating CD4 cell count at DLBCL diagnosis. Tumor molecular characteristics, including BCL6, p53 and cMYC expression, but not Epstein-Barr virus infection status, were significantly correlated with stromal immune infiltration, particularly FOXP3 T cells. A higher density of infiltrating CD8 T cell was significantly associated with reduced mortality in patients with HIV-related DLBCL (odds ratio = 0.30 [0.09-0.97] for ≥25 vs. <10%). CONCLUSION: These data provide evidence for the prognostic significance of cytotoxic T cells in determining outcomes of HIV-related lymphoma.

A comparative study of molecular characteristics of diffuse large B-cell lymphoma from patients with and without human immunodeficiency virus infection.

PURPOSE: HIV-related diffuse large B-cell lymphoma (DLBCL) may be biologically different from DLBCL in the general population. We compared, by HIV status, the expression and prognostic significance of selected oncogenic markers in DLBCL diagnosed at Kaiser Permanente in California, between 1996 and 2007. EXPERIMENTAL DESIGN: Eighty HIV-infected DLBCL patients were 1:1 matched to 80 HIV-uninfected DLBCL patients by age, gender, and race. Twenty-three markers in the following categories were examined using IHC: (i) cell-cycle regulators, (ii) B-cell activators, (iii) antiapoptotic proteins, and (iv) others, such as IgM. Tumor marker expression was compared across HIV infection status by Fisher exact test. For markers differentially expressed in HIV-related DLBCL, logistic regression was used to evaluate the association between tumor marker expression and 2-year overall mortality, adjusting for International Prognostic Index, cell-of-origin phenotype, and DLBCL morphologic variants. RESULTS: Expression of cMYC (% positive in HIV-related and -unrelated DLBCL: 64% vs. 32%), BCL6 (45% vs. 10%), PKC-ß2 (61% vs. 4%), MUM1 (59% vs. 14%), and CD44 (87% vs. 56%) was significantly elevated in HIV-related DLBCLs, whereas expression of p27 (39% vs. 75%) was significantly reduced. Of these, cMYC expression was independently associated with increased 2-year mortality in HIV-infected patients [relative risk = 3.09 (0.90-10.55)] in multivariable logistic regression. CONCLUSIONS: These results suggest that HIV-related DLBCL pathogenesis more frequently involves cMYC and BCL6 among other factors. In particular, cMYC-mediated pathogenesis may partly explain the more aggressive clinical course of DLBCL in HIV-infected patients.