A Phase Ib/II Study of the JAK1 Inhibitor, Itacitinib, plus nab-Paclitaxel and Gemcitabine in Advanced Solid Tumors.

LESSONS LEARNED: Itacitinib in combination with nab-paclitaxel plus gemcitabine demonstrated an acceptable safety profile with clinical activity in patients with advanced solid tumors including pancreatic cancer.The results support future studies of itacitinib as a component of combination regimens with other immunologic and targeted small molecule anticancer agents. BACKGROUND: Cytokine-mediated signaling via JAK/STAT is central to tumor growth, survival, and systemic inflammation, which is associated with cancer cachexia, particularly in pancreatic cancer. Because of their centrality in the pathogenesis of cancer cachexia and progression, JAK isozymes have emerged as promising therapeutic targets. Preclinical studies have demonstrated antiproliferative effects of JAK/STAT pathway inhibition in both in vitro and in vivo models of cancer, including pancreatic cancer. METHODS: This phase Ib/II dose-optimization study assessed itacitinib, a selective JAK1 inhibitor, combined with nab-paclitaxel plus gemcitabine in adults with treatment-naïve advanced/metastatic disease (Part 1) or pancreatic adenocarcinoma (Parts 2/2A; NCT01858883). Starting doses (Part 1) were itacitinib 400 mg, nab-paclitaxel 125 mg/m2, and gemcitabine 1,000 mg/m2. Additional dose levels incorporated were granulocyte colony-stimulating factor, de-escalations of itacitinib to 300 mg once daily (QD), nab-paclitaxel to 100 mg/m2, and gemcitabine to 750 mg/m2. RESULTS: Among 55 patients in Part 1, 6 developed seven hematologic dose-limiting toxicities (Cycle 1). Itacitinib 300 mg plus nab-paclitaxel 125 mg/m2 and gemcitabine 1,000 mg/m2 was tolerated and expanded in Part 2. Treatment discontinuation and grade 3/4 neutropenia rates prompted itacitinib de-escalation to 200 mg QD in Part 2A. The most common grade 3/4 toxicities were fatigue and neutropenia. Partial responses occurred across all itacitinib doses and several tumor types (overall response rate, 24%). CONCLUSION: Itacitinib plus chemotherapy demonstrated acceptable safety and clinical activity in patients with advanced solid tumors including pancreatic cancers. This study was terminated early (sponsor's decision) based on negative phase III results for a JAK1/2 inhibitor in previously treated advanced pancreatic cancer.

Primary analysis of a phase II open-label trial of INCB039110, a selective JAK1 inhibitor, in patients with myelofibrosis.

Combined Janus kinase 1 (JAK1) and JAK2 inhibition therapy effectively reduces splenomegaly and symptom burden related to myelofibrosis but is associated with dose-dependent anemia and thrombocytopenia. In this open-label phase II study, we evaluated the efficacy and safety of three dose levels of INCB039110, a potent and selective oral JAK1 inhibitor, in patients with intermediate- or high-risk myelofibrosis and a platelet count ≥50×109/L. Of 10, 45, and 32 patients enrolled in the 100 mg twice-daily, 200 mg twice-daily, and 600 mg once-daily cohorts, respectively, 50.0%, 64.4%, and 68.8% completed week 24. A ≥50% reduction in total symptom score was achieved by 35.7% and 28.6% of patients in the 200 mg twice-daily cohort and 32.3% and 35.5% in the 600 mg once-daily cohort at week 12 (primary end point) and 24, respectively. By contrast, two patients (20%) in the 100 mg twice-daily cohort had ≥50% total symptom score reduction at weeks 12 and 24. For the 200 mg twice-daily and 600 mg once-daily cohorts, the median spleen volume reductions at week 12 were 14.2% and 17.4%, respectively. Furthermore, 21/39 (53.8%) patients who required red blood cell transfusions during the 12 weeks preceding treatment initiation achieved a ≥50% reduction in the number of red blood cell units transfused during study weeks 1-24. Only one patient discontinued for grade 3 thrombocytopenia. Non-hematologic adverse events were largely grade 1 or 2; the most common was fatigue. Treatment with INCB039110 resulted in clinically meaningful symptom relief, modest spleen volume reduction, and limited myelosuppression.

Phase 2 study of add-on parsaclisib for patients with myelofibrosis and suboptimal response to ruxolitinib: final results.

ABSTRACT: Ruxolitinib reduces spleen volume, improves symptoms, and increases survival in patients with intermediate- or high-risk myelofibrosis. However, suboptimal response may occur, potentially because of signaling via the phosphoinositide 3-kinase (PI3K)/protein kinase B pathway. This phase 2 study evaluated dosing, efficacy, and safety of add-on PI3Kδ inhibitor parsaclisib for patients with primary or secondary myelofibrosis with suboptimal response to ruxolitinib. Eligible patients remained on a stable ruxolitinib dose and received add-on parsaclisib 10 or 20 mg, once daily for 8 weeks, and once weekly thereafter (daily-to-weekly dosing; n = 32); or parsaclisib 5 or 20 mg, once daily for 8 weeks, then 5 mg once daily thereafter (all-daily dosing; n = 42). Proportion of patients achieving a ≥10% decrease in spleen volume at 12 weeks was 28% for daily-to-weekly dosing and 59.5% for all-daily dosing. Proportions of patients achieving ≥50% decrease at week 12 in Myelofibrosis Symptom Assessment Form and Myeloproliferative Neoplasms Symptom Assessment Form symptom scores were 14% and 18% for daily-to-weekly dosing, and 28% and 32% for all-daily dosing, respectively. Most common nonhematologic treatment-emergent adverse events were nausea (23%), diarrhea (22%), abdominal pain and fatigue (each 19%), and cough and dyspnea (each 18%). New-onset grade 3 and 4 thrombocytopenia were observed in 19% of patients, each dosed daily-to-weekly, and in 26% and 7% of patients dosed all-daily, respectively, managed with dose interruptions. Hemoglobin levels remained steady. The addition of parsaclisib to stable-dose ruxolitinib can reduce splenomegaly and improve symptoms, with manageable toxicity in patients with myelofibrosis with suboptimal response to ruxolitinib. This trial was registered at www.clinicaltrials.gov as #NCT02718300.

Targeting the PI3K pathway in myeloproliferative neoplasms.

INTRODUCTION: Decreasing efficacy over time and initial suboptimal response to Janus kinase (JAK) inhibitors such as ruxolitinib in a subset of patients are critical clinical challenges associated with myeloproliferative neoplasms (MPNs), primarily myelofibrosis. AREAS COVERED: The role of phosphatidylinositol-3 kinase (PI3K) in MPN disease progression and treatment resistance and as a potential therapeutic target in patients who experience loss of response to JAK inhibition is discussed. Understanding the complex signaling networks involved in the pathogenesis of MPNs has identified potentially novel therapeutic targets and treatment strategies, such as inhibiting other signaling pathways in addition to the JAK/signal transducer and activator of transcription (STAT) pathway. PI3K plays a crucial role downstream of JAK signaling in rescuing tumor cell proliferation, with PI3Kδ being particularly important in hematologic malignancies. Concurrent targeting of both PI3K and JAK/STAT pathways may offer an innovative therapeutic strategy to maximize efficacy. EXPERT OPINION: Based on our understanding of the underlying mechanisms and the role of PI3K pathway signaling in the loss of response or resistance to JAK inhibitor treatment and initial results from clinical studies, the combination of parsaclisib (PI3Kδ inhibitor) and ruxolitinib holds great clinical potential. If confirmed in larger clinical trials, parsaclisib may provide more treatment options and improve clinical outcomes for patients with MPNs.

In vitro and in vivo characterization of MDX-1401 for therapy of malignant lymphoma.

PURPOSE: This study was undertaken to evaluate the effects of MDX-1401, a nonfucosylated fully human monoclonal antibody that binds to human CD30, and to determine whether it exhibits greater in vitro and in vivo activity than its parental antibody. EXPERIMENTAL DESIGN: Assays measuring antibody binding to CD30-expressing cells and FcgammaRIIIa (CD16) transfectants as well as antibody-dependent cellular cytotoxicity (ADCC) were conducted. Antitumor activity was determined using a Karpas-299 systemic model. RESULTS: The binding of MDX-1401 to CD30 antigen was identical to fucose-containing parental anti-CD30 antibody (MDX-060). In contrast, MDX-1401 showed increased binding affinity to FcgammaRIIIa-transfected cells resulting in increased effector function. MDX-1401 greatly improved ADCC activity as evidenced by a decrease in half-maximal effective concentration (EC(50)) and an increase in maximum cell lysis when compared with MDX-060. Increased ADCC activity was observed among a panel of cell lines, including one with very low CD30 antigen expression in which parental antibody failed to induce any detectable ADCC. MDX-1401 activity with all FcgammaRIIIa polymorphic variants, including less active Phe/Phe158 and Phe/Val158 effector cells, was shown. Furthermore, MDX-1401 was efficacious in inhibiting tumor growth in CD30(+) lymphoma xenografts. CONCLUSIONS: The low doses of antibody required for ADCC activity irrespective of donor genotype, the ability to mediate ADCC in target cells expressing low levels of CD30, and increased in vivo efficacy support the development of MDX-1401 for treatment of malignant lymphoma.

Improving Splenomegaly Segmentation by Learning from Heterogeneous Multi-Source Labels.

Splenomegaly segmentation on computed tomography (CT) abdomen anatomical scans is essential for identifying spleen biomarkers and has applications for quantitative assessment in patients with liver and spleen disease. Deep convolutional neural network automated segmentation has shown promising performance for splenomegaly segmentation. However, manual labeling of abdominal structures is resource intensive, so the labeled abdominal imaging data are rare resources despite their essential role in algorithm training. Hence, the number of annotated labels (e.g., spleen only) are typically limited with a single study. However, with the development of data sharing techniques, more and more publicly available labeled cohorts are available from different resources. A key new challenging is to co-learn from the multi-source data, even with different numbers of labeled abdominal organs in each study. Thus, it is appealing to design a co-learning strategy to train a deep network from heterogeneously labeled scans. In this paper, we propose a new deep convolutional neural network (DCNN) based method that integrates heterogeneous multi-resource labeled cohorts for splenomegaly segmentation. To enable the proposed approach, a novel loss function is introduced based on the Dice similarity coefficient to adaptively learn multi-organ information from different resources. Three cohorts were employed in our experiments, the first cohort (98 CT scans) has only splenomegaly labels, while the second training cohort (100 CT scans) has 15 distinct anatomical labels with normal spleens. A separate, independent cohort consisting of 19 splenomegaly CT scans with labeled spleen was used as testing cohort. The proposed method achieved the highest median Dice similarity coefficient value (0.94), which is superior (p-value<0.01 against each other method) to the baselines of multi-atlas segmentation (0.86), SS-Net segmentation with only spleen labels (0.90) and U-Net segmentation with multi-organ training (0.91). Our approach for adapting the loss function and training structure is not specific to the abdominal context and may be beneficial in other situations where datasets with varied label sets are available.

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks.

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to: 1) large anatomical and spatial variations of splenomegaly; 2) large inter- and intra-scan intensity variations on multi-modal MRI; and 3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3-D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method has achieved the highest median and mean Dice scores among the investigated baseline DCNN methods.

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline.

Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volume and biomarkers but also for monitoring interventions. Recently, segmentation algorithms using deep learning have been widely used to reduce time humans spend to label CT data. However, the computerized segmentation has two major difficulties: managing intermediate results (e.g., resampled scans, 2D sliced image for deep learning), and setting up the system environments and packages for autonomous execution. To overcome these issues, we propose an automated pipeline for the abdominal spleen segmentation. This pipeline provides an end-to-end synthesized process that allows users to avoid installing any packages and to deal with the intermediate results locally. The pipeline has three major stages: pre-processing of input data, segmentation of spleen using deep learning, 3D reconstruction with the generated labels by matching the segmentation results with the original image dimensions, which can then be used later and for display or demonstration. Given the same volume scan, the approach described here takes about 50 s on average whereas the manual segmentation takes about 30 min on the average. Even if it includes all subsidiary processes such as preprocessing and necessary setups, the whole pipeline process requires on the average 20 min from beginning to end.

Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation.

OBJECTIVE: Magnetic resonance imaging (MRI) is an essential imaging modality in noninvasive splenomegaly diagnosis. However, it is challenging to achieve spleen volume measurement from three-dimensional MRI given the diverse structural variations of human abdomens as well as the wide variety of clinical MRI acquisition schemes. Multi-atlas segmentation (MAS) approaches have been widely used and validated to handle heterogeneous anatomical scenarios. In this paper, we propose to use MAS for clinical MRI spleen segmentation for splenomegaly. METHODS: First, an automated segmentation method using the selective and iterative method for performance level estimation (SIMPLE) atlas selection is used to address the concerns of inhomogeneity for clinical splenomegaly MRI. Then, to further control outliers, semiautomated craniocaudal spleen length-based SIMPLE atlas selection (L-SIMPLE) is proposed to integrate a spatial prior in a Bayesian fashion and guide iterative atlas selection. Last, a graph cuts refinement is employed to achieve the final segmentation from the probability maps from MAS. RESULTS: A clinical cohort of 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate both automated and semiautomated methods. CONCLUSION: The results demonstrated that both methods achieved median Dice , and outliers were alleviated by the L-SIMPLE (≍1 min manual efforts per scan), which achieved 0.97 Pearson correlation of volume measurements with the manual segmentation. SIGNIFICANCE: In this paper, spleen segmentation on MRI splenomegaly using MAS has been performed.

A Phase Ib study of ruxolitinib + gemcitabine ± nab-paclitaxel in patients with advanced solid tumors.

PURPOSE: Aberrant activation of the Janus-associated kinase (JAK)/signal transducer and activator of transcription (STAT) pathway is associated with increased malignant cell proliferation and survival. This Phase Ib study evaluated ruxolitinib, a potent JAK1/2 inhibitor, in combination with gemcitabine with or without nab-paclitaxel in patients with advanced solid tumors. PATIENTS AND METHODS: Patients received ruxolitinib + gemcitabine (regimen A) or ruxolitinib + gemcitabine + nab-paclitaxel (regimen B). The objective of the dose-finding phase was to identify the maximum tolerated doses (MTDs) of ruxolitinib plus gemcitabine with or without nab-paclitaxel. RESULTS: Among 42 patients enrolled, the median age was 62.5 years, 81.0% had pancreatic cancer, and almost 62% had received prior systemic therapy. Regimen A was tolerated with standard doses of gemcitabine; regimen B was tolerated with reduced doses of gemcitabine/nab-paclitaxel or concomitant granulocyte colony-stimulating factor. The sponsor decided to terminate the study early due to the interim analysis results of the Phase III JANUS 1 study. Discontinuations were mainly due to radiologic or clinical disease progression (81.0% of patients). Median treatment durations were 55.5 days (cohort A0) and 150.5 days (pooled B cohorts). Four patients (pooled B cohorts) had dose-limiting toxicities: grade 3 pneumonia (n=1), grade 4 neutropenia (n=1), and grade 4 thrombocytopenia (n=2). The most common grade 3/4 hematologic adverse events (AEs) were anemia, thrombocytopenia, and neutropenia. Serious AEs occurring in ≥2 patients in cohort A0 or pooled B cohorts were abdominal pain, sepsis (cohort A0), dehydration, anemia, and asthenia (pooled B cohorts). Overall response rates (ORRs) were 12.5% in cohort A0 and 38.5% in pooled B cohorts. Among patients with pancreatic cancer, ORR was 23.5% (14.0% cohort A0 30.0% pooled B cohorts). CONCLUSION: The study was terminated early prior to reaching MTDs per sponsor decision; although ruxolitinib plus gemcitabine with or without nab-paclitaxel was generally safe and well tolerated in patients with advanced solid tumors, this combination will not be pursued further.

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth.

A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks (CycleGAN) and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: (1) MRI to CT splenomegaly synthetic segmentation for abdominal images, and (2) CT to MRI total intracranial volume synthetic segmentation (TICV) for brain images. The proposed end-to-end approach achieved superior performance to two stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available 2.

Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation.

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities.

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks.

Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.

Randomized, double-blind, phase two study of ruxolitinib plus regorafenib in patients with relapsed/refractory metastatic colorectal cancer.

BACKGROUND: The Janus kinase/signal transducer and activator of transcription (JAK-STAT) signaling pathway plays a key role in the systemic inflammatory response in many cancers, including colorectal cancer (CRC). This study evaluated the addition of ruxolitinib, a potent JAK1/2 inhibitor, to regorafenib in patients with relapsed/refractory metastatic CRC. METHODS: In this two-part, multicenter, phase 2 study, eligible adult patients had metastatic adenocarcinoma of the colon or rectum; an Eastern Cooperative Oncology Group performance status of 0-2; received fluoropyrimidine, oxaliplatin, and irinotecan-based chemotherapy, an anti-vascular endothelial growth factor therapy (if no contraindication); and if KRAS wild-type (and no contraindication), an anti-epidermal growth factor receptor therapy; and progressed following the last administration of approved therapy. Patients who received previous treatment with regorafenib, had an established cardiac or gastrointestinal disease, or had an active infection requiring treatment were excluded. The study was conducted in 95 sites in North America, European Union, Asia Pacific, and Israel. After an open-label, safety run-in phase (part 1; ruxolitinib 20 mg twice daily [BID] plus regorafenib 160 mg once daily [QD]), the double-blind, randomized phase (part 2) was conducted wherein patients were randomized 1:1 to receive ruxolitinib 15 mg BID plus regorafenib 160 mg QD [ruxolitinib group] or placebo plus regorafenib 160 mg QD [placebo group]. Part 2 included substudy 1 (patients with high systemic inflammation, ie, C-reactive protein [CRP] >10 mg/L) and substudy 2 (patients with low systemic inflammation, ie, CRP ≤10 mg/L); the primary endpoint was overall survival (OS). RESULTS: The study was terminated early; substudy 1 was terminated for futility at interim analysis and substudy 2 was terminated per sponsor decision. Ruxolitinib 20 mg BID was well tolerated in the safety run-in (n = 11). Overall, 396 patients were randomized (substudy 1: n = 175 [ruxolitinib group, n = 87; placebo group, n = 88]; substudy 2: n = 221 [ruxolitinib group, n = 110; placebo group, n = 111]). There was no significant difference in OS or progression-free survival (PFS) between treatments in substudy 1 (OS: hazard ratio [HR] = 1.040 [95% confidence interval: 0.725-1.492]; PFS: HR = 1.004 [0.724-1.391]) and substudy 2 (OS: HR = 0.767 [0.478-1.231]; PFS: HR = 0.787 [0.576-1.074]). The most common hematologic adverse event was anemia. No new safety signals with ruxolitinib were identified. CONCLUSIONS: Although addition of ruxolitinib to regorafenib did not show increased safety concerns in patients with relapsed/refractory metastatic CRC, this combination did not improve OS/PFS vs. regorafenib plus placebo.

Multi-atlas Segmentation Enables Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly.

Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≈1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

Multi-Atlas Spleen Segmentation on CT Using Adaptive Context Learning.

Automatic spleen segmentation on CT is challenging due to the complexity of abdominal structures. Multi-atlas segmentation (MAS) has shown to be a promising approach to conduct spleen segmentation. To deal with the substantial registration errors between the heterogeneous abdominal CT images, the context learning method for performance level estimation (CLSIMPLE) method was previously proposed. The context learning method generates a probability map for a target image using a Gaussian mixture model (GMM) as the prior in a Bayesian framework. However, the CLSSIMPLE typically trains a single GMM from the entire heterogeneous training atlas set. Therefore, the estimated spatial prior maps might not represent specific target images accurately. Rather than using all training atlases, we propose an adaptive GMM based context learning technique (AGMMCL) to train the GMM adaptively using subsets of the training data with the subsets tailored for different target images. Training sets are selected adaptively based on the similarity between atlases and the target images using cranio-caudal length, which is derived manually from the target image. To validate the proposed method, a heterogeneous dataset with a large variation of spleen sizes (100 cc to 9000 cc) is used. We designate a metric of size to differentiate each group of spleens, with 0 to 100 cc as small, 200 to 500cc as medium, 500 to 1000 cc as large, 1000 to 2000 cc as XL, and 2000 and above as XXL. From the results, AGMMCL leads to more accurate spleen segmentations by training GMMs adaptively for different target images.

Phase I/II study of an anti-CD30 monoclonal antibody (MDX-060) in Hodgkin's lymphoma and anaplastic large-cell lymphoma.

PURPOSE: MDX-060 is a human anti-CD30 immunoglobulin (Ig) G1kappa monoclonal antibody that inhibits growth of CD30-expressing tumor cells in preclinical models. To determine the safety, maximum-tolerated dose (MTD), and efficacy of MDX-060 in patients with relapsed or refractory CD30+ lymphomas, sequential phase I and II studies were performed. PATIENTS AND METHODS: In the phase I portion, MDX-060 was administered intravenously at doses of 0.1, 1, 5, or 10 mg/kg weekly for 4 weeks to cohorts of three to six patients. Twenty-one patients--16 with Hodgkin's lymphoma (HL), three with anaplastic large-cell lymphoma (ALCL), and two with CD30+ T-cell lymphoma--were enrolled. Because of the lack of a defined MTD or dose-response correlation, the phase II portion was amended to include several dose levels. In the phase II portion, an additional 51 patients, 47 with HL and four with ALCL, were treated at doses of 1, 5, 10, and 15 mg/kg. RESULTS: MDX-060 was well tolerated, and an MTD has not been identified. Only 7% of patients experienced grade 3 or 4 treatment-related adverse events. Among the 72 patients treated, clinical responses were observed in six. Twenty-five patients had stable disease, including five who remained free from progression 1 year after treatment. CONCLUSION: MDX-060 was well tolerated at doses up to 15 mg/kg. MDX-060 has limited activity as a single agent, but the minimal toxicity observed and the significant proportion of patients with stable disease suggests that further study of MDX-060 in combination with other therapies is warranted.