Albert Assad

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.

Ruxolitinib for essential thrombocythemia refractory to or intolerant of hydroxyurea: long-term phase 2 study results

To the editor: Essential thrombocythemia (ET) is a Philadelphia chromosome–negative myeloproliferative neoplasm (MPN) characterized by persistent thrombocytosis, excessive bone marrow megakaryocyte proliferation, and normal erythrocyte mass.[1][1] Symptoms may include bone pain, pruritus, night

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.

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. 1

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.

A Phase Ib/II Study of the JAK1 Inhibitor, Itacitinib, plusnab‐Paclitaxel and Gemcitabine in Advanced Solid Tumors

Abstract 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 (sponsors decision) based on negative phase III results for a JAK1/2 inhibitor in previously treated advanced pancreatic cancer.

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

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.

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.