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

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.

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.

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.

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.

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.

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.

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.

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

Lessons Learned: Itacitinib in combination with -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.

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.

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.

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).

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.

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.

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.

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 ≥50×10/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.

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.

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.