Early Response Assessment Through Multiparametric MRI Based Endpoints In A Phase II Multicenter Study Evaluating the Efficacy of DPX-Survivac, Intermittent Low Dose Cyclophosphamide (CPA) and Pembrolizumab Combination Study in Subjects with Solid Tumors.

Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.e14245

Abstract

BACKGROUND:
Accurate assessment of tumor response to immunotherapy is challenged by pseudoprogression that mimics true progression. Conventional imaging and RECIST assessment do not adequately distinguish between them given their inability to account for changes in the tumor microenvironment. DPX-Survivac is a novel T cell activating therapy that triggers immune responses against tumors expressing survivin and is being studied in this trial in combination with CPA and pembrolizumab in several solid tumors. Multiparametric MRI approaches – dynamic contrast-enhanced MRI and diffusion-weighted imaging MRI are useful for accurate assessment of structural, perfusion and vascular assessment of the lesion and may identify pseudoprogression and compare to the RECIST-based assessment.
METHODS:
The study will enroll up to 226 evaluable subjects in 5 different cohorts: ovarian cancer, HCC, NSCLC, bladder cancer and MSI-H cancer. These subjects will undergo initial imaging 28 days prior to treatment, to be assessed based on RECIST 1.1, and a pre-treatment tumor biopsy for quantitation of survivin and PD-L1 expression and MSI analyses. Treatment for 35 cycles or until disease progression. All patients will have CT images for RECIST 1.1 and iRECIST assessment. A subset of subjects will undergo mpMRI to calculate advanced imaging biomarkers.
RESULTS:
MRI, clinical and patient-reported outcomes will be analyzed.
CONCLUSIONS:
This study will provide important evidence on the utility of mpMRI + CT-based assessment of response to immunotherapy and use it as an adjunct to the CT-based RECIST criteria by providing insight on how tumor lesions are impacted by treatment.

Lung Segmentation from CT with Severe Pathologies Using Anatomical Constraints

Lung Segmentation from CT with Severe Pathologies Using Anatomical Constraints

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Robust Segmentation of Challenging Lungs in CT Using Multi-Stage Learning and Level Set Optimization

Robust Segmentation of Challenging Lungs in CT Using Multi-Stage Learning and Level Set Optimization

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Nunc magna turpis, tristique at dictum vel, sollicitudin blandit felis. Morbi aliquam elit et pellentesque vulputate. Donec elementum, ante quis ornare porttitor, tortor dolor vestibulum velit, et viverra enim massa vitae ex.

Image Processing for Computed Tomography Applications – Segmentation of Vascular Structures in Human Organs

Image Processing for Computed Tomography Applications – Segmentation of Vascular Structures in Human Organs

Maecenas tempor, ex at maximus efficitur, felis sem ultrices ligula, ut hendrerit purus eros ac urna. Vestibulum ut vestibulum tortor. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Cras eu lectus quam. Nunc ligula arcu, auctor sit amet tellus eleifend, facilisis laoreet odio. Donec placerat urna eleifend blandit porttitor.

Nunc magna turpis, tristique at dictum vel, sollicitudin blandit felis. Morbi aliquam elit et pellentesque vulputate. Donec elementum, ante quis ornare porttitor, tortor dolor vestibulum velit, et viverra enim massa vitae ex.