H. Lee Moffitt Cancer Center and Research Institute, Inc.

The University of Arizona

University of South Florida

The University of Texas System

Intezyne Technologies, Incorporated

Other

Leland Stanford Junior University

The United States of America, As Represented by the Secretary, Department of Health and Human Services

The Translational Genomics Research Institute

Stichting Maastricht Radiation Oncology “maastro-clinic”

Robert J Gillies

David L Morse

Josef Vagner

Narges Tafreshi

Victor J Hruby

Amanda Huynh

Jonathan L Sessler

Kevin N Sill

Dmitry B Goldgof

Lawrence O Hall

Christian Preihs

Natalie M Barkey

Robert A Gatenby

Mark L McLaughlin

Michael Doligalski

Sandy A Napel

Philippe Lambin

Steven A Eschrich

Daniel L Rubin

Sylvia K Plevritis

Matthew B Schabath

Olya Stringfield

Andreas L A J Dekker

Gary V Martinez

James B Mitchell

Mark Ronald McLaughlin

Murali Krishna Cherukuri

Eugene A Mash

Haiyong Han

Marilyn M Bui

Steven Alan Enkemann

Haibin Tian

Shingo Matsumoto

Rahul Paul

Thaddeus Wadas

Yoganand Balagurunathan

Hyun Joo Kil

Ariosto Siqueira Silva

William Bradford Carter

Keita Saito

Natarajan Raghunand

Wei Mu

John Arrington

Parastou Foroutan

Suryakiran Navath

Arig A Ibrahim Hashim

Two major treatment strategies employed in fighting non-small cell lung cancer (NSCLC) are tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). The choice of strategy is based on heterogeneous biomarkers expressed by the lung tumor tissue. A major challenge for molecular testing of these biomarkers is the insufficiency of biopsy specimens from patients with advanced NSCLC. Disclosed herein is a method for predicting a response to immune-checkpoint blockade immunotherapy. The method generally involves imaging the subject with positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with computed tomography to produceF-FDG PET/CT images of the tumor, analyzing the images using PET, CT, and Kulbek Leibler Divergence statistical (KLD) features or, alternatively using deep leaning such as Neural Networks; generating a radiomic signature from the identified features or Network characteristics; and computing a radiomic score based on the radiomic signature that is predictive of responsiveness to ICIs or TKIs.

Radiomics-based treatment decision support for lung cancer

The subject invention pertains to a modified MC1R peptide ligand comprising a peptide that is a melanocortin 1 receptor (MC1R) ligand and a functionality or linker, such as a click functionality, for conjugation to a surface or agent. The modified MC1R peptide ligand can be coupled, e.g., via a click reaction with a complementary click functionality attached, to a moiety to form an MC1R-targeted agent. Drugs, contrast agents, polymers, particles, micelles, surfaces of larger structures, or other moieties can be targeted to the MC1R. The subject invention also pertains to a MC1R peptide ligand-micelle complex comprising a peptide that is a melanocortin 1 receptor ligand connected via a click reaction product to a micelle. The micelle is stable in vivo and can target melanoma tumor cells by association of the peptide ligand with the MC1R or the tumor and selectively provide a detectable and/or therapeutic agent (such as an imagable contrast agent and/or anti-cancer agent) selectively to the tumor cell.

Melanocortin 1 receptor ligands and methods of use

The present disclosure describes a multi-initialization ensemble-based defense strategy against an adversarial attack. In one embodiment, an exemplary method includes training a plurality of conventional neural networks (CNNs) with a training set of images, wherein the images include original images and images modified by an adversarial attack; after training of the plurality of conventional neural networks, providing an input image to the plurality of conventional neural networks, wherein the input image has been modified by an adversarial attack; receiving a probability output for the input image from each of the plurality of conventional neural networks; producing an ensemble probability output for the input image by combining the probability outputs from each of the plurality of conventional neural networks; and labeling the input image as belonging to one of the one or more categories based on the ensemble probability output.

Mitigating adversarial attacks on medical imaging understanding systems

Cellular targets on cancer cells have been identified that can be used with targeted molecular imaging to detect the cancer cells in vivo. Non-invasive methods for detecting cancer cells, such as metastasized cancer cells, are therefore provided. Also provided are compositions and kits for use in the disclosed methods.

Molecular imaging of cancer cells in vivo

Disclosed are monoacylated Toll-like receptor 2 ligands which can be used in both the development of targeted agents for the imaging and treatment of pancreatic cancer as well as other cancers, and as an adjuvant for cancer immunotherapy. The monoacylated compounds disclosed herein have a higher binding affinity for TLR2 relative to a known potent diacylated agonists, but only &#x2212;&#xbd; the bioactivity. Competition of the monoacylated compound with the diacylated compound for binding TLR2 was confirmed. Hence, the reported monoacylated compounds are inhibitors/antagonists of TLR2 activation.

Toll-like receptor 2 ligands and methods of making and using thereof

The subject matter disclosed herein relates generally to cancer therapy and to anti-cancer compounds and imaging agents. More specifically, the subject matter disclosed herein relates to agents that target MC1R and their use in the treatment of cancer. Methods of screening for MC1R targeted agents are also disclosed.

Radiotherapeutic and companion imaging agents to target MC1R

The subject invention pertains to a modified MC1R peptide ligand comprising a peptide that is a melanocortin 1 receptor (MC1R) ligand and a functionality or linker, such as a click functionality, for conjugation to a surface or agent. The modified MC1R peptide ligand can be coupled, e.g., via a click reaction with a complementary click functionality attached, to a moiety to form an MC1R-targeted agent. Drugs, contrast agents, polymers, particles, micelles, surfaces of larger structures, or other moieties can be targeted to the MC1R. The subject invention also pertains to a MC1R peptide ligand-micelle complex comprising a peptide that is a melanocortin 1 receptor ligand connected via a click reaction product to a micelle. The micelle is stable in vivo and can target melanoma tumor cells by association of the peptide ligand with the MC1R or the tumor and selectively provide a detectable and/or therapeutic agent (such as an imageable contrast agent and/or anti-cancer agent) selectively to the tumor cell.

Disclosed is CT or MR contrast agent which comprises a base or carrier scaffold formed of a polyhydroxol compound having a linker to which a Gd-DOTA is covalently bonded. Also disclosed is a method of screening a patient for colon cancer using a CT or MR contrast, which method comprises administering to a patient undergoing screening a compound as above described.

Method and compositions for orally administered contrast agents for MR imaging

Virtually every cancer patient is imaged with CT, PET or MRI. Importantly, such imaging reveals that tumors are complex and heterogeneous, often containing multiple habitats within them. Disclosed herein are methods for analyzing these images to infer cellular and molecular structure in each of these habitats. The methods can involve spatially superimposing two or more radiological images of the tumor sufficient to define regional habitat variations in two or more ecological dynamics in the tumor, and comparing the habitat variations to one or more controls to predict the severity of the tumor.

Radiologically identified tumor habitats

An example method for diagnosing tumors in a subject by performing a quantitative analysis of a radiological image can include identifying a region of interest (ROI) in the radiological image, segmenting the ROI from the radiological image, identifying a tumor object in the segmented ROI and segmenting the tumor object from the segmented ROI. The method can also include extracting a plurality of quantitative features describing the segmented tumor object, and classifying the tumor object based on the extracted quantitative features. The quantitative features can include one or more texture-based features.

Systems and methods for diagnosing tumors in a subject by performing a quantitative analysis of texture-based features of a tumor object in a radiological image

Disclosed are methods for quantitatively predicting the severity of a tumor in a subject. In some embodiments, the methods further comprise selecting a course of therapy for the subject. In some embodiments, the tumor comprises is non-small cell lung cancer.

Quantitative predictors of tumor severity

An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Systems, methods and devices for analyzing quantitative information obtained from radiological images

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Tucson, AZ, United States of America

Robert J Gillies