We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App


ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

Researchers Develop AI Algorithm to Predict Immunotherapy Response

By MedImaging International staff writers
Posted on 12 Sep 2018
Print article
A team of French researchers have designed an algorithm and developed it to analyze Computed Tomography (CT) scan images, establishing for the first time that artificial intelligence (AI) can process medical images to extract biological and clinical information. The researchers have created a so-called radiomic signature, which defines the level of lymphocyte infiltration of a tumor and provides a predictive score for the efficacy of immunotherapy in the patient.

In the near future, this could make it possible for physicians to use imaging to identify biological phenomena in a tumor located anywhere in the body without performing a biopsy.

Currently, there are no markers, which can accurately identify patients who will respond to anti-PD-1/PD-L1 immunotherapy in a situation where only 15 to 30% of patients do respond to such treatment. The more immunologically richer the tumor environment (presence of lymphocytes), the higher is the chances of immunotherapy being effective. Hence, the researchers tried to characterize this environment using imaging and correlate this with the patients’ clinical response. In their study, the radiomic signature was captured, developed and validated genomically, histologically and clinically in 500 patients with solid tumors (all sites) from four independent cohorts.

The researchers first used a machine learning-based approach to teach the algorithm how to use relevant information extracted from CT scans of patients participating in an earlier study, which also held tumor genome data. Thus, based solely on images, the algorithm learned to predict what the genome might have revealed about the tumor immune infiltrate, in particular with respect to the presence of cytotoxic T-lymphocytes (CD8) in the tumor, thus establishing a radiomic signature.

The researchers tested and validated this signature in other cohorts, including that of TCGA (The Cancer Genome Atlas), thus demonstrating that imaging could predict a biological phenomenon, providing an estimation of the degree of immune infiltration of a tumor. Further, in order to test the signature’s applicability in a real situation and correlate it to the efficacy of immunotherapy, it was evaluated using CT scans performed before the start of treatment in patients participating in five phase I trials of anti-PD-1/PD-L1 immunotherapy. The researchers found that the patients in whom immunotherapy was effective at three and six months had higher radiomic scores as did those with better overall survival.

In their next clinical study, the researchers will assess the signature both retrospectively and prospectively, using a larger number of patients and stratifying them based on cancer type in order to refine the signature. They will also use more sophisticated automatic learning and AI algorithms to predict patient response to immunotherapy, while integrating data from imaging, molecular biology and tissue analysis. The researchers aim to identify those patients who are most likely to respond to treatment, thereby improving the efficacy/cost ratio of treatment.

Gold Supplier
Portable X-Ray System
FDR Xair
Mobile X-Ray Table
X Mobil
X-Ray Wall Stand
Dosimetry Software
BEAMSCAN Software 4.5

Print article
FIME - Informa
Sun Nuclear -    Mirion



view channel
Image: BiOI ruby-like crystals can improve medical imaging safety by lowering intensities of harmful X-rays (Photo courtesy of University of Cambridge)

Sustainable Solar Cell Material Could Revolutionize Medical Imaging

The use of X-rays for internal body imaging has dramatically changed non-invasive medical diagnostics. Yet, the high dose of X-rays required for these imaging techniques, due to the poor performance of... Read more


view channel
Image: An international, multi-institutional project aims to develop a radically new MRI scanner that is compact and transportable (Photo courtesy of U of M Medical School)

Compact and Portable MRI Scanner to Expand Existing Imaging Capabilities and Accessibility

Magnetic Resonance Imaging (MRI) technology which provides detailed images of the human brain is instrumental in understanding brain functions and diagnosing medical conditions. MRI has become indispensable... Read more


view channel
Image: A new study has shown the value of endoscopic ultrasound in NSCLC (Photo courtesy of Freepik)

Endoscopic Ultrasound Can Provide Value in NSCLC, Finds Study

The usefulness of confirmatory mediastinoscopy following tumor-negative results on endoscopic ultrasound still remains debatable among researchers. This procedure is often employed for mediastinal staging... Read more

Nuclear Medicine

view channel
Image: New imaging method offers potential for diagnosing, staging, and treating multiple types of cancer (Photo courtesy of SNMMI)

New Imaging Method Superior for Diagnosing Multiple Types of Cancer

Cancer-associated fibroblasts play a significant role in tumor development, migration, and progression. A subset of these fibroblasts expresses fibroblast activation protein (FAP), a protein prominently... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.