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Predicting the Response to Immunotherapy Using Artificial Intelligence


A study published in The Lancet Oncology establishes for the first
time that artificial intelligence can process medical images to extract
biological and clinical information. By designing an algorithm and
developing it to analyse CT scan images, medical researchers at Gustave
Roussy, CentraleSupélec, Inserm, Paris-Sud University and TheraPanacea
(spin-off from CentraleSupélec specialising in artificial intelligence
in oncology-radiotherapy and precision medicine) have created a
so-called radiomic signature. This signature defines the level of
lymphocyte infiltration of a tumour and provides a predictive score for
the efficacy of immunotherapy in the patient.

In the future,
physicians might thus be able to use imaging to identify biological
phenomena in a tumour located in any part of the body without having to
perform a biopsy.

Up to now, no marker can accurately identify those 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. It is known that the
richer the tumour environment is immunologically (presence of
lymphocytes) the greater the chance that immunotherapy will be
effective, so the researchers have tried to characterise this
environment using imaging and correlate this with the patients' clinical
response. Such is the objective of the radiomic signature designed and
validated in the study published in The Lancet Oncology.

In this retrospective study, the radiomic signature was captured,
developed and validated in 500 patients with solid tumours (all sites)
from four independent cohorts. It was validated genomically,
histologically and clinically, making it particularly robust.

Using an approach based on machine learning, the team first taught the
algorithm to use relevant information extracted from CT scans of
patients participating in the MOSCATO study1, which also held
tumor genome data. Thus, based solely on images, the algorithm learned
to predict what the genome might have revealed about the tumour immune
infiltrate, in particular with respect to the presence of cytotoxic
T-lymphocytes (CD8) in the tumour, and it established a radiomic

This signature was tested and validated in other cohorts including that
of TCGA (The Cancer Genome Atlas) thus showing that imaging could
predict a biological phenomenon, providing an estimation of the degree
of immune infiltration of a tumour.

Then, to test the applicability of this signature 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 5 phase I trials of anti-PD-1/PD-L1 immunotherapy. It
was found that the patients in whom immunotherapy was effective at 3 and
6 months had higher radiomic scores as did those with better overall

The next clinical study will assess the signature both retrospectively
and prospectively, will use larger numbers of patients and will stratify
them according to cancer type in order to refine the signature.

This will also employ more sophisticated automatic learning and
artificial intelligence algorithms to predict patient response to
immunotherapy. To that end, the researchers are intending to integrate
data from imaging, molecular biology and tissue analysis. This is the
objective of the collaboration between Gustave Roussy, Inserm,
Université Paris-Sud, CentraleSupélec and TheraPanacea to identify those
patients who are the most likely to respond to treatment, thus improving
the efficacy/cost ratio of the treatment.

// About radiomics
In radiomics, it is considered that
imaging (CT, MRI, ultrasound, etc.) not only reveals the organisation
and architecture of tissues but also their molecular or cellular
composition. The technique involves the use of algorithms to analyse a
medical image objectively in order to extract from it information which
is invisible to the naked eye, such as the texture of a tumour, its
micro-environment, its heterogeneity, etc. For the patient this
represents a non-invasive approach that can be repeated over the course
of the disease to follow its progress.

Lancet Oncology

1 Results of the MOSCATO study published in Cancer Discovery

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