Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06163846 |
| Other study ID # |
AIRC_IG_2019_23596 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
July 10, 2020 |
| Est. completion date |
June 2025 |
Study information
| Verified date |
December 2023 |
| Source |
IRCCS San Raffaele |
| Contact |
Alessandra Maielli |
| Phone |
0226433639 |
| Email |
maielli.alessandra[@]hsr.it |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Lung cancer is the leading cause of cancer-related death in Europe. Pathological staging is
the gold standard, but it can be influenced by neo-adjuvant treatment and number of sampled
lymph nodes; it is not feasible in advanced stages and in patients with high-risk
comorbidities. Therefore, patients with tumors of the same stage can experience variations in
the incidence of recurrence and survival since suboptimal staging leads to inappropriate
treatment that result in poorer outcomes. It is still undetermined what are the tumor
characteristics that can accurately assess tumor burden and predict patient outcome.Our
central hypothesis is that image-derived and genetic characteristics are consistent with
disease stage and patient outcome. Combining through artificial intelligence techniques data
coming from imaging and circulating cell-free tumor DNA (ctDNA) can provide accurate staging
and predict outcome. This hypothesis has been formulated based on preliminary data and on the
evidence that image-derived biomarkers by means of image mining (radiomics and deep learning
algorithms) are able to provide "phenotype" and prognostic information. On the other hand,
the analysis of ctDNA isolated from the plasma of patients has been proposed as an
alternative method to assess the disease in the different phases, in particular, at diagnosis
and after surgery, for detection of residual disease.
Description:
Our central hypothesis is that peculiar image-derived and genetic characteristics are
consistent with disease stage and patient outcome. Therefore, they are biomarkers of disease
burden and relapse, which can be non- invasively assessed. The combination through artificial
intelligence methods of data coming from medical imaging and circulating cell-free tumor DNA
(ctDNA) can provide accurate staging and outcome prediction. This hypothesis has been
formulated based on the evidence that medical images are able to provide meanable data
reflecting tumor characteristics, capturing intrinsic tumor heterogeneity, non-invasively,
using a whole- body and whole-lesion assessment. In fact, in recent years, advanced analysis
of medical imaging using radiomics, machine learning or in combination - image mining, has
been explored. Image- derived biomarkers, by means of texture feature extraction and
convolutional neural network application, have been tested to provide "phenotype" information
(malignant vs benign, and histotype identification, and T or N staging. Moreover,
correlations between image-derived quantitative features with tissue gene-expression patterns
have been shown, linking the imaging phenotypes to the genotype as also demonstrated in our
preliminary data. Secondly, image mining approach has been proposed to provide prognostic
information at baseline evaluation, as also shown in our previous work. Still, few
prospective studies with robust methodological approach have been published. On the other
hand, the analysis of circulating cell-free tumor DNA (ctDNA) isolated from the plasma of
lung cancer patients has been proposed as an alternative method to assess the disease in the
different phases. In particular, at diagnosis, the post-surgical detection of residual
disease, the identification of mutations in the metastatic setting for treatment guidance and
monitoring treatment response. Even if, ctDNA has been detected in patients with all stages
of NSCLC with levels increasing with stage and tumor burden ctDNA information has not been
explored yet for the purpose of staging. The possibility to detect a tumor in the early phase
of its development or the recurrence has to face the issue of the low amount of cfDNA in
patients with minimal disease burden. Moreover, the presence of a para-physiological ctDNA
background particularly in aged people affects the specificity. In this respect, the
investigators expect that the combination of different biomarkers will allow to solve this
problem.Artificial Intelligence analytics are increasingly described in healthcare
applications. In recent years, supervised, semi-supervised, and unsupervised machine learning
methods have been applied to analyze genomic, proteomic, clinical data and radiographical
characteristics. Deep learning methods offer opportunities for comprehensive analysis of
multi-dimensional data for improved prognosis prediction. The rationale for the proposed
project is that, once it is known which imaging features and ctDNA-derived information is
linked to the tumor stage and post-operative risk of relapse, the developed algorithm will be
an effective and innovative approach for both staging and follow-up of patients affected by
lung cancer, with implications on decision-making in clinical practice.
