Advanced Deep Learning Embedded Motion Radiomics Pipeline for Predicting Anti-PD-1/PD-L1 Immunotherapy Response in the Treatment of Bladder Cancer: Preliminary Results
Articolo
Data di Pubblicazione:
2019
Abstract:
A key objective of modern medicine is precision medicine, whose purpose is to personalize
the treatment based on the specific characteristics of the patients and their illness. To guide treatment
decisions, it is generally necessary to have a sample of the neoplastic tissue, which is obtained
only with biopsies or similar invasive surgical procedures. As tumors are heterogeneous in their
volume and change over time, a dynamic analysis of diagnostic medical images can provide a better
understanding of the entire tumor, both in the screening and follow-up phase. In this work, the authors
proposed the use of a radiomics pipeline which is able to characterize the possible response of the
oncological patients to the anti- programmed death-ligand protein 1 (PD-L1) immunotherapeutic
treatment. The immunotherapeutic treatment consists of a modern therapeutic approach in which the
physicians try to reactivate the patient’s immune system so that it recognizes and destroys cancer cells.
The oncological biomarkers capable of characterizing patients who can benefit from immunotherapy
from those who would not, are being studied. One of them is related to the expression of the PD-L1
inhibitor in the surface of neoplastic cells which are analyzed in this paper, considering that the
analyzed immunotherapeutic treatment is of the anti-PD-L1 type. In this context, the authors propose
a pipeline for an immunotherapy response prediction based on the analysis of only CT-scan images
of patients with metastatic bladder cancer. Using a framework based on the use of deep Autoeconder
network, CT-scan images were analyzed to extract the features capable of discriminating the patient’s
response to anti-PD-L1 immunotherapy treatment from those who are not. The preliminary results
obtained (accuracy of approximately 86% with a sensitivity of approximately 80% against a specificity
of approximately 89%) on the analyzed patient dataset, allows the confirmation of the feasibility of the
proposed method. Although validated in a dataset containing patients with only one tumor histology
(bladder cancer), the proposed method shows how modern radiomics techniques can contribute
significantly in the implementation of non-invasive predictive systems that support the physician in
the therapeutic choice. The idea of the authors is to create a form of oncological point of care on an
embedded platform that allows physicians to always have a support tool in choosing the best therapy
to suggest to the patient.
Tipologia CRIS:
14.a.1 Articolo su rivista
Keywords:
radiomics, STM32, deep learning, immunotherapy
Elenco autori:
Rundo, F; Spampinato, C; Banna, ; Conoci, S
Link alla scheda completa:
Link al Full Text:
Pubblicato in: