Tumori Journal Abstract Book - October 2020 - 17

A - Thoracic Cancers	
1

Radioterapia Oncologica Università Campus Bio-Medico, Roma;
Radioterapia Oncologica, Università Campus Bio-Medico, Roma; 3Centro
Diagnostico Italiano Spa, Milano; 4Sistemi di Elaborazione ed Informatica,
Facoltà di Ingegneria Biomedica, Università Campus Bio-Medico, Roma;
5
Facoltà di Ingegneria, Università Campus Bio-Medico, Roma; 6Sistemi di
Elaborazione ed Informatica, Facoltà di Ingegneria Biomedica, Università
Campus Bio-Medico, Roma; 7Radioterapia Oncologica, Università Campus
Bio-Medico, Roma
2

Background: This study aims to predict the Overall
Survival (OS) of patients with lung cancer who underwent
a chemoradiotherapy treatment using a radiomic approach.
Moreover, we evaluate how an accurate segmentation
impacts the results using three incremental segmentations
of the same lesion.
Material and methods: For this retrospective study 97
CT images of patients with NSCLC lung cancer treated
with chemoradiotherapy were included. Images were
acquired with a Siemens Somatom Emotion, with 140
Kv, 80 mAs, and 3mm slice thickness. Patients were
divided into two classes according to their OS (53 patients
died, 44 survived after median follow-up of 18.55
months. For each patient three segmentations were considered: the Gross Tumor Volume (GTV), the Clinical
Target Volume (CTV) and the Planning Target Volume
(PTV). All volumes were manually segmented by a radiation oncologist who included in the GTV all the macroscopic disease seen at CT, in the CTV the GTV plus a
margin for sub-clinical disease and in the PTV, the CTV
plus of a 0.5 cm safety margin. Totally 242 radiomic features: First order, 3D Gray Level Co-occurrence Matrix
and Three Orthogonal Planes-Local Binary Patterns,
were extracted with an inhouse developed MATLAB
code. For features selection, we ran a wrapper based
approach using three machine learning (ML) algorithms;
AdaBoost (Ada), Decision Tree (DT) and Random
Forest(RF). In the classification stage, the same ML algorithms were used.
Results: Totally 9 different experiment were performed,
combining three ML algorithms and three segmentations
for each patient. Performance was evaluated through accuracy and AUC. We obtained the following results: AdaGTV:70.1%,70.1%, Ada-CTV:83.5%,82.8%,Ada-PTV:
71.1%,70.1%, DT-GTV:70.1%,70.1%, DT-CTV:60.8%,
61.1%, DT-PTV:63.9%,64.1%, RF-GTV:73.2%,71.8%,
RF-CTV:78.4%,77.3%, RF-PTV:77.3%,76.6%.
Conclusions: Results reported an accuracy of 83.5% and
an AUC of 82.8% in predicting the OS for stage III
patients undergoing chemoradiation. The best result was
obtained with the intermediate segmentation, which
includes margin for sub-clinical disease and increases the
prediction accuracy. This result could be the first try to
change the knowledge about the segmentation step in
radiomics.

17

A24
Atezolizumab treatment of
NSCLC patients is associated with
antinuclear antibody increase
and occurrence of aberrant NK
cells associated to ADCC
Fameli A.1, Bianco G.1, Monoriti M.1, Calandruccio N.D.1, Iuliano E.1,
Oliva B.M.1, Giannicola R.1, Correale P.1
1

Grande Ospedale Metropolitano, Reggio Calabria

Background: Peripheral PD-1/PDL1-immunecheckpoint
blockade is an innovative treatment for NSCLC.
Atezolizumab, is a mAb to PDL1 which is expressed in
cancer cells and tumor infiltrating inflammatory cells that
may account for additional ADCC-activity. This mAb has
been recently approved for the treatment of metastaticNSCLC patients and few information is available concerning its effects on either hematochemical or lymphocyte
subsets change. We have therefore evaluated possible
change in these parameters.
Material (patients) and methods: This is a retrospective
observational study on 14 NSCLC patients (3 with squamous and 11 with non squamous histology). We evaluated
baseline and post treatment changes in inflammatory
markers (LDH, CRP, ESR), autoantibodies and then we
performed a flow cytometric study of peripheral lymphocyte subsets involving T, B and natural killer (NK) cells in
patients' PBMCs. NK subsets were studied in
CD56brightCD16+/-, CD56dimCD16bright and CD56-CD16+
NK cell compartments.
Results: Our monitoring revealed no change in inflammatory markers as well as WBC with the exception of neutrophils to lymphocytes ratio (Baseline vs. third cycle =
4.38±3.06 vs. 3.5±1.65; p=0.09) that showed a trend to
decline and of a significant increase in antinuclear-antibodies (ANA) (Baseline vs. third cycle = 28% vs.64%,
P<0.0005). Flow cytometric analysis did not show
changes in CD4+ and CD8+ T cells, even though a significant post-treatment decline in CD4+/CD8+ T cell
ratio was recorded (Baseline vs. third cycle = 2.22±1.24
vs. 2.02±1.20, p=0.01). Similarly, there was no significant
change in CD56bright, CD56dim NK cells with the exception
of the CD56-CD16+ subset that showed a significant posttreatment increase (Baseline vs. third cycle = 3.76±0.8
vs. 4.68±0.81; p<0.036).
Conclusions: Anti-PDL-1 blockade by atezolizumab is
associated to a significant increase in auto-antibodies and
CD56-CD16+ NKs a particular aberrant subset that usually is expanded in patients with chronic viral infections or
autoimmune disorders and which is associated with
NK-mediated ADCC. A phenomenon that could play an
additional therapeutic effect for this mAb. We believe that



Tumori Journal Abstract Book - October 2020

Table of Contents for the Digital Edition of Tumori Journal Abstract Book - October 2020

Contents
Tumori Journal Abstract Book - October 2020 - Cover1
Tumori Journal Abstract Book - October 2020 - Cover2
Tumori Journal Abstract Book - October 2020 - I
Tumori Journal Abstract Book - October 2020 - II
Tumori Journal Abstract Book - October 2020 - Contents
Tumori Journal Abstract Book - October 2020 - IV
Tumori Journal Abstract Book - October 2020 - V
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