Keywords:
Tissue characterisation, Cancer, Segmentation, Decision analysis, Computer Applications-Detection, diagnosis, Image manipulation / Reconstruction, CT-Quantitative, CT, Thorax, Oncology, Computer applications
Authors:
G. Ficarra1, E. Barabino1, C. Genova1, S. Mennella1, M. Verda2, G. Pittaluga1, F. Grossi1, G. Cittadini1; 1Genova/IT, 2Imperia/IT
Purpose
Lung cancer is the leading cause of cancer-related mortality in the United States1 and among the most common cancers worldwide.
Non–small cell lung cancer (NSCLC) comprises approximately 85% of all lung cancer cases2 and among patients with NSCLC approximately 40% present with stage IV disease3.
Although targeted therapy has been associated with a significant benefit in patients presenting gene abnormalities,
those abnormalities are rare and thus chemotherapy remains the mainstay of treatment for most patients4.
The ability to avoid the immune system is one of the hallmarks of cancer5 and there are many complex interactions under investigation; the most studied is the link between the lymphocytes membrane receptor,
Program Cell Death 1 (PD-1),
and its ligands (PDL1 or PDL2) which are expressed by some tumor cells6,
inhibiting lymphocyte activity.
As a consequence many PD-1 and PD-L1 inhibitors have reached late phase development in lung cancer7-11 and several immunotherapic agents have been approved by FDA in record time due to strong clinical benefits and manageable side effects7-12.
These results have rapidly reset the management of advanced non-small cell lung cancer (NSCLC).
As there are no reliable clinical or biological markers of activity during treatment with immune checkpoint inhibitors,
functional and anatomical imaging plays a leading role in decision-making13; nevertheless response patterns of tumors treated with immunotherapy are temporally and morphologically heterogeneous and may differ with those seen with conventional chemotherapeutic agents or targeted therapies,
thus an accurate assessment of the response can be challenging.
Conventional dimensional radiological response criteria,
the Response Evaluation Criteria in Solid Tumors (RECIST 1.114),
do not detect pseudoprogression (PsPD) and may underestimate the therapeutic benefit of immune checkpoint blockade.
Further criteria specifically developed for immunotherapy have been developed to better define the tumor response,
the immune-related Response Criteria (irRC15).
A simplification of these criteria,
defined as irRECIST (immune-related RECIST16,17),
was later proposed; more recently,
the RECIST working group published a proposition of new criteria (iRECIST18),
to standardize response assessment among immunotherapy clinical trials.
In the last years,
interest has grown in imaging techniques that could provide an “in-vivo” tissutal lesion characterization and eventually predictive information.
Morphological and functional image-derived characteristics of a tissue can be described by a multitude of mathematical features and this constitutes the “texture analysis” approach.
The aim of our study was to define if CT Texture Analysis (CTTA) derived parameters might add new insights in detecting tumor response in patients receiving immune checkpoint inhibitors for advanced NSCLC.