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      Imaging-Based Prediction of Molecular Therapy Targets in NSCLC by Radiogenomics and AI Approaches: A Systematic Review

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          Abstract

          The objective of this systematic review was to analyze the current state of the art of imaging-derived biomarkers predictive of genetic alterations and immunotherapy targets in lung cancer. We included original research studies reporting the development and validation of imaging feature-based models. The overall quality, the standard of reporting and the advancements towards clinical practice were assessed. Eighteen out of the 24 selected articles were classified as “high-quality” studies according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The 18 “high-quality papers” adhered to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) with a mean of 62.9%. The majority of “high-quality” studies (16/18) were classified as phase II. The most commonly used imaging predictors were radiomic features, followed by visual qualitative computed tomography (CT) features, convolutional neural network-based approaches and positron emission tomography (PET) parameters, all used alone or combined with clinicopathologic features. The majority (14/18) were focused on the prediction of epidermal growth factor receptor (EGFR) mutation. Thirty-five imaging-based models were built to predict the EGFR status. The model’s performances ranged from weak ( n = 5) to acceptable ( n = 11), to excellent ( n = 18) and outstanding ( n = 1) in the validation set. Positive outcomes were also reported for the prediction of ALK rearrangement, ALK/ROS1/RET fusions and programmed cell death ligand 1 (PD-L1) expression. Despite the promising results in terms of predictive performance, image-based models, suffering from methodological bias, require further validation before replacing traditional molecular pathology testing.

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          Most cited references59

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          First-line ceritinib versus platinum-based chemotherapy in advanced ALK -rearranged non-small-cell lung cancer (ASCEND-4): a randomised, open-label, phase 3 study

          The efficacy of ceritinib in patients with untreated anaplastic lymphoma kinase (ALK)-rearranged non-small-cell lung cancer (NSCLC) is not known. We assessed the efficacy and safety of ceritinib versus platinum-based chemotherapy in these patients.
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            Alectinib versus crizotinib in patients with ALK-positive non-small-cell lung cancer (J-ALEX): an open-label, randomised phase 3 trial.

            Alectinib, a potent, highly selective, CNS-active inhibitor of anaplastic lymphoma kinase (ALK), showed promising efficacy and tolerability in the single-arm phase 1/2 AF-001JP trial in Japanese patients with ALK-positive non-small-cell lung cancer. Given those promising results, we did a phase 3 trial to directly compare the efficacy and safety of alectinib and crizotinib.
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              Brigatinib versus Crizotinib in ALK-Positive Non–Small-Cell Lung Cancer

              Brigatinib, a next-generation anaplastic lymphoma kinase (ALK) inhibitor, has robust efficacy in patients with ALK-positive non-small-cell lung cancer (NSCLC) that is refractory to crizotinib. The efficacy of brigatinib, as compared with crizotinib, in patients with advanced ALK-positive NSCLC who have not previously received an ALK inhibitor is unclear.
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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                30 May 2020
                June 2020
                : 10
                : 6
                : 359
                Affiliations
                [1 ]Humanitas University, Pieve Emanuele, 20090 Milan, Italy; gaia.ninatti@ 123456st.hunimed.eu (G.N.); arturo.chiti@ 123456hunimed.eu (A.C.)
                [2 ]Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; margarita.kirienko@ 123456icloud.com
                [3 ]Department of Translational Research, Diagnostic Radiology 3, University of Pisa, 56126 Pisa, Italy; emanuele.neri@ 123456med.unipi.it
                [4 ]Humanitas Clinical and Research Center-IRCCS, Rozzano, 20089 Milan, Italy
                Author notes
                Author information
                https://orcid.org/0000-0001-8207-1256
                https://orcid.org/0000-0002-3923-1151
                https://orcid.org/0000-0001-7950-4559
                https://orcid.org/0000-0003-2214-6492
                https://orcid.org/0000-0002-5806-1856
                Article
                diagnostics-10-00359
                10.3390/diagnostics10060359
                7345054
                32486314
                b0b0c2d5-7c32-407f-9a82-84fb14f0dde6
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 04 May 2020
                : 29 May 2020
                Categories
                Review

                radiogenomics,ct,pet/ct,lung cancer,egfr,alk,pd-l1,artificial intelligence,radiomics,targeted therapy

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