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Open AccessReview

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

1
Humanitas University, Pieve Emanuele, 20090 Milan, Italy
2
Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
3
Department of Translational Research, Diagnostic Radiology 3, University of Pisa, 56126 Pisa, Italy
4
Humanitas Clinical and Research Center-IRCCS, Rozzano, 20089 Milan, Italy
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(6), 359; https://doi.org/10.3390/diagnostics10060359
Received: 4 May 2020 / Revised: 28 May 2020 / Accepted: 29 May 2020 / Published: 30 May 2020
(This article belongs to the Collection Feature Papers)
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. View Full-Text
Keywords: radiogenomics; CT; PET/CT; lung cancer; EGFR; ALK; PD-L1; artificial intelligence; radiomics; targeted therapy radiogenomics; CT; PET/CT; lung cancer; EGFR; ALK; PD-L1; artificial intelligence; radiomics; targeted therapy
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MDPI and ACS Style

Ninatti, G.; Kirienko, M.; Neri, E.; Sollini, M.; Chiti, A. Imaging-Based Prediction of Molecular Therapy Targets in NSCLC by Radiogenomics and AI Approaches: A Systematic Review. Diagnostics 2020, 10, 359.

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