The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

Abstract

PurposeLung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients’ care.MethodsA literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020ResultsWe identified several studies at each point of patient’s care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.ConclusionPromising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

“…However, it is necessary to point out that all SR sections are independent from each other, so that the Patient Clinical Data and Clinical Evaluation sections are optional and may be filled in or not upon user choice, although they were conceived with the aim of creating databases. In fact, the possibility of collecting all these data could allow the creation of a large database, not only for epidemiological studies, but also in the highest conception of radiology, to lay the foundations for radiomics studies [34][35][36][37]. Radiology reports should be rich in data that could potentially be pooled, analyzed and correlated with patient outcomes, thereby assisting future clinical and imaging guidelines.…”

Low‐dose computed tomography lung cancer screening: Clinical evidence and implementation research

“…These data can, for example, include variables relating to shape, voxel gray level intensity, and spatial relationships. Thus, the aim of radiomics in LCS is to develop new imaging biomarkers that could help differentiate between malignant and benign nodules [66]. Liu et al.…”

“…These data can, for example, include variables relating to shape, voxel gray level intensity, and spatial relationships. Thus, the aim of radiomics in LCS is to develop new imaging biomarkers that could help differentiate between malignant and benign nodules [66]. Liu et al used radiomic models to differentiate between adenocarcinomas and benign lesions detected using LDCT, and found a higher specificity and equivalent sensitivity when compared to the Lung-RADS classification system [67].…”

Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study

“…In recent years computerised analysis of imaging data (particularly from CT and PET/CT) has shown great promises to improve the management of patients with lung cancer [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. The rationale behind this paradigm is that the quantitative extraction of imaging parameters from suspicious lesions—particularly shape and texture features—may reveal hidden patterns that would otherwise go unnoticed to the naked eye [ 11 , 12 ].…”