Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest

Abstract

Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

“…Different ensemble ML using CT radiomics features were investigated for prediction of overall survival in patients with Non-Small Cell Lung Cancer [11]. Interestingly, the accuracy improved when using the clinical target volume, confirming the hypothesis that, in the surroundings of the visible tumor, there is useful information to predict patients' outcome [6]. A retrospective study recently started in the framework of Artificial Intelligence in Medicine (AIM), a project founded by INFN, aims to investigate imaging and dose biomarkers for clinical outcome of pediatric patients affected by medulloblastoma who underwent Cranio-Spinal Irradiation (CSI) using Helical Tomotherapy [163].…”

“…Machine learning (ML) is a field of AI algorithms (Fig. 1) which can recognize patterns in medical images by analyzing voxel intensity values or quantitative imaging features, called also "radiomic features", by identifying their best combination and building a model for classification or regression [3][4][5][6]. By ML, image features can also be combined with variables from other sources, such as dose distribution from the radiotherapy treatment [7]) or clinical variables [8] to improve accuracy of classification.…”

Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review

“…A total of 433 studies were identified, in which 432 studies were identified by the comprehensive literature search and one study was identified by a hand search of the relevant literature. After screening and evaluating, 13 studies with 2942 patients meeting the criteria were included in this systematic review [22,23,[27][28][29][30][31][32][33][34][35][36][37].…”

“…The included studies were published from 2017 to 2022. Almost all the studies (12/13, 92%) were retrospectively designed [22,23,[27][28][29][30][31][32][33][34][35][36], except one of the studies, which was prospective [37]. Patients from seven studies (7/13, 54%) were from one center [22, 28-30, 34, 36, 37], and the others (6/13, 46%) were from multiple center [23,27,[31][32][33]35].…”

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

“…Radiomics leverages on artificial intelligence techniques and the increasing availability of large, open-access and multicentric datasets of pre-classified cases to infer clinical information about unknown ones (‘population imaging’ approach [ 13 ]). Several studies have underlined the potential benefit of radiomics for clinical problem-solving in lung cancer, such as prediction of malignancy [ 14 , 15 , 16 ], histological subtype [ 17 , 18 , 19 ], prognosis [ 20 , 21 , 22 ] and response to treatment [ 23 , 24 , 25 ] (see also Figure 1 for an overview of potential applications).…”