Radiomics in Lung Metastases: A Systematic Review

Gabelloni, Michela; Faggioni, Lorenzo; Fusco, Roberta; Simonetti, Igino; Muzio, Federica De; Giacobbe, Giuliana; Borgheresi, Alessandra; Bruno, Federico; Cozzi, Diletta; Grassi, Francesca; Scaglione, Mariano; Giovagnoni, Andrea; Barile, Antonio; Miele, Vittorio; Gandolfo, Nicoletta; Granata, Vincenza

doi:10.3390/jpm13020225

Cited by 7 publications

(4 citation statements)

References 127 publications

(140 reference statements)

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“…Furthermore, our evaluation framework using bootstrap-bias-corrected 10-times-repeated 20-fold crossvalidation accounts for the variance introduced by an arbitrary single train/test split in small sample sizes [18,27] and measures both calibration and discrimination [27]. Other authors applied radiomics to primary pulmonary neoplasms and metastases in CECT and PET/CT [28][29][30][31][32][33]. Predicted outcomes were composed of the severity of pulmonary lesions, epidermal growth factor receptor status, or survival outcomes [28][29][30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

“…Other authors applied radiomics to primary pulmonary neoplasms and metastases in CECT and PET/CT [28][29][30][31][32][33]. Predicted outcomes were composed of the severity of pulmonary lesions, epidermal growth factor receptor status, or survival outcomes [28][29][30][31][32][33]. In contrast, we focus on the per-node dignity of thoracic lymph nodes, where rather little research has been conducted [23,24].…”

Section: Discussionmentioning

confidence: 99%

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Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Laqua

Woźnicki

Bley

et al. 2023

Cancers

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Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional “hand-crafted” radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865–0.878), SBS 35.8 (34.2–37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Laqua

Woźnicki

Bley

et al. 2023

Cancers

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“…Studies have indicated that radiomics has the potential to improve or surpass existing tumor diagnosis methods, as it can effectively evaluate genomic features [24,25], tumor subtypes [26][27][28], and lymph node metastasis [29][30][31][32] in various cancers. Radiomics has demonstrated the ability to predict the prognosis of various malignancies, including colorectal cancer [33][34][35][36][37][38], gastric cancer [39][40][41][42][43][44], cervical cancer [31,[45][46][47][48], pancreatic cancer [49][50][51], nasopharyngeal cancer [52][53][54][55][56][57], breast cancer [58][59][60][61][62], and lung cancer [63][64][65][66][67][68]. To conduct a comprehensive literature review, we employed the PubMed search engine in combination with an online litera...…”

Section: Introductionmentioning

confidence: 99%

Radiomics and Its Feature Selection: A Review

Zhang,

Guo,

Jin

2023

Symmetry

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Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying pathologies, genetic information, and prognostic indicators. The integration of radiomics with artificial intelligence presents innovative avenues for cancer diagnosis, prognosis evaluation, and therapeutic choices. In the context of oncology, radiomics offers significant potential. Feature selection emerges as a pivotal step, enhancing the clinical utility and precision of radiomics. It achieves this by purging superfluous and unrelated features, thereby augmenting model performance and generalizability. The goal of this review is to assess the fundamental radiomics process and the progress of feature selection methods, explore their applications and challenges in cancer research, and provide theoretical and methodological support for future investigations. Through an extensive literature survey, articles pertinent to radiomics and feature selection were garnered, synthesized, and appraised. The paper provides detailed descriptions of how radiomics is applied and challenged in different cancer types and their various stages. The review also offers comparative insights into various feature selection strategies, including filtering, packing, and embedding methodologies. Conclusively, the paper broaches the limitations and prospective trajectories of radiomics.

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“…In particular, texture analysis (TA) has been increasingly applied to radiological imaging for diagnosing, characterizing, and monitoring treatment response by quantifying tumor heterogeneity and irregularity of tissue components [12,13]. Tumors with high heterogeneity have been shown to have worse prognosis, potentially reflecting intrinsic biological aggressiveness or treatment resistance [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors

Rengo

Onori

Bellini

et al. 2023

JPM

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Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST’s prognosis as determined by the Miettinen classification. Methods: patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. Results: 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. Conclusions: the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.

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Radiomics in Lung Metastases: A Systematic Review

Cited by 7 publications

References 127 publications

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer

Radiomics and Its Feature Selection: A Review

Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors

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