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Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery

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TLDR
A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgery.
Abstract
Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery A cohort of 295 patients was selected Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients The endpoint of this study was DFS Both computed tomography (CT) and fluorodeoxyglucose positron emission tomography (PET) images generated from the PET/CT scanner were analysed Textural features were calculated using the LifeX package Statistical analysis was performed using the R platform The datasets were separated into two cohorts by random selection to perform training and validation of the statistical models Predictors were fed into a multivariate Cox proportional hazard regression model and the receiver operating characteristic (ROC) curve as well as the corresponding area under the curve (AUC) were computed for each model built The Cox models that included radiomic features for the CT, the PET, and the PET+CT images resulted in an AUC of 075 (95%CI: 065–085), 068 (95%CI: 057–080), and 068 (95%CI: 058–074), respectively The addition of clinical predictors to the Cox models resulted in an AUC of 061 (95%CI: 051–069), 064 (95%CI: 053–075), and 065 (95%CI: 050–072) for the CT, the PET, and the PET+CT images, respectively A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgery

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Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

TL;DR: The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.
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Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions

TL;DR: PET radiomics features were able to differentiate between primary and metastatic lung lesions and showed the potential to identify primary lung cancer subtypes.
References
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Journal ArticleDOI

Radiomics: extracting more information from medical images using advanced feature analysis.

TL;DR: Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
Journal ArticleDOI

Non-Small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and Survivorship

TL;DR: The introduction of angiogenesis, epidermal growth factor receptor inhibitors, and other new anti-cancer agents is changing the present and future of this disease and will certainly increase the number of lung cancer survivors.
Journal ArticleDOI

The Eighth Edition Lung Cancer Stage Classification

TL;DR: This paper summarizes the eighth edition of lung cancer stage classification, which is the worldwide standard as of January 1, 2017, based on a large global database, a sophisticated analysis, extensive internal validation as well as multiple assessments confirming generalizability.
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