2020
DOI: 10.1016/s2589-7500(20)30002-9
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CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction

Abstract: Background Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. MethodsWe did a retrospective mult… Show more

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Cited by 97 publications
(104 citation statements)
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“…It has been confirmed that image-based phenotypes can be used to quantify the spatial heterogeneity of tumors (7). Recently, some studies have indicated that radiomics signature generated by using radiomics features which are extracted from high-quality imaging data might be served as imaging markers to predict treatment outcome (8)(9)(10)(11).…”
Section: Introductionmentioning
confidence: 99%
“…It has been confirmed that image-based phenotypes can be used to quantify the spatial heterogeneity of tumors (7). Recently, some studies have indicated that radiomics signature generated by using radiomics features which are extracted from high-quality imaging data might be served as imaging markers to predict treatment outcome (8)(9)(10)(11).…”
Section: Introductionmentioning
confidence: 99%
“…Today, machine learning and the extraction of statistical features make it possible to predict mutations and micrometastases [1]. In addition to this sub-level acquired by machine learning and the extraction of statistical features, there is a diagnostic meta-level that allows conclusions regarding treatment response and survival due to interdisciplinary data integration [2,3]. Oncology is also undergoing a fundamental change in previous diagnostic-therapeutic procedures.…”
Section: Introductionmentioning
confidence: 99%
“…Radiomic features have shown prognostic and predicting response to multiple different treatments across a wide variety of cancers, including lung, [12][13][14][15][16][17] breast, 18-21 21 brain, [22][23][24][25][26] prostate 27 28 and colorectal 29 cancers. Specifically, in lung cancer, 9 radiomics approaches have been used to predict the benefit of adjuvant chemotherapy, prognosticate disease risk in early-stage lung cancer, 30 predict treatment response to concurrent chemoradiation in locally advanced disease 15 and to predict response to immune checkpoint inhibition in advanced NSCLC. 31 32 These features are sought to capture the extent of heterogeneity and other biologically relevant features, such as interaction with stromal or vascular components within the given region of interest.…”
Section: Introductionmentioning
confidence: 99%