Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

Meng, Fanyang; Guo, Yan; Li, Mingyang; Lu, Xinwu; Wang, Shuo; Zhang, Lei; Zhang, Huimao

doi:10.1016/j.tranon.2020.100936

Cited by 17 publications

(25 citation statements)

References 39 publications

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“…The AUC of included studies ranged from 0.78 to 0.97 in the training set and 0.77 to 0.98 in the validation set (supplementary Table 1). The combined model outperformed radiomics model in four studies [18,20,30,35].…”

Section: Performance Evaluationmentioning

confidence: 97%

“…The number of patients/nodules ranged between 34/34 and 794/886. Eight studies [15,[17][18][19][20][21][22][23] included only mixed ground-glass nodules (MGGNs), seven [24][25][26][27][28][29][30] only pure ground-glass nodules (PGGNs), and thirteen [14,16,[31][32][33][34][35][36][37][38][39][40][41] both of MGGNs and PGGNs.…”

Section: General Characteristics Of Included Studiesmentioning

confidence: 99%

“…The ROIs were segmented manually in 20 studies [14,15,18,21,22,[24][25][26][27]29,[31][32][33][34][36][37][38][39][40][41], semi-automatically in six studies [17,19,20,28,30,35] and automatically in two studies [16,23] (Table 2). Multiple segmentations, more than one person segmented the ROIs, were conducted in 24 studies.…”

Section: Regions Of Interest (Rois) Segmentationmentioning

confidence: 99%

“…Multiple segmentations, more than one person segmented the ROIs, were conducted in 24 studies. Eleven studies [14,15,17,19,24,28,29,31,35,37,39] reported that the persons who segmented the ROIs were blinded to the histopathological results of the tumors. ROIs in the study by Wu et al [15] included not only the gross tumor volume, but also the solid regions of the GGN and the peri-nodular regions.…”

Section: Regions Of Interest (Rois) Segmentationmentioning

confidence: 99%

“…Three studies [14][15][16] performed the model in a geographical validation set, and four [17,21,31,36] in a temporal validation set. Ten studies [18,20,22,24,27,28,30,35,38,39] randomly assigned all patients to the training set or the validation set in a certain ratio. Of the eleven studies that lacked validation sets, only one [19] employed internal crossvalidation.…”

Section: Performance Evaluationmentioning

confidence: 99%

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CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: A systematic review

Shi

Zhao

Peng

et al. 2021

European Journal of Radiology

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To provide an overview of the available studies investigating the use of computer tomography (CT) radiomics features for differentiating invasive adenocarcinomas (IAC) from indolent lung adenocarcinomas presenting as ground-glass nodules (GGNs), to identify the bias of the studies and to propose directions for future research. Method: PubMed, Embase, Web of Science Core Collection were searched for relevant studies. The studies differentiating IAC from indolent lung adenocarcinomas appearing as GGNs based on CT radiomics features were included. Basic information, patient information, CT-scanner information, technique information and performance information were extracted for each included study. The quality of each study was assessed using the Radiomic Quality Score (RQS) and the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: Twenty-eight studies were included with patients ranging from 34 to 794. All of them were retrospective. Patients in three studies were from multiple centers. Most studies segmented regions of interest manually. Pyradiomics and AK software were the most frequently used for features extraction. The number of radiomics features extracted varied from 7 to 10329. Logistic regression was the most frequently chosen model. Entropy was identified as radiomics signature in seven studies. The AUC of included studies ranged from 0.77 to 0.98 in 15 validation sets. The percentage RQS ranged from 3% to 50%. According to PROBAST, the overall risk of bias (ROB) was high in 89.3% (25/28) of included studies, unclear in 7.1% (2/28) of included studies, and low in 3.6% (1/28) of included studies. All studies were low concern regarding the applicability of primary studies to the review question. Conclusion: CT radiomics-based model is promising and encouraging in differentiating IAC from indolent lung adenocarcinomas, though they require methodological rigor. Well-designed studies are necessary to demonstrate their validity and standardization of methods and results can prompt their use in daily clinical practice.

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Section: Performance Evaluationmentioning

confidence: 97%