A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules

Cai, Jing; Liu, H.; Yuan, Huanchu; Wu, Yali; Xu, Qing; Yan, Ling; Li, J.; Fu, Jingyan; Ye, J.

doi:10.1016/j.crad.2020.10.005

Cited by 14 publications

(12 citation statements)

References 32 publications

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“…Several studies (24,25) have been conducted and have developed models based on radiomics parameters to predict the histological invasiveness of pGGNs, which demonstrated satisfactory performance. However, we aimed to provide some initial insights based on protocol G scanning that can predict the histological invasiveness of pGGNs in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies (22,23) have demonstrated that subsolid nodules classification (pGGNs; heterogeneous GGNs; part-solid nodules) have a significant linear trend with the histological invasiveness degree of the pulmonary adenocarcinoma spectrum. Moreover, radiomics features assessments have also been applied to discriminate histological invasiveness in pGGNs (24,25). However, it is extremely challenging to differentiate these two groups owing to varying criteria and image quality.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the histological invasiveness of pulmonary adenocarcinoma manifesting as persistent pure ground-glass nodules by ultra-high-resolution CT target scanning in the lateral or oblique body position

Ren¹,

Liu²,

Xu³

et al. 2021

Quant Imaging Med Surg

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Background: Ultra-high-resolution computed tomography (U-HRCT) has improved image quality for displaying the detailed characteristics of disease states and lung anatomy. The purpose of this study was to retrospectively examine whether U-HRCT target scanning in the lateral or oblique body position (protocol G scan) could predict histological invasiveness of pulmonary adenocarcinoma manifesting as pure groundglass nodules (pGGNs). Methods: From January 2015 to December 2016, 260 patients with 306 pathologically confirmed pGGNs who underwent preoperative protocol G scans were retrospectively reviewed and analyzed. The U-HRCT findings of preinvasive lesions [atypical adenomatous hyperplasias (AAH) and adenocarcinomas in situ (AIS)] and invasive pulmonary adenocarcinomas [minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC)] were manually compared and analyzed using orthogonal multiplanar reformation (MPR) images. The logistic regression model was established to determine variables that could predict the invasiveness of pGGNs. Receiver operating characteristic (ROC) curve analysis was performed to evaluate their diagnostic performance. Results: There were 213 preinvasive lesions (59 AAHs and 154 AISs) and 93 invasive pulmonary adenocarcinomas (53 MIAs and 40 IACs). Compared with the preinvasive lesions, invasive adenocarcinomas exhibited a larger diameter (13.5 vs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the histological invasiveness of pulmonary adenocarcinoma manifesting as persistent pure ground-glass nodules by ultra-high-resolution CT target scanning in the lateral or oblique body position

Ren¹,

Liu²,

Xu³

et al. 2021

Quant Imaging Med Surg

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“…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%

“…No studies performed the phantom research. Nine studies [18,20,22,24,25,30,33,34,39] acquired images using the same CT scanner, thus avoiding the scanner differences. Wu et al used ComBat to correct the radiomics features for the batch effect introduced by different scanners [15].…”

Section: Image Acquisition and Preprocessingmentioning

confidence: 99%

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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“…The Darwin research platform (https://arxiv.org/abs/2009.00908) was used to perform radiomic features extraction and selection, and to build machine-learning models. 22 Volumes of interest (VOIs) were drawn manually by a neuroradiologist (Z.S.H., with 5 years of experience) and all contours were reviewed by a senior neuroradiologist (Y.K.W., with 20 years of experience). Axial T 1 -w, T 2 -w, and CET 1 -w sequence images were used for VOIs segmentation, including areas of cystic changes.…”

Section: Tumor Segmentationmentioning

confidence: 99%

Machine Learning‐Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma

Huang

Xiao

et al. 2021

Magnetic Resonance Imaging

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Background: Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. Purpose: To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. Study Type: Retrospective. Population: A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32). Field Strength/Sequence: Axial T 1 -weighted (T 1 -w), T 2 -weighted (T 2 -w), contrast-enhanced T 1 -weighted (CET 1 -w) on 3.0 T or 1.5 T magnetic resonance scanners. Assessment: Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. Statistical Tests: The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. Results: Seven texture features, three from T 1 -w, two from T 2 -w, and two from CET 1 -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. Data Conclusion: The model based on radiomic features from T 1 -w, T 2 -w, and CET 1 -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. Level of Evidence: 4 Technical Efficacy: 2

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A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules

Cited by 14 publications

References 32 publications

Predicting the histological invasiveness of pulmonary adenocarcinoma manifesting as persistent pure ground-glass nodules by ultra-high-resolution CT target scanning in the lateral or oblique body position

Predicting the histological invasiveness of pulmonary adenocarcinoma manifesting as persistent pure ground-glass nodules by ultra-high-resolution CT target scanning in the lateral or oblique body position

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

Machine Learning‐Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma

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