Dataset Information

Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

ABSTRACT:

Objectives

We aimed to analyze the CT findings of ground-glass opacity nodules diagnosed pathologically as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma in order to investigate whether quantitative CT parameters enable distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

Methods

We reviewed CT images and pathologic specimens from 191 resected ground-glass opacity nodules with little or no solid component at CT. Nodule size, volume, density, mass, skewness/kurtosis, and CT attenuation values at the 2.5th-97.5th percentiles on histogram, and texture parameters (uniformity and entropy) were assessed from CT datasets.

Results

Of 191 tumors, 38 were AISs (20%), 61 were MIAs (32%), and 92 (48%) were invasive adenocarcinomas. Multivariate logistic regression analysis helped identify the 75th percentile CT attenuation value (P = 0.04) and entropy (P<0.01) as independent predictors for invasive adenocarcinoma, with an area under the receiver operating characteristic curve of 0.780.

Conclusion

Quantitative analysis of preoperative CT imaging metrics can help distinguish invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

SUBMITTER: Son JY

PROVIDER: S-EPMC4125172 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

Son Ji Ye JY Lee Ho Yun HY Lee Kyung Soo KS Kim Jae-Hun JH Han Joungho J Jeong Ji Yun JY Kwon O Jung OJ Shim Young Mog YM

PloS one 20140807 8

<h4>Objectives</h4>We aimed to analyze the CT findings of ground-glass opacity nodules diagnosed pathologically as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma in order to investigate whether quantitative CT parameters enable distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.<h4>Methods</h4>We reviewed CT images and pathologic specimens from 191 resected ground-glass opacity nodules with little or n ...[more]

PMID: 25102064

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Project description:BackgroundPure ground-glass nodules (pGGNs) with pleural contact (P-pGGNs) comprise not only invasive adenocarcinoma (IAC), but also minimally invasive adenocarcinoma (MIA). Radiomics recognizes complex patterns in imaging data by extracting high-throughput features of intra-tumor heterogeneity in a non-invasive manner. In this study, we sought to develop and validate a radiomics signature to identify IAC and MIA presented as P-pGGNs.MethodsIn total, 100 patients with P-pGGNs (69 training samples and 31 testing samples) were retrospectively enrolled from December 2012 to May 2018. Imaging and clinical findings were also analyzed. In total, 106 radiomics features were extracted from the 3D region of interest (ROI) using computed tomography (CT) imaging. Univariate analyses were used to identify independent risk factors for IAC. The least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation was used to generate predictive features to build a radiomics signature. Receiver-operator characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy of the radiomics signature. Decision curve analyses (DCA) were also conducted to evaluate whether the radiomics signature was sufficiently robust for clinical practice.ResultsUnivariate analysis showed significant differences between MIA (N = 47) and IAC (N = 53) groups in terms of patient age, lobulation signs, spiculate margins, tumor size, CT values and relative CT values (all P < 0.05). ROC curve analysis showed, when MIA was identified from IAC, that the critical value of tumor length diameter (TLD) was1.39 cm and the area under the ROC curve (AUC) was 0.724 (sensitivity = 0.792, specificity = 0.553). The critical CT value on the largest axial plane (CT-LAP) was - 597.45 HU, and the AUC was 0.666 (sensitivity = 0.698, specificity= 0.638). The radiomics signature consisted of seven features and exhibited a good discriminative performance between IAC and MIA, with an AUC of 0.892 (sensitivity = 0.811, specificity 0.719), and 0.862 (sensitivity = 0.625, specificity = 0.800) in training and testing samples, respectively.ConclusionsOur radiomics signature exhibited good discriminative performance in differentiating IAC from MIA in P-pGGNs, and may offer a crucial reference point for follow-up and selective surgical management.

Project description:ObjectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsThis study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.ResultsFive radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.ConclusionsThe proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs.Key points• CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

Project description:PurposeTo develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.Materials and methodsThis retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.ResultsFive radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.ConclusionThis study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

Project description:Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra- and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or peri-nodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Conclusions: The gross radiomic signature of intra- and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.

Project description:Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723-0.924), 0.833 (95% CI, 0.733-0.934), and 0.848 (95% CI, 0.750-0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.

Dataset Information

Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

Objectives

Methods

Results

Conclusion

Publications

Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

Similar Datasets

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