HRCT texture analysis for pure or part-solid ground-glass nodules: distinguishability of adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma

Yagi, Takuya; Yamazaki, Mitsuhiro; Ohashi, Riuko; Ogawa, Rei; Ishikawa, Hiroyuki; Yoshimura, Norihiko; Tsuchida, Masanori; Ajioka, Yoichi; Aoyama, Hidefumi

doi:10.1007/s11604-017-0711-2

Cited by 27 publications

(22 citation statements)

References 21 publications

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“…However, the majority of the studies were performed with slice thickness comparable to the American College of Radiology (ACR) LDCT protocol recommendations for lung cancer screening and the comparison of patient characteristics and acquisition parameters did not reveal significant differences between SSN classes 10,11 . The overall number of nodules is relatively small but comparable and larger than many other studies 14–16,21,22,24,26,27 . Predictive classification performance was thus established in a limited number of cases for both training and testing.…”

Section: Discussionmentioning

confidence: 44%

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Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules

Oikonomou

Salazar²,

Zhang

et al. 2019

Sci Rep

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109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.

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Section: Discussionmentioning

confidence: 44%

“…Yagi T et al . reported that the 90 th percentile together with entropy were independent differentiators of PIA from AIS-MIA with an area under the curve 0.90 24 . Using radiomics with 57 morphologic and texture-based features and Support Vector Machines (SVM), Li M et al .…”

Section: Discussionmentioning

confidence: 98%

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules

Oikonomou

Salazar²,

Zhang

et al. 2019

Sci Rep

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“…Chen et al picked 76 features meaningful for the distinction of malignancy of pulmonary nodules from 750 extracted radiomic features and built a predictive model whose accuracy was up to 84% using four selected advanced features ( 20 ). Yagi et al carried out the texture analysis of high-resolution computed tomography (HRCT) and found that 90th percentile and entropy performed well in discrimination between AIS/MIA and IVA with an AUC value of 0.90 (95% CI: 0.84–0.95) ( 13 ). Three different predictive models set with clinical, radiological, and nine selected radiomic features from 960 features extracted from unenhanced CT images in our study all presented good predictive power in the discrimination between AIS/MIA (indolent adenocarcinoma) and IVA (AUC > 0.8, P < 0.05).…”

Section: Discussionmentioning

confidence: 99%

“…Compared with IVA, AIS, and MIA are considered as indolent lung adenocarcinoma because of the excellent prognosis ( 11 , 12 ). AIS/MIA could be followed up or treated with sublobar resection while more aggressive surgical interventions should be taken for IVA ( 13 , 14 ). Several previous studies revealed that the 5-year survival rates of AIS and MIA could be 100% and near 100% with a complete resection while that of IVA in stage Ia is no more than 75% ( 11 , 13 , 14 ).…”

Section: Introductionmentioning

confidence: 99%

“…AIS/MIA could be followed up or treated with sublobar resection while more aggressive surgical interventions should be taken for IVA ( 13 , 14 ). Several previous studies revealed that the 5-year survival rates of AIS and MIA could be 100% and near 100% with a complete resection while that of IVA in stage Ia is no more than 75% ( 11 , 13 , 14 ). Thus, it might provide some guidance for clinical therapeutic decision-making if pGGN-like IVA could be figured out on preoperative CT images.…”

Section: Introductionmentioning

confidence: 99%

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Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

Zhu

Shen

et al. 2020

Front. Oncol.

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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 mode...

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The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules

Gao

et al. 2021

Eur Radiol

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HRCT texture analysis for pure or part-solid ground-glass nodules: distinguishability of adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma

Abstract: The 90th percentile CT numbers and entropy can accurately distinguish AIS-MIA from IAC.

Cited by 27 publications

References 21 publications

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules

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