High-resolution Computed Tomography Features Distinguishing Benign and Malignant Lesions Manifesting as Persistent Solitary Subsolid Nodules

Yang, Weiguang; Sun, Yifeng; Fang, Wentao; Qian, Fangfei; Ye, Jianding; Chen, Qunhui; Jiang, Yifeng; Yu, Kejing; Han, Baohui

doi:10.1016/j.cllc.2017.05.023

Cited by 42 publications

(70 citation statements)

References 32 publications

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“…However, in our study, on baseline CT examinations, other features such as size, attenuation, and borders were unable to distinguish between benign and malignant SSN; these features were only effective on final follow up chest CT. These contradictory observations may be related to different patient and nodule subtypes in our study compared to Yang et al [24], although it is likely that our study employed quantitative radiomics versus subjective assessment used in the prior study.…”

Section: Discussionmentioning

confidence: 63%

“…Yang et al have reported that morphologic features such as lesion size, borders, and spiculated margins can help differentiate benign and malignant PGGN [18]. Their study of 1934 subsolid nodules (including 94 benign and 1840 malignant) reported that larger size, well-defined borders, and spiculated margins favor malignant over benign etiology for subsolid nodules [24]. We also found that a shape radiomic feature (surface: volume ratio) helps in the characterization of subsolid nodules on the baseline and follow up chest CT.…”

Section: Discussionmentioning

confidence: 99%

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Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

et al. 2019

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Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Materials and methods The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. Results Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT ( P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). Conclusions Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.

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Section: Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

et al. 2019

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“…In addition, the differential diagnosis of solitary solid pulmonary nodules has proven to be more difficult than that of sub-solid nodules. Studies of patients who have received surgical resections have shown that more than 90% of sub-solid nodules can be malignant [ 35 ], while the malignancy rate of solid nodules ranges from 53 to 75% [ 36 , 37 ]. This highlights the necessity of differentiating the nature of solid pulmonary nodules in an accurate and timely manner.…”

Section: Discussionmentioning

confidence: 99%

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules

et al. 2020

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Purpose: To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). Materials and methods: The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). Results: Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful.

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“…Nodule volume (log) is the common predictor to Model 1 and Model 2. Nodule size, either assessed with the nodule maximum diameter or with the nodule volume, has been identified as a significant predictor of invasiveness in several studies that aimed at a binary classification of lung adenocarcinoma invasiveness 5 , 6 , 11 , 13 . In our study, it also only modestly correlated with the best CT attenuation predictors (R: 0.30).…”

Section: Discussionmentioning

confidence: 99%

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

Margerie-Mellon

Gill

Salazar³

et al. 2020

Sci Rep

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The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.

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High-resolution Computed Tomography Features Distinguishing Benign and Malignant Lesions Manifesting as Persistent Solitary Subsolid Nodules

Cited by 42 publications

References 32 publications

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

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