A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules

Yan, Ling; Ye, J; Yunxin, Yin,; Ling, Jun; Pan, Xiaojie

doi:10.1016/j.crad.2022.06.004

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Classical CT parameters are associated with GGN extension or invasion [19,20]. In this study, we found that the proportions of Daverage, Dsolid, and internal bronchial morphology were higher in adenocarcinoma than those in PGL, as in the previous studies [21,22].…”

Section: Discussionsupporting

confidence: 82%

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

Wang

Yue

Fan

et al. 2023

JCM

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Objectives: Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP. Material and Methods: Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram. Results: This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05). Conclusions: Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs’ invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.

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Section: Discussionsupporting

confidence: 82%

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

Wang

Yue

Fan

et al. 2023

JCM

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“…CT-derived parameters such as size and qualitative features, are correlated with the growth and in ltration of early-stage LA. 33 We found that internal vascular morphology and pleural indications were correlated with PD-L1 expression. Further univariate analysis suggested that pleural indentation was a crucial morphological feature of positive PD-L1 expression.…”

Section: Discussionmentioning

confidence: 67%

Predictive value of spectral dual-detector computed tomography for PD-L1 expression in stage I lung adenocarcinoma: Development and validation of a nomogram

Wang,

Fan,

Yue

et al. 2023

Preprint

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Background Programmed death ligand 1 (PD-L1) expression is a predictive biomarker for the efficacy of immune checkpoint inhibitors in treating early-stage lung adenocarcinoma (LA). Given that studies on the relationship between PD-L1 expression and spectral computed tomography (SDCT) quantification, qualitative parameters, and clinical biomarkers are limited, we aimed to explore this aspect for stage I LA and developed a nomogram to aid in preoperative individualized identification. Methods We analyzed SDCT parameters and PD-L1 expression in patients with invasive non-mucinous LA diagnosed by postoperative pathology. Based on the expression of PD-L1 (threshold, 1%), the patients were divided into positive and negative groups. Results Overall, 356 participants were included in the analysis. After univariate analysis, 18 parameters were associated with PD-L1 expression. Multiple regression analysis was performed on the significant parameters with AUC > 0.6, further, CT.value, CT40kev.a, Zeff.a, ED.a, ED.v, TK1, and Dsolid were found to be independent risk factors for PD-L1 expression in stage I LA. The clinical decision-making curve of the SDCT parameter-clinical nomogram had the highest net benefit over a wide range of threshold probabilities, and the clinical impact curve indicated its clinical validity. Model comparisons revealed the higher discriminatory accuracy of the nomogram than that of any individual variable. Conclusion Quantitative parameters based on SDCT can predict the expression of PD-L1 in early-stage LA, with Zeff.a playing a prominent role; our nomogram showed excellent predictive performance and good calibration. This approach may promote better non-invasive prediction of PD-L1 expression.

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“…Ma et al ( 9 ) reported a radiomics signature that could aid in distinguishing between preinvasive GGNs (AAH/AIS) and invasive GGNs (MIA/IAC), with the delta radiomics signature demonstrating a higher AUC than the radiomics signature in identifying invasive GGNs. Lv et al ( 10 ) observed that their delta radiomics model showed satisfactory diagnostic efficiency and superiority compared to the clinical model in distinguishing between invasive adenocarcinoma (IA) and preinvasive lesion (PIL)/MIA in GGN-like lung adenocarcinoma. However, its diagnostic efficiency was slightly lower than that of the radiomics or combined models, which seems to contradict the findings of Ma et al In our study, although the delt radiomics-clinical model and the general radiomics-clinical model both demonstrated excellent performance, the former included fewer features yet did not exhibit a performance advantage, which, to some extent, aligns with the findings of Lv et al ’s study.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies ( 6 , 7 ) have shown that delta radiomics can improve lung cancer detection rate in screening and pulmonary nodule malignancy prediction. There are also researches suggesting that delta radiomics is valuable in predicting the invasiveness of GGNs, but the results supporting this may be unstable due to the relatively limited number of studies ( 8 - 10 ).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study

Xue,

Zhu,

Tao

et al. 2024

Quant Imaging Med Surg

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Background Radiomics models based on computed tomography (CT) can be used to differentiate invasive ground-glass nodules (GGNs) in lung adenocarcinoma to help determine the optimal timing of GGN resection, improve the accuracy of prognostic prediction, and reduce unnecessary surgeries. However, general radiomics does not fully utilize follow-up data and often lacks model interpretation. Therefore, this study aimed to build an interpretable model based on delta radiomics to predict GGN invasiveness. Methods A retrospective analysis was conducted on a set of 303 GGNs that were surgically resected and confirmed as lung adenocarcinoma in Shanghai Chest Hospital between September 2017 and August 2022. Delta radiomics and general radiomics features were extracted from preoperative follow-up CT scans and combined with clinical features for modeling. The performance of the delta radiomics-clinical model was compared to that of the radiomics-clinical model. Additionally, Shapley additive explanations (SHAP) was employed to interpret and visualize the model. Results Two models were constructed using a combination of 34 radiomic features and 10 delta radiomic features, along with 14 clinical features. The radiomics-clinical model and the delta radiomics-clinical model exhibited area under the curve (AUC) of 0.986 [95% confidence interval (CI): 0.977–0.995] and 0.974 (95% CI: 0.959–0.987) in the training set, respectively, and 0.949 (95% CI: 0.908–0.978) and 0.927 (95% CI: 0.879–0.966) in the test set, respectively. The DeLong test of the two models showed no statistical significance (P=0.10) in the test set. SHAP was used to output a summary plot for global interpretation, which showed that preoperative mass, three-dimensional (3D) length, mean diameter, volume, mean CT value, and delta radiomics feature original_firstorder_RootMeanSquared were the relatively more important features in the model. Waterfall plots for local interpretation showed how each feature contributed to the prediction output of a given GGN. Conclusions The delta radiomics-based model proved to be a helpful tool for predicting the invasiveness of GGNs in lung adenocarcinoma. This approach offers a precise, noninvasive alternative in informing clinical decision-making. Additionally, SHAP provided insightful and user-friendly interpretations and visualizations of the model, enhancing its clinical applicability.

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A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules

Cited by 8 publications

References 23 publications

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

Predictive value of spectral dual-detector computed tomography for PD-L1 expression in stage I lung adenocarcinoma: Development and validation of a nomogram

Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study

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