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. 2024 Oct 7:10:20552076241289181.
doi: 10.1177/20552076241289181. eCollection 2024 Jan-Dec.

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study

Hong Zheng  1 , Wei Chen  2 , Wanyin Qi  3 , Haibo Liu  4 , Zhichao Zuo  5
Affiliations

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study

Hong Zheng et al. Digit Health. .

Abstract

Objective: The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions.

Methods: A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance.

Results: The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924).

Conclusion: The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.

Keywords: Intratumor heterogeneity; invasiveness; machine learning; pulmonary adenocarcinoma; pure ground-glass nodule.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Acquisition and calculation process of the ITH score.
Figure 2.
Figure 2.
Study framework of enrolled cases, including CT scan, diagnosis approaches, the ITH-clinRad guided catBoost classifier, and comparison of diagnosis efficacy.
Figure 3.
Figure 3.
ROC curve analysis of 11 machine learning algorithms with the merge of the ITH score with clinRad.
Figure 4.
Figure 4.
AUC, ACC, SEN, SPE, PPV, and NPV of 11 machine learning algorithms.
Figure 5.
Figure 5.
SHAP for model interpretation, including beeswarm plot (a), heatmap (b), and force plot (c).
Figure 6.
Figure 6.
ROC curve across the ITH-clinRad-guided catBoost classifier, ITH score, clinRad-guided catBoost classifier, and lesion size.
Figure 7.
Figure 7.
Correlation between the ITH score and clinRad, including a chord diagram (a) and matrix plot (b).
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