What does this research mean for the field?

A composite prediction model integrating clinical data, CT radiomics, and circulating tumor cell (CTC) counts accurately discriminates between minimally invasive and invasive early-stage lung adenocarcinoma, outperforming single-modality approaches. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to develop a predictive model integrating CT radiomics and circulating tumor cells to assess lung adenocarcinoma invasiveness.

March 21, 2026OncoTargets and TherapyOpen Access

Integrated CT Radiomics and Circulating Tumor Cell Analysis in Predicting Lung Adenocarcinoma Invasion: A Dual-Center Study with Implications for Personalized Treatment

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Authors

QZQingtao ZhaoHebei Medical University RWRunzhe WangHebei Medical University QZQingxin ZhaoHebei Medical University

Discussion

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Implication

A dual-center study demonstrates improved prediction of lung adenocarcinoma invasiveness using radiomics and CTCs, suggesting enhanced personalized treatment strategies.

Key Points

This research aims to develop a predictive model integrating CT radiomics and circulating tumor cells to assess lung adenocarcinoma invasiveness.
Retrospective analysis of clinical, imaging, CTCs, and pathological data from 202 lung adenocarcinoma patients across two centers.
Development set and test set established using 146 cases from one center, with external validation from 56 cases from another center.
Machine learning techniques used to analyze CTC counts and radiomic features, followed by LASSO regression for feature selection.
107 radiomic features extracted, categorized into several groups with varying percentages.
The composite clinical-radiomics-CTCs model achieved the highest predictive accuracy, with an AUC of 0.980, outperforming all other models.
Significant differences in clinical-imaging semantic features demonstrated critical predictors for model development.

Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/69be34d16e48c4981c672e5b https://doi.org/10.2147/ott.s597565

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March 21, 2026OncoTargets and TherapyOpen Access

Integrated CT Radiomics and Circulating Tumor Cell Analysis in Predicting Lung Adenocarcinoma Invasion: A Dual-Center Study with Implications for Personalized Treatment

View Full Paper

Ask AI

Bookmark

Authors

QZQingtao ZhaoHebei Medical University RWRunzhe WangHebei Medical University QZQingxin ZhaoHebei Medical University

Discussion

Member takes

Implication

A dual-center study demonstrates improved prediction of lung adenocarcinoma invasiveness using radiomics and CTCs, suggesting enhanced personalized treatment strategies.

Key Points

This research aims to develop a predictive model integrating CT radiomics and circulating tumor cells to assess lung adenocarcinoma invasiveness.
Retrospective analysis of clinical, imaging, CTCs, and pathological data from 202 lung adenocarcinoma patients across two centers.
Development set and test set established using 146 cases from one center, with external validation from 56 cases from another center.
Machine learning techniques used to analyze CTC counts and radiomic features, followed by LASSO regression for feature selection.
107 radiomic features extracted, categorized into several groups with varying percentages.
The composite clinical-radiomics-CTCs model achieved the highest predictive accuracy, with an AUC of 0.980, outperforming all other models.
Significant differences in clinical-imaging semantic features demonstrated critical predictors for model development.

Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/69be34d16e48c4981c672e5b https://doi.org/10.2147/ott.s597565

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