Therapeutic advances in respiratory disease · 2025
A novel decision tree algorithm model based on chest CT parameters to predict the risk of recurrence and metastasis in surgically resected stage I synchronous multiple primary lung cancer.
СтатьяМногоцентровое исследованиеВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Li Shuangjiang, Chen Guona, Zhang Wenbiao, Ma Huiyun, Liu Baocong, Xu Li, Li Qiong
Аннотация
BACKGROUND: Chest computed tomography (CT) may provide evidence to forecast unexpected recurrence and metastasis following radical surgery for stage I synchronous multiple primary lung cancer (SMPLC).
OBJECTIVE: This study aims to develop and validate a novel CT-based multi-parametric decision tree algorithm (CT-DTA) model capable of accurate risk assessment.
DESIGN: A multicenter retrospective cohort study.
METHODS: There were 209 patients with pathological stage I SMPLC from three tertiary centers included. We initially screened all of the CT-derived imaging parameters in the training cohort (130 patients from Center A) and then selected those showing statistical significance to construct a DTA model. The discriminative strength of the CT-DTA model for postoperative recurrence and metastasis was then validated in the validation cohort (79 patients from Centers B and C). Moreover, the performance of the CT-DTA model was further evaluated across different subgroups of the entire cohort.
RESULTS: Five key imaging parameters measured on chest thin-section CT, including consolidation tumor ratio (CTR), long-axis diameter of the lesion, number of pure solid nodules, presence of spiculation and pleural indentation, constituted a CT-DTA model with nine leaf nodes, and CTR was the leading risk contributor of them. The CT-DTA model achieved a satisfactory predictive accuracy indicated by an area under the curve of more than 0.80 in both the training cohort and validation cohort. Meanwhile, this CT-DTA model was also exhaustively demonstrated to play as the only independent risk factor for postoperative recurrence and metastasis. Its promising predictive performance still remained stable across nearly all of the subgroups stratified by clinicopathological characteristics.
CONCLUSION: This CT-DTA model could serve as a noninvasive, user-friendly, and practicable risk prediction tool to aid treatment decision-making in operable stage I SMPLC.
OBJECTIVE: This study aims to develop and validate a novel CT-based multi-parametric decision tree algorithm (CT-DTA) model capable of accurate risk assessment.
DESIGN: A multicenter retrospective cohort study.
METHODS: There were 209 patients with pathological stage I SMPLC from three tertiary centers included. We initially screened all of the CT-derived imaging parameters in the training cohort (130 patients from Center A) and then selected those showing statistical significance to construct a DTA model. The discriminative strength of the CT-DTA model for postoperative recurrence and metastasis was then validated in the validation cohort (79 patients from Centers B and C). Moreover, the performance of the CT-DTA model was further evaluated across different subgroups of the entire cohort.
RESULTS: Five key imaging parameters measured on chest thin-section CT, including consolidation tumor ratio (CTR), long-axis diameter of the lesion, number of pure solid nodules, presence of spiculation and pleural indentation, constituted a CT-DTA model with nine leaf nodes, and CTR was the leading risk contributor of them. The CT-DTA model achieved a satisfactory predictive accuracy indicated by an area under the curve of more than 0.80 in both the training cohort and validation cohort. Meanwhile, this CT-DTA model was also exhaustively demonstrated to play as the only independent risk factor for postoperative recurrence and metastasis. Its promising predictive performance still remained stable across nearly all of the subgroups stratified by clinicopathological characteristics.
CONCLUSION: This CT-DTA model could serve as a noninvasive, user-friendly, and practicable risk prediction tool to aid treatment decision-making in operable stage I SMPLC.