Evidence map›Paper›PMID 41418024›Full record

ArticleInternational journal of surgery (London, England)2025

Ablation versus sublobar resection for stage IA non-small cell lung cancer: a multicenter retrospective cohort study using a deep learning model in the ablation group.

Bingchen Xu, Dongcun Huang, Dengyao Liu, Tao Huang, Zhixian Chen, Zhihua Zhu, Letao Lin, Fujun Zhang

Abstract read
In one paragraph

Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Bingchen XuState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Minimally Invasive Intervention, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.
Dongcun HuangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Minimally Invasive Intervention, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.
Dengyao LiuState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Minimally Invasive Intervention, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.
Tao HuangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Minimally Invasive Intervention, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.
Zhixian ChenGuangZhou FuDa Cancer Hospital, Guangzhou, P.R. China.
Zhihua ZhuState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Thoracic Surgery, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.
Letao LinState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Minimally Invasive Intervention, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.
Fujun ZhangState Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Department of Minimally Invasive Intervention, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImage-guided thermal ablation (IGTA) has increasingly been used for stage IA non-small cell lung cancer (NSCLC), yet long-term outcomes comparing IGTA with sublobar resection (SLR) without surgical contraindications remain unclear. Identifying patients who may benefit from ablation is also technically challenging. MATERIALS AND

methodsA total of 2,145 patients treated with IGTA or SLR from 2012 to 2023 were analyzed. Propensity score matching was performed. Progression-free survival (PFS) and cancer-specific survival (CSS) rates were estimated using the Kaplan-Meier method. A deep learning model based on pre-treatment computed tomography images was developed to predict disease-free survival after IGTA and identify patients most appropriate for ablation.

resultsAfter matching, the median follow-up was 39.3 months for the SLR group and 46.7 months for the IGTA group. No significant differences were observed in PFS (hazard ratio [HR], 2.02; 95% confidence interval [CI], 0.83-4.89; P = 0.120) or CSS (HR, 3.09; 95% CI, 0.87-11.04; P = 0.082). The area under the curve (AUC) of the Vision Transformer (ViT) model was 0.826 and 0.814 for the training and external validation cohorts, respectively. Higher ViT scores predicted worse survival (HR, 8.61; P < 0.001). The Multimodal XGBoost model further improved the performance.

conclusionIn this study, long-term outcomes after IGTA were not significantly different from those after SLR in patients with stage IA NSCLC. The ViT model accurately predicted outcomes after ablation. These results indicate that ablation may be a feasible alternative to conventional surgery in appropriately selected patients.

Indexed as

deep learningimage-guided thermal ablationinterpretableminimally invasive interventionvision transformer

Identifiers

PMID41418024
PMCPMC13105676

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.