Evidence map›Paper›PMID 40214734›Full record

ArticleEuropean radiology2025

Short-term intra- and peri-tumoral spatiotemporal CT radiomics for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

Xiao Bao, Qin Peng, Dongliang Bian, Jianjiao Ni, Shuchang Zhou, Peng Zhang, Yajia Gu, Jing Gong, Jingyun Shi

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  6. Delta radiomics for predicting early radiation-induced lung injury after thoracic radiotherapy: a retrospective paired-CT study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xiao Bao *Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Qin Peng *Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Dongliang Bian *Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Jianjiao NiDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Shuchang ZhouDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Peng ZhangDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. zhangpeng1121@tongji.edu.cn.
Yajia GuDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China. cjr.guyajia@vip.163.com.
Jing GongDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China. gongjing1990@163.com.
Jingyun ShiDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. shijingyun89179@126.com.ORCID http://orcid.org/0000-0001-6576-5184

Funding

National Natural Science Foundation of China No.82001785National Natural Science Foundation of China No. 82001903Natural Science Foundation of Shanghai No. 21ZR1414200the Excellent Young Talents Project of Shanghai Public Health Three-year (2023-2025) Action Plan No. GWVI-11.2-YQ48
6 · The paper itself

Abstract

purposeThis study aims to develop a short-term spatiotemporal CT radiomics model to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy (NCI) in NSCLC by decoding the intra- and peri-tumoral imaging phenotypes.

methodsA total of 352 patients undergoing curative surgery following NCI for NSCLC were enrolled from two centers, forming a training cohort (n = 186), an internal validation cohort (n = 80), and an external validation cohort (n = 86). Intra- and peri-tumoral CT radiomics features were computed to capture imaging phenotypes of the tumor microenvironment. Delta radiomics features were also calculated by quantifying changes in each radiomics feature. A support vector machine classifier was utilized to develop the short-term spatiotemporal model by analyzing changes in radiomics features.

resultsThe multi-timepoint short-term spatiotemporal model, incorporating pre-treatment, post-treatment and delta radiomic features, achieved AUC values of 0.84, 0.77, and 0.75 in the training, internal validation, and external validation cohorts, respectively. These results significantly outperformed the RECIST model and pre-treatment model, with p-values < 0.05 indicating statistical significance.

conclusionThis study demonstrates that short-term temporal analysis of intra- and peri-tumoral CT radiomics is a promising approach for predicting MPR to NCI in NSCLC. These findings underscore the potential of radiomics as a non-invasive tool for assessing treatment response and guiding personalized therapy in NSCLC patients. KEY POINTS: Question Neoadjuvant chemoimmunotherapy has improved in major pathological response rate for non-small cell lung cancer (NSCLC), but it is unclear which patients will benefit most. Findings The multi-timepoint short-term spatiotemporal model based on CT pictures demonstrates high predictive performance for assessing major pathological response following neoadjuvant chemoimmunotherapy in NSCLC. Clinical relevance Short-term intra- and peri-tumoral CT radiomics is a promising approach for predicting major pathological response to neoadjuvant chemoimmunotherapy in NSCLC. These findings underscore the potential of radiomics as a non-invasive tool for assessing treatment response in NSCLC.

Indexed as

Carcinoma, Non-Small-Cell LungImmunotherapyLung NeoplasmsNeoadjuvant TherapyTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedRadiomicsSupport Vector MachineTreatment OutcomeImmune checkpoint inhibitorsNeoadjuvant therapyNon-small cell lung carcinomaRadiomicsResponse prediction

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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.