Evidence mapPaperPMID 41365302Full record

ArticleCell reports. Medicine2025

A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer.

Zihan Zhao, Dexia Chen, Xiaolong Wei, Shuman Li, Xinke Zhang, Weihao Lin, Xueyi Zheng, Ke Zheng, Shuyang Wu, Xiaobo Wen and 10 more

Abstract read
In one paragraph

Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Zihan ZhaoDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Dexia ChenSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Xiaolong WeiDepartment of Pathology, Cancer Hospital of Shantou University Medical College, Shantou 515031, China.
Shuman LiDepartment of Medical Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
Xinke ZhangDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Weihao LinDepartment of Pathology, Cancer Hospital of Shantou University Medical College, Shantou 515031, China.
Xueyi ZhengDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Ke ZhengDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Shuyang WuCentre for Inflammation Research, Institute of Regeneration and Repair, University of Edinburgh, EH16 4UU Edinburgh, UK.
Xiaobo WenDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Baishen ZhangDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Yan ZhengThoracic Surgery Department, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
Shaobin ChenDepartment of Chest Surgery, Cancer Hospital of Shantou University Medical College, No. 7 Raoping Road, Shantou 515031, China.
Chuanmiao XieState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Shuangjiang LiGuangdong Esophageal Cancer Institute, Department of Endoscopy, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Dan XieDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
Ruixuan WangSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China. Electronic address: wangruix5@mail.sysu.edu.cn.
Wenqun XingThoracic Surgery Department, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China. Electronic address: wenqunxingvip@126.com.
Jian ZhouState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China. Electronic address: zhoujian@sysucc.org.cn.
Muyan CaiDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, China. Electronic address: caimy@sysucc.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neoadjuvant immunochemotherapy (nICT) has significantly improved the treatment of locally advanced esophageal cancer (EC), yet accurately identifying patients' response remains a major challenge. In this study, we introduce eSPARK, a multimodal framework designed to integrate routinely available clinical data for informed decision-making in nICT treatment for EC. The model is developed using 344 patients from three independent regions, each with pre-treatment-paired computed tomography (CT) imaging and pathological slides, and postoperative pathological complete response (pCR) outcomes. By incorporating cytological semantic information, eSPARK demonstrates superior generalizability, outperforming single-modality models and achieving robust predictive accuracy across multicenter datasets. Additionally, a multi-scale interpretability module identifies several biomarkers, including the neutrophil-to-lymphocyte ratio (NLR) in the tumor microenvironment, associated with nICT response. Our findings underscore the potential of eSPARK as a powerful tool for personalized therapeutic decision-making in locally advanced EC and its broader implications for advancing precision oncology through multidisciplinary data integration.

Indexed as

Esophageal NeoplasmsImmunotherapyNeoadjuvant TherapyPrecision MedicineAgedFemaleHumansMaleMiddle AgedNeutrophilsTumor Microenvironmentdeep learningesophageal cancermultimodalneoadjuvant immunochemotherapy

Identifiers

PMID41365302
PMCPMC12765826

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.