Evidence map›Paper›PMID 35932353›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2022

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.

Jiayi Zhang, Yanfen Cui, Kaikai Wei, Zhenhui Li, Dandan Li, Ruirui Song, Jialiang Ren, Xin Gao, Xiaotang Yang

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 4 pooled it
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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

22 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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.

Jiayi Zhang *Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China.
Yanfen Cui *Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
Kaikai Wei *Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China.
Zhenhui Li *Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, Yunnan, China.
Dandan LiDepartment of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
Ruirui SongDepartment of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
Jialiang RenGE Healthcare China, Beijing, 100176, China.
Xin GaoDivision of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China.
Xiaotang YangDepartment of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China. yangxt210@126.com.ORCID 0000-0002-2233-1404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients.

methodsA total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC).

resultsThe DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05).

conclusionsA CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.

Indexed as

Deep LearningNeoplasms, Second PrimaryStomach NeoplasmsArea Under CurveHumansNeoadjuvant TherapyRetrospective StudiesDeep learningLocally advanced gastric cancerNeoadjuvant chemotherapyPre-treatment computed tomography

Identifiers

PMID35932353

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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.