Evidence map›Paper›PMID 36826503›Full record

ArticleEuropean radiology2023

Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study.

Jing Li, Shuning Xu, Yi Wang, Mengjie Fang, Fei Ma, Chunmiao Xu, Hailiang Li

Erratum issuedAbstract read
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In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
4.4field-weighted citation impact, top 5% of its field
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

28 citing papers in PubMed, 40 citations in OpenAlex.

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  5. Therapeutic efficacy and oncological prognosis of ypTNM 0-I gastric cancer after neoadjuvant therapy: a multicenter propensity-score matched study in China.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Jing LiDepartment of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
Shuning XuDepartment of Gastrointestinal Oncology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China.
Yi WangDepartment of Pathology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China.
Mengjie FangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Fei MaDepartment of Gastrointestinal Surgery, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China.
Chunmiao XuDepartment of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
Hailiang LiDepartment of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127 Dongming Road, Zhengzhou, 450008, Henan, China. lihailiang_cancer@163.com.
Henan Cancer Hospital · CNChinese Academy of Sciences · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis work focused on developing and validating the spectral CT-based nomogram to preoperatively predict perineural invasion (PNI) for locally advanced gastric cancer (LAGC).

methodsThis work prospectively included 196 surgically resected LAGC patients (139 males, 57 females, 59.55 ± 11.97 years) undergoing triple enhanced spectral CT scans. Patients were labeled as perineural invasion (PNI) positive and negative according to pathologic reports, then further split into primary (n = 130) and validation cohort (n = 66). We extracted clinicopathological information, follow-up data, iodine concentration (IC), and normalized IC values against to aorta (nICs) at arterial/venous/delayed phases (AP/VP/DP). Clinicopathological features and IC values between PNI positive and negative groups were compared. Multivariable logistic regression was performed to screen independent risk factors of PNI. Then, a nomogram was established, and its capability was determined by ROC curves. Its clinical use was evaluated by decision curve analysis. The correlations of PNI and the nomogram with patients' survival were explored by log-rank survival analysis.

resultsBorrmann classification, tumor thickness, and nICDP were independent predictors of PNI and used to build the nomogram. The nomogram yielded higher AUCs of 0.853 (0.744-0.928) and 0.782 (0.701-0.850) in primary and validation cohorts than any other parameters (p < 0.05). Both PNI and the nomogram were related to post-surgical treatment planning. Only PNI was associated with disease-free survival in the primary cohort (p < 0.05).

conclusionThis work prospectively established a spectral CT-based nomogram, which can effectively predict PNI preoperatively and potentially guide post-surgical treatment strategy in LAGC. KEY POINTS: • The present prospective study established a spectral CT-based nomogram for preoperative prediction of perineural invasion in LAGC. • The proposed nomogram, including morphological features and the quantitative iodine concentration values from spectral CT, had the potential to predict PNI for LAGC before surgery, along with guide post-surgical treatment planning. • Normalized iodine concentration at the delayed phase was the most valuable quantitative parameter, suggesting the importance of delayed enhancement in gastric CT.

Indexed as

IodineStomach NeoplasmsFemaleHumansMaleNomogramsProspective StudiesRetrospective StudiesTomography, X-Ray ComputedIodineComputed tomography, spectral imagingGastric cancerIodine concentrationPerineural invasion

Identifiers

PMID36826503
OpenAlexW4321749436

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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.