Evidence map›Paper›PMID 41081204›Full record

ArticleQuantitative imaging in medicine and surgery2025

Predictive value of

Qiaoliang Chen, Shuangxiu Tan, Jing Chen, Jiaxin Yin, Qing Sun, Di Liang, Lian Wang, Aimei Li, Jian He

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 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

9 authors.

Qiaoliang Chen *Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Shuangxiu Tan *Department of Ultrasound Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jing ChenDepartment of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jiaxin YinDepartment of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Qing SunNanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Di LiangNanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Lian WangCollege of Medicine, Wuhan University of Science and Technology, Wuhan, China.
Aimei Li *Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Jian He *Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer (CRC) is among the most prevalent malignant neoplasms within the digestive system. Perineural invasion (PNI) is a significant predictor of CRC prognosis, thus the crucial need to predict PNI status prior to surgical intervention. The aim of this study was to explore the efficacy of Methods: This study involved the retrospective collection of Results: According to univariate analysis, the PNI group and the non-PNI group differed statistically significantly in the metabolic tumor volume (MTV) 40% (P=0.027), the total lesion glycolysis (TLG) 40% (P=0.027), TLG60% (P=0.033), the coefficients of variation (CV) (P<0.001), the heterogeneity index (HF) (P=0.021), PAT grade (P=0.002), cN stage (P<0.001), and cM stage (P=0.016). The results of the multivariate logistic regression analysis identified the following variables as independent risk factors for predicting PNI: CV [odds ratio (OR) =3.128, 95% confidence interval (CI): 1.476-6.628, P=0.003], PAT grade (PAT grade 2: OR =12.016, 95% CI: 2.859-50.509, P<0.001; PAT grade 3: OR =22.417, 95% CI: 4.291-117.104, P<0.001), and cN stage (OR =4.769, 95% CI: 1.636-13.900, P=0.004). The ROC curve indicated an area under the curve (AUC) value of 0.893 (95% CI: 0.837-0.949) for the nomogram model. The internal validation concordance index (C-index) was found to be 0.861. Calibration curves and DCA demonstrated that the nomogram model exhibited both good accuracy and clinical utility. Conclusions:

Indexed as

Colorectal cancer (CRC)nomogramperineural invasion (PNI)positron emission tomography/computed tomography (PET/CT)prediction

Identifiers

PMID41081204
PMCPMC12514694

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Registered trials

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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.