Evidence map›Paper›PMID 30837449›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2019

Development and Validation of a Nomogram for Preoperative Prediction of Perineural Invasion in Colorectal Cancer.

Xiaoliang Huang, Jungang Liu, Guo Wu, Shaomei Chen, Franco Jeen Pc, Weishun Xie, Weizhong Tang

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.7field-weighted citation impact, top 6% 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

24 citing papers in PubMed, 48 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Xiaoliang HuangDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Jungang LiuDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Guo WuDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Shaomei ChenDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Franco Jeen PcDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Weishun XieDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Weizhong TangDepartment of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Tumor Hospital of Guangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND In colorectal cancer (CRC), perineural invasion (PNI) is usually identified histologically in biopsy or resection specimens and is considered a high-risk feature for recurrence of CRC and is an indicator for adjuvant therapy. Preoperative identification of PNI could help determine the need for adjuvant therapy and the approach to surgical resection. This study aimed to develop and validate a nomogram for the preoperative prediction of PNI in patients with CRC. MATERIAL AND METHODS A total of 664 patients with CRC from a single center were classified into a training dataset (n=468) and a validation dataset (n=196). The least absolute shrinkage and selection operator (LASSO) regression model was used to select potentially relevant features. Multivariate logistic regression analysis was used to develop the nomogram. The performance of the nomogram was assessed based on its calibration, discrimination, and clinical utility. RESULTS The nomogram consisted of five clinical features and provided good calibration and discrimination in the training dataset, with an area under the curve (AUC) of 0.704 (95% CI, 0.657-0.751). Application of the nomogram in the validation cohort showed acceptable discrimination, with the AUC of 0.692 (95% CI, 0.617-0.766) and good calibration. Decision curve analysis (DCA) showed that the nomogram was clinically useful. CONCLUSIONS The nomogram developed in this study might allow clinicians to predict the risk of PNI in patients with CRC preoperatively. The nomogram showed favorable discrimination and calibration values, which may help optimize preoperative treatment decision-making for patients with CRC.

Indexed as

Decision Support TechniquesNomogramsAgedArea Under CurveBiomarkers, TumorChinaColorectal NeoplasmsFemaleHumansMaleMiddle AgedNeoplasm InvasivenessNeoplasm Recurrence, LocalPeripheral NervesReproducibility of ResultsRetrospective StudiesBiomarkers, Tumor

Identifiers

PMID30837449
PMCPMC6415589
OpenAlexW2921937132

What OpenQuestion holds

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LicenceCC BY-NC-ND
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 OpenQuestion graph.