Evidence map›Paper›PMID 36169239›Full record

ArticleThe British journal of radiology2022

Development of an improved diagnostic nomogram for preoperative prediction of small cell neuroendocrine cancer of the cervix.

Yun-Zhi Li, Peng Liu, Bao-Hong Mao, Li-Li Wang, Jia-Liang Ren, Yong-Sheng Xu, Guang-Yao Liu, Zhong-Hong Xin, Jun-Qiang Lei

Open access · hybridAbstract read
In one paragraph

Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 48% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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 at 5 institutions in 1 country.

Yun-Zhi LiLanzhou University, Lanzhou, Gansu, China.
Peng LiuDepartment of Radiology, Gansu Provincial Cancer Hospital, Lanzhou, Gansu, China.
Bao-Hong MaoDepartment of Clinical Medical Research Centre, Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu, China.
Li-Li WangDepartment of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu, China.
Jia-Liang RenGE Healthcare China, Beijing, China.
Yong-Sheng XuLanzhou University, Lanzhou, Gansu, China.
Guang-Yao LiuDepartment of Magnetic Resonance, the Second Hospital of Lanzhou University, Lanzhou, Gansu, China.
Zhong-Hong XinLanzhou University, Lanzhou, Gansu, China.
Jun-Qiang LeiLanzhou University, Lanzhou, Gansu, China.
First Hospital of Lanzhou University · CNGansu Provincial Hospital · CNGansu Provincial Maternal and Child Health Hospital · CNLanzhou University · CNLanzhou University Second Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAccurate preoperative diagnosis of small cell neuroendocrine cancer of the cervix (SCNECC) is crucial for establishing the best treatment plan. This study aimed to develop an improved, non-invasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information.

methodsA total of 105 pathologically confirmed cervical cancer patients (35 SCNECC, 70 non-SCNECC) from multiple centres with complete clinical and MR records were included. Whole lesion histogram analysis of the ADC was performed. Multivariate logistic regression analysis was used to develop diagnostic models based on clinical, morphological, and histogram data. The predictive performance in terms of discrimination, calibration, and clinical usefulness of the different models was assessed. A nomogram for preoperatively discriminating SCNECC was developed from the combined model.

resultsIn preoperative SCNECC diagnosis, the combined model, which had a diagnostic AUC (area under the curve) of 0.937 (95% CI: 0.887-0.987), outperformed the clinical-morphological model, which had an AUC of 0.869 (CI: 0.788-0.949), and the histogram model, which had an AUC of 0.872 (CI: 0.792-0.951). The calibration curve and decision curve analyses suggest that the combined model achieved good fitting and clinical utility.

conclusionsNon-invasive preoperative diagnosis of SCNECC can be achieved with high accuracy by integrating clinical, MR morphological, and ADC histogram features. The nomogram derived from the combined model can provide an easy-to-use clinical preoperative diagnostic tool for SCNECC. ADVANCES IN KNOWLEDGE: It is clear that the therapeutic strategies for SCNECC are different from those for other pathological types of cervical cancer according to V 1.2021 of the NCCN clinical practice guidelines in oncology for cervical cancer. This research developed an improved, non-invasive method for the preoperative diagnosis of SCNECC by integrating clinical, MR morphological, and apparent diffusion coefficient (ADC) information.

Indexed as

Carcinoma, NeuroendocrineCarcinoma, Small CellUterine Cervical NeoplasmsCervix UteriDiffusion Magnetic Resonance ImagingFemaleHumansNomogramsRetrospective Studies

Identifiers

PMID36169239
PMCPMC9733602
OpenAlexW4297489547

What OpenQuestion holds

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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.