Evidence map›Paper›PMID 36651979›Full record

ArticleAbdominal radiology (New York)2023

Preoperative prediction of miliary changes in the small bowel mesentery in advanced high-grade serous ovarian cancer using MRI radiomics nomogram.

Qinhao Guo, Zijing Lin, Jing Lu, Rong Li, Lei Wu, Lin Deng, Jinwei Qiang, Xiaohua Wu, Yajia Gu, Haiming Li

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Article in Abdominal radiology (New York), 2023. 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
1.1field-weighted citation impact, top 22% 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, 4 citations in OpenAlex.

  1. Review
4 · The record

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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

10 authors at 4 institutions in 1 country.

Qinhao Guo *Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Zijing Lin *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Jing LuDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Rong LiDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Lei WuDepartment of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
Lin DengDepartment of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
Jinwei QiangDepartment of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
Xiaohua Wu *Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Yajia Gu *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Haiming Li *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. lihaiming0109@163.com.
Shanghai Medical College of Fudan University · CNJinshan Hospital of Fudan University · CNFudan University Shanghai Cancer Center · CNGuangdong Academy of Medical Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and validate an MRI-based radiomics nomogram for the preoperative prediction of miliary changes in the small bowel mesentery (MCSBM) in advanced high-grade serous ovarian cancer (HGSOC). MATERIALS AND

methodsOne hundred and twenty-eight patients with pathologically proved  advanced HGSOC (training cohort: n = 91; validation cohort: n = 37) were retrospectively included. All patients were initially evaluated as MCSBM-negative by preoperative imaging modalities but were finally confirmed by surgery and histopathology (MCSBM-positive: n = 53; MCSBM-negative: n = 75). Five radiomics signatures were built based on the features from multisequence magnetic resonance images. Independent clinicoradiological factors and radiomics-fusion signature were further integrated to construct a radiomics nomogram. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves and clinical utility.

resultsRadiomics signatures, ascites, and tumor size were independent predictors of MCSBM. A nomogram integrating radiomics features and clinicoradiological factors demonstrated satisfactory predictive performance with areas under the curves (AUCs) of 0.871 (95% CI 0.801-0.941) and 0.858 (95% CI 0.739-0.976) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) revealed that the nomogram had a significantly improved ability compared with the clinical model in the training cohort (NRI = 0.343, p = 0.002; IDI = 0.299, p < 0.001) and validation cohort (NRI = 0.409, p = 0.015; IDI = 0.283, p = 0.001).

conclusionOur proposed nomogram has the potential to serve as a noninvasive tool for the prediction of MCSBM, which is helpful for the individualized assessment of advanced HGSOC patients.

Indexed as

NomogramsOvarian NeoplasmsFemaleHumansMagnetic Resonance ImagingMesenteryRetrospective StudiesHigh-grade serous ovarian cancerMagnetic resonance imagingMiliary changes in the small bowel mesenteryPrimary debulking surgeryRadiomics nomogram

Identifiers

PMID36651979
OpenAlexW4317105292

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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.