Evidence map›Paper›PMID 31185938›Full record

Observational studyBMC cancer2019

Development and validation of a model that includes two ultrasound parameters and the plasma D-dimer level for predicting malignancy in adnexal masses: an observational study.

Maciej Stukan, Michał Badocha, Karol Ratajczak

Open access · goldAbstract readObservational StudyValidation Study
In one paragraph

Observational study in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 15% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Diagnostic accuracy of ultrasound models for assessment of ovarian tumors: systematic review and meta-analysis.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026
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  10. Study on the Diagnostic Value of Clinical Decision Rules for Malignant Tumors Complicated with Pulmonary Thromboembolism: A Retrospective Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
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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

3 authors at 2 institutions in 1 country.

Maciej StukanDepartment of Gynecologic Oncology, Gdynia Oncology Center, Pomeranian Hospitals, Gdynia, Poland, Postal address: ul. Powstania Styczniowego 1, 81-519, Gdynia, Poland. maciej.stukan@gmail.com.ORCID http://orcid.org/0000-0003-2376-4663
Michał BadochaDepartment of Physical Chemistry, Gdańsk University of Technology, Gdańsk, Poland, Postal address: ul. Gabriela Narutowicza 11/12, 80-233, Gdańsk, Poland.
Karol RatajczakKarol Ratajczak Consulting, ul. Damroki 1A, 80-175, Gdańsk, Poland.
Gdańsk University of Technology · PLStocznia Gdynia (Poland) · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPre-operative discrimination of malignant from benign adnexal masses is crucial for planning additional imaging, preparation, surgery and postoperative care. This study aimed to define key ultrasound and clinical variables and develop a predictive model for calculating preoperative ovarian tumor malignancy risk in a gynecologic oncology referral center. We compared our model to a subjective ultrasound assessment (SUA) method and previously described models.

methodsThis prospective, single-center observational study included consecutive patients. We collected systematic ultrasound and clinical data, including cancer antigen 125, D-dimer (DD) levels and platelet count. Histological examinations served as the reference standard. We performed univariate and multivariate regressions, and Bayesian information criterion (BIC) to assess the optimal model. Data were split into 2 subsets: training, for model development (190 observations) and testing, for model validation (n = 100).

resultsAmong 290 patients, 52% had malignant disease, including epithelial ovarian cancer (72.8%), metastatic disease (14.5%), borderline tumors (6.6%), and non-epithelial malignancies (4.6%). Significant variables were included into a multivariate analysis. The optimal model, included three independent factors: solid areas, the color score, and the DD level. Malignant and benign lesions had mean DD values of 2.837 and 0.354 μg/ml, respectively. We transformed established formulae into a web-based calculator ( http://gin-onc-calculators.com/gynonc.php ) for calculating the adnexal mass malignancy risk. The areas under the curve (AUCs) for models compared in the testing set were: our model (0.977), Simple Rules risk calculation (0.976), Assessment of Different NEoplasias in the adneXa (ADNEX) (0.972), Logistic Regression 2 (LR2) (0.969), Risk of Malignancy Index (RMI) 4 (0.932), SUA (0.930), and RMI3 (0.912).

conclusionsTwo simple ultrasound predictors and the DD level (also included in a mathematical model), when used by gynecologist oncologist, discriminated malignant from benign ovarian lesions as well or better than other more complex models and the SUA method. These parameters (and the model) may be clinically useful for planning adequate management in the cancer center. The model needs substantial validation.

Indexed as

Models, TheoreticalAdnexal DiseasesAdnexa UteriAdultAgedArea Under CurveBayes TheoremCA-125 AntigenCarcinoma, Ovarian EpithelialDiagnosis, DifferentialFemaleFibrin Fibrinogen Degradation ProductsHumansLogistic ModelsMiddle AgedPlatelet CountCA-125 AntigenFibrin Fibrinogen Degradation Productsfibrin fragment DCalculationD-dimerDiagnosis, differentialOvarian cancerSensitivity and specificityUltrasound

Identifiers

PMID31185938
PMCPMC6558858
OpenAlexW2951371808

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.