Evidence map›Paper›PMID 38831258›Full record

ArticleBMC pediatrics2024

Predictors of pulmonary metastases on chest computed tomography in children and adolescents with osteosarcoma-tips for qualifying patients for thoracotomy.

Marek Duczkowski, Agnieszka Duczkowska, Anna Olwert, Elżbieta Michalak, Katarzyna Bilska, Teresa Klepacka, Magdalena Rychłowska-Pruszyńska, Anna Raciborska, Monika Bekiesińska-Figatowska

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In one paragraph

Article in BMC pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

9 authors.

Marek DuczkowskiDepartment of Diagnostic Imaging, Institute of Mother and Child, Warsaw, 01-211, Poland. marek.duczkowski@imid.med.pl.
Agnieszka DuczkowskaDepartment of Diagnostic Imaging, Institute of Mother and Child, Warsaw, 01-211, Poland.
Anna OlwertWIT Academy, Warsaw, 01-447, Poland.
Elżbieta MichalakDepartment of Pathomorphology, Institute of Mother and Child, Warsaw, 01-211, Poland.
Katarzyna BilskaDepartment of Oncology and Surgical Oncology for Children and Youth, Institute of Mother and Child, Warsaw, 01-211, Poland.
Teresa KlepackaDepartment of Pathomorphology, Institute of Mother and Child, Warsaw, 01-211, Poland.
Magdalena Rychłowska-PruszyńskaDepartment of Oncology and Surgical Oncology for Children and Youth, Institute of Mother and Child, Warsaw, 01-211, Poland.
Anna RaciborskaDepartment of Oncology and Surgical Oncology for Children and Youth, Institute of Mother and Child, Warsaw, 01-211, Poland.
Monika Bekiesińska-FigatowskaDepartment of Diagnostic Imaging, Institute of Mother and Child, Warsaw, 01-211, Poland.

Funding

Institute of Mother and Child, Poland No. 510-14-01
6 · The paper itself

Abstract

backgroundOsteosarcoma is the most common primary malignant bone tumour in children and adolescents. Lungs are the most frequent and often the only site of metastatic disease. The presence of pulmonary metastases is a significant unfavourable prognostic factor. Thoracotomy is strongly recommended in these patients, while computed tomography (CT) remains the gold imaging standard. The purpose of our study was to create tools for the CT-based qualification for thoracotomy in osteosarcoma patients in order to reduce the rate of useless thoracotomies.

methodsSixty-four osteosarcoma paediatric patients suspected of lung metastases on CT and their first-time thoracotomies (n = 100) were included in this retrospective analysis. All CT scans were analysed using a compartmental evaluation method based on the number and size of nodules. Calcification and location of lung lesions were also analysed. Inter-observer reliability between two experienced radiologists was assessed. The CT findings were then correlated with the histopathological results of thoracotomies. Various multivariate predictive models (logistic regression, classification tree and random forest) were built and predictors of lung metastases were identified.

resultsAll applied models proved that calcified nodules on the preoperative CT scan best predict the presence of pulmonary metastases. The rating of the operated lung on the preoperative CT scan, dependent on the number and size of nodules, and the total number of nodules on this scan were also found to be important predictors. All three models achieved a relatively high sensitivity (72-92%), positive predictive value (81-90%) and accuracy (74-79%). The positive predictive value of each model was higher than of the qualification for thoracotomy performed at the time of treatment. Inter-observer reliability was at least substantial for qualitative variables and excellent for quantitative variables.

conclusionsThe multivariate models built and tested in our study may be useful in the qualification of osteosarcoma patients for metastasectomy through thoracotomy and may contribute to reducing the rate of unnecessary invasive procedures in the future.

Indexed as

Bone NeoplasmsLung NeoplasmsOsteosarcomaThoracotomyTomography, X-Ray ComputedAdolescentChildFemaleHumansMaleRetrospective StudiesChildrenComputed tomographyMetastasectomyOsteosarcomaPredictorPulmonary metastasesThoracotomy

Identifiers

PMID38831258
PMCPMC11145898

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