Evidence map›Paper›PMID 40853471›Full record

ReviewAbdominal radiology (New York)2026

Recognizing an IVC leiomyosarcoma: CT imaging hallmarks and the utility of 3D rendering.

Zahra F Rahmatullah, Mohammad Yasrab, Satomi Kawamoto, Elliot K Fishman

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

4 authors.

Zahra F RahmatullahJohns Hopkins Hospital, Baltimore, United States. zrahmat3@jh.edu.
Mohammad YasrabJohns Hopkins Hospital, Baltimore, United States.
Satomi KawamotoJohns Hopkins Hospital, Baltimore, United States.
Elliot K FishmanJohns Hopkins Hospital, Baltimore, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inferior vena cava (IVC) leiomyosarcoma is a rare and aggressive malignancy arising from smooth muscle cells of the IVC wall, accounting for less than 1 in 100,000 adult malignancies. It carries a poor prognosis, with high recurrence rates and 5-year disease-free survival reported as low as 6%. Clinical presentation is often vague, including abdominal or back pain, lower extremity edema, and weight loss, making imaging pivotal for diagnosis. Contrast-enhanced CT (CECT) is the modality of choice, but diagnosis remains challenging due to overlap with more common pathologies such as renal cell carcinoma, hepatocellular carcinoma, adrenal tumors, and retroperitoneal lymphadenopathy. This pictorial review presents a curated series of cases highlighting the spectrum of imaging appearances of IVC leiomyosarcoma. By showcasing both typical and atypical imaging patterns, this review aims to broaden radiologists' diagnostic awareness and improve confidence in identifying this elusive tumor. Special emphasis is placed on 3D CT imaging techniques, including optimal acquisition protocols and post-processing methods, to enhance lesion visualization and vascular mapping. Early and accurate recognition is crucial, as aggressive surgical resection remains the mainstay of treatment. Understanding key imaging clues can facilitate timely diagnosis and intervention, ultimately impacting patient outcomes in this rare but life-threatening disease.

Indexed as

Imaging, Three-DimensionalLeiomyosarcomaTomography, X-Ray ComputedVascular NeoplasmsVena Cava, InferiorContrast MediaDiagnosis, DifferentialHumansContrast Media3D imagingCinematic renderingCTIVC leiomyosarcomaIVC sarcomaRetroperitoneal tumors

Identifiers

What OpenQuestion holds

Textmetadata
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.