Evidence map›Paper›PMID 30362816›Full record

ArticleThe British journal of radiology2019

Qualitative and quantitative CECT features for differentiating renal primitive neuroectodermal tumor from the renal cell carcinoma and its subtypes.

Pawan Kumar, Anuradha Singh, Ashwin Deshmukh, Ravi Hari Phulware, Sameer Rastogi, Adarsh Barwad, S H Chandrashekhara, Vishwajeet Singh

Open access · bronzeAbstract read
In one paragraph

Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 29% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. 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

8 authors at 1 institution in 1 country.

Pawan Kumar1 Department of Radiodiagnosis, All India Institute of Medical Sciences , New Delhi , India.
Anuradha Singh1 Department of Radiodiagnosis, All India Institute of Medical Sciences , New Delhi , India.
Ashwin Deshmukh1 Department of Radiodiagnosis, All India Institute of Medical Sciences , New Delhi , India.
Ravi Hari Phulware2 Department of Pathology, All India Institute of Medical Sciences , New Delhi , India.
Sameer Rastogi3 Department of Medical Oncology, All India Institute of Medical Sciences , New Delhi , India.
Adarsh Barwad2 Department of Pathology, All India Institute of Medical Sciences , New Delhi , India.
S H Chandrashekhara1 Department of Radiodiagnosis, All India Institute of Medical Sciences , New Delhi , India.
Vishwajeet Singh4 Department of Biostatistics, All India Institute of Medical Sciences , New Delhi , India.
All India Institute of Medical Sciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objective: To identify important qualitative and quantitative clinical and imaging features that could potentially differentiate renal primitiveneuroectodermal tumor (PNET) from various subtypes of renalcell carcinoma (RCC).

methods: We retrospectively reviewed 164 patients, 143 with pathologically proven RCC and 21 with pathologically proven renal PNET. Univariate analysis of each parameter was performed. In order to differentiate renal PNET from RCC subtypes and overall RCC as a group, we generated ROC curves and determined cutoff values for mean attenuation of the lesion, mass to aorta attenuation ratio and mass to renal parenchyma attenuation ratio in the nephrographic phase.

results: Univariate analysis revealed 11 significant parameters for differentiating renal PNET from clear cell RCC (age, p = <0.001; size, p =< 0.001; endophytic growth pattern, p < 0.001;margin of lesion, p =< 0.001; septa within the lesion, p =< 0.001; renal vein invasion, p =< 0.001; inferior vena cava involvement, p = 0.014; enhancement of lesion less than the renal parenchyma, p = 0.008; attenuation of the lesion, p = 0.002; mass to aorta attenuation ratio, p =< 0.001; and mass to renal parenchyma attenuation ratio, p =< 0.001). Univariate analysis also revealed seven significant parameters for differentiating renal PNET from papillary RCC. For differentiating renal PNET from overall RCCs as a group, when 77.3 Hounsfield unit was used as cutoff value in nephrographic phase, the sensitivity and specificity were 71.83 and 76.92 % respectively. For differentiating renal PNET from overall RCCs as a group, when 0.57 was used as cutoff for mass to aorta enhancement ratio in nephrographic phase, the sensitivity and specificity were 80.28 and 84.62 % respectively.

conclusion: Specific qualitative and quantitative features can potentially differentiate renal PNET from various subtypes of RCC. ADVANCES IN KNOWLEDGE:: The study underscores the utility of combined demographic and CT findings to potentially differentiate renal PNET from the much commoner renal neoplasm, i.e. RCC. It has management implications as if RCC is suspected, surgeons proceed with resection without need for confirmatory biopsy. On the contrary, a suspected renal PNET should proceed with biopsy followed by chemoradiotherapy, thus obviating the unnecessary morbidity and mortality.

Indexed as

AdultCarcinoma, Renal CellContrast MediaDiagnosis, DifferentialFemaleHumansKidney NeoplasmsMaleMiddle AgedNeuroectodermal Tumors, Primitive, PeripheralRetrospective StudiesROC CurveTomography, X-Ray ComputedContrast Media

Identifiers

PMID30362816
PMCPMC6404838
OpenAlexW2898138015

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.