Evidence map›Paper›PMID 36761384›Full record

ArticleMolecular and clinical oncology2023

Renal cell carcinoma T staging: Diagnostic accuracy of preoperative contrast-enhanced computed tomography.

Salah M Fateh, Lusan A Arkawazi, Soran H Tahir, Rezheen J Rashid, Dalshad H Rahman, Ismaeel Aghaways, Fahmi H Kakamad, Abdulwahid M Salih, Rawa Bapir, Saman S Fakhralddin and 3 more

Open access · diamondAbstract read
In one paragraph

Article in Molecular and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

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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

13 authors at 3 institutions in 2 countries.

Salah M FatehCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Lusan A ArkawaziCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Soran H TahirCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Rezheen J RashidDepartment of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Republic of Iraq.
Dalshad H RahmanDepartment of Surgery, Shar Hospital, Sulaimani, Kurdistan 46000, Republic of Iraq.
Ismaeel AghawaysCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Fahmi H KakamadCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Abdulwahid M SalihCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Rawa BapirDepartment of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Republic of Iraq.
Saman S FakhralddinCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Fattah H FattahCollege of Medicine, University of Sulaimani, Sulaimani, Kurdistan 46000, Republic of Iraq.
Berun A AbdallaDepartment of Scientific Affairs, Smart Health Tower, Sulaimani, Kurdistan 46000, Republic of Iraq.
Shvan H MohammedKscien Organization, Sulaimani, Kurdistan 46000, Republic of Iraq.
University of Sulaimani · IQHealth Forecasting · USKurdistan Regional Government · IQ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Renal cell carcinoma (RCC) accounts for 1-2% of all malignancies and is the most common renal tumor in adults. Imaging studies are used for diagnosis and staging. Tumor-Node-Metastasis staging strongly affects prognosis and management, while contrast-enhanced computed tomography (CECT) is regarded as a standard imaging technique for local and distant staging. The present study aimed to evaluate the accuracy of CECT for the preoperative staging of RCC by using surgical and pathological staging as the reference methods. This single-center prospective study was conducted between October 2019 and November 2021. The preoperative abdominal CT scans of patients suspected of having RCC were reviewed. Imaging data were collected, including tumor side and size, and perinephric fat invasion. Intraoperative notes were recorded, including the operation type, perinephric fat invasion, renal vein (RV) or inferior vena cava (IVC) tumor extension, and surrounding organ invasion. pathological data were collected on tumor size, RCC type, presence of clear margins, presence of renal capsule or perinephric fat invasion, renal sinus or pelvicalyceal system (PCS) invasion, segmental or main RV extension, and the involvement of Gerota's fascia and nearby organs. Preoperative CECT revealed that 42 out of 59 tumors had a greater maximum diameter than the pathological specimen, with an overall disparity of 0.25 cm. The specificity of CT for the detection of tumor invasion of the perinephric and renal sinus fat and PCS was 95%, and the sensitivity ranged from 80 to 88%. CT had an 83% sensitivity and a 95 specificity in detecting T4 stage cancer, with a 100% specificity for adrenal invasion. The concordance between radiographic and histological results for RV and IVC involvement was high, with specificities of 94 and 98%, and sensitivities of 80 and 100%, respectively. Overall accuracy for correct T staging was 80%. In conclusion, CECT is accurate in the local T staging of RCC, with high sensitivity and specificity for estimating tumor size and detecting extension to nearby structures and venous invasion.

Indexed as

contrast-enhanced computed tomographylocal staging of renal cell carcinomaperinephric invasionrenal cell carcinomarenal vein invasionT staging

Identifiers

PMID36761384
PMCPMC9892965
OpenAlexW4315648877

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LicenceCC BY-NC-ND
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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.