Evidence map›Paper›PMID 33603485›Full record

ArticleCancer management and research2021

A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma.

Yanqing Ma, Xiren Xu, Peipei Pang, Yang Wen

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In one paragraph

Article in Cancer management and research, 2021. 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
–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

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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.

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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

4 authors.

Yanqing MaDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310000, People's Republic of China.ORCID 0000-0002-6131-3284
Xiren XuDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310000, People's Republic of China.
Peipei PangDepartment of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, 310000, People's Republic of China.
Yang WenDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the role of tumor and mini-peritumor in the context of CT-based radiomics analysis to differentiate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC).

methodsA total of 58 fp-AMLs and 172 ccRCCs were enrolled. The volume of interest (VOI) was manually delineated in the standardized CT images and radiomics features were automatically calculated with software. After methods of feature selection, the CT-based logistic models including tumoral model (Ra-tumor), mini-peritumoral model (Ra-peritumor), perirenal model (Ra-Pr), perifat model (Ra-Pf), and tumoral+perirenal model (Ra-tumor+Pr) were constructed. The area under curves (AUCs) were calculated by DeLong test to evaluate the efficiency of logistic models.

resultsThe AUCs of Ra-peritumor of nephrographic phase (NP) were slightly higher than those of corticomedullary phase (CMP). Furthermore, the Ra-Pr showed significant higher efficiency than the Ra-Pf, and relative more optimal radiomics features were selected in the Ra-Pr than Ra-Pf. The Ra-tumor+Pr combined tumoral and perirenal radiomics analysis was of most significant in distinction compared with Ra-tumor and Ra-peritumor.

conclusionThe validity of NP to differentiate fp-AML from ccRCC was slightly higher than that of CMP. To the NP analysis, the Ra-Pr was superior to the Ra-Pf in distinction, and the lesions invaded to the perirenal tissue more severely than to the perifat tissue. It is important to the individual therapeutic surgeries according to the different lesion location. The pooled tumoral and perirenal radiomics analysis was the most promising approach in distinguishing fp-AML and ccRCC.

Indexed as

angiomyolipomaclear cell renal cell carcinomacomputed tomographyperitumorradiomics

Identifiers

PMID33603485
PMCPMC7886092

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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.