Evidence map›Paper›PMID 34868918›Full record

ArticleFrontiers in oncology2021

Deep Learning Plus Three-Dimensional Printing in the Management of Giant (>15 cm) Sporadic Renal Angiomyolipoma: An Initial Report.

Yunliang Gao, Yuanyuan Tang, Da Ren, Shunhua Cheng, Yinhuai Wang, Lu Yi, Shuang Peng

Abstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

7 authors.

Yunliang GaoDepartment of Urology, The Second Xiangya Hospital, Central South University, Changsha, China.
Yuanyuan TangDepartment of Oncology, The Second Xiangya Hospital, Central South University, Changsha, China.
Da RenDepartment of Urology, The Second Xiangya Hospital, Central South University, Changsha, China.
Shunhua ChengHunan Engineering Research Center of Smart and Precise Medicine, Changsha, China.
Yinhuai WangDepartment of Urology, The Second Xiangya Hospital, Central South University, Changsha, China.
Lu YiDepartment of Urology, The Second Xiangya Hospital, Central South University, Changsha, China.
Shuang PengClinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the feasibility and effectivity of deep learning (DL) plus three-dimensional (3D) printing in the management of giant sporadic renal angiomyolipoma (RAML).

methodsThe medical records of patients with giant (>15 cm) RAML were retrospectively reviewed from January 2011 to December 2020. 3D visualized and printed kidney models were performed by DL algorithms and 3D printing technology, respectively. Patient demographics and intra- and postoperative outcomes were compared between those with 3D-assisted surgery (3D group) or routine ones (control group).

resultsAmong 372 sporadic RAML patients, 31 with giant ones were eligible for analysis. The median age was 40.6 (18-70) years old, and the median tumor size was 18.2 (15-28) cm. Seventeen of 31 (54.8%) had a surgical kidney removal. Overall, 11 underwent 3D-assisted surgeries and 20 underwent routine ones. A significant higher success rate of partial nephrectomy (PN) was noted in the 3D group (72.7%

conclusions3D visualized and printed kidney models appear to be additional tools to assist operational management and avoid a high rate of kidney removal for giant sporadic RAMLs.

Indexed as

angiomyolipomadeep learninggiantkidneypartial nephrectomythree-dimensional printing

Identifiers

PMID34868918
PMCPMC8634108

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