Evidence map›Paper›PMID 32356484›Full record

ArticleThe British journal of radiology2020

Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

Assad Oberai, Bino Varghese, Steven Cen, Tomas Angelini, Darryl Hwang, Inderbir Gill, Manju Aron, Christopher Lau, Vinay Duddalwar

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
3.3field-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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 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

9 authors at 1 institution in 1 country.

Assad OberaiDepartment of Aerospace and Mechanical Engineering, Univ. of Southern California, Los Angeles, CA, USA.
Bino VargheseDepartment of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
Steven CenDepartment of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
Tomas AngeliniDepartment of Computer Science, Univ. of Southern California, Los Angeles, CA, USA.
Darryl HwangDepartment of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
Inderbir GillInstitute of Urology, Univ. of Southern California, Los Angeles, CA, USA.
Manju AronDepartment of Pathology, Univ. of Southern California, Los Angeles, CA, USA.
Christopher LauDepartment of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
Vinay DuddalwarDepartment of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
University of Southern California · US

Funding

USC/NORRIS COMPREHENSIVE CANCER CENTER (CORE) SUPPORTP30CA014089 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Saul Priceman · 1985 to 2026
$181.4M
NCI NIH HHS P30 CA014089
6 · The paper itself

Abstract

objectiveEstablish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network.

methodsIn this institutional review board approved study of 143 patients with predominantly solid, lipid-poor, contrast enhancing renal lesions (46 benign and 97 malignant), patients with a pre-operative multiphase CECT of the abdomen and pelvis obtained between June 2009 and June 2015 were retrospectively queried. Benign renal masses included oncocytoma and lipid-poor angiomyolipoma and the malignant group included clear cell, papillary, and chromophobe carcinomas.Region of interests of whole tumor volumes were manually segmented, and CT phase images with the largest cross-section of the segmented tumor in the axial plane were used for assessment. Post-surgical pathological evaluation was used to establish diagnosis.The segmented images of renal masses were used as input to a CNN. The data were augmented and split into training (83.9%) and validation sets (16.1%) to determine the hyperparameters of the CNN. Thereafter. the performance of the resulting CNN was quantified using eightfold cross-validation.

resultsThe CNN-based classifier demonstrated an overall accuracy of 78% (95% confidence interval: 76-80%), sensitivity of 70% (95% confidence interval: 66-74%), specificity of 81% (79-83%) and an area under the curve of 0.82.

conclusionA CNN-based classifier to diagnose solid enhancing malignant renal masses based on multiphase CECT images was developed. ADVANCES IN KNOWLEDGE: It was established that a CNN-based classifier could be trained to accurately distinguish malignant renal lesions.

Indexed as

Contrast MediaDeep LearningAdenoma, OxyphilicAngiomyolipomaCarcinoma, Renal CellFemaleHumansKidney NeoplasmsMaleMiddle AgedRetrospective StudiesTomography, X-Ray ComputedContrast Media

Identifiers

PMID32356484
PMCPMC7336076
OpenAlexW3022494500

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.