Evidence map›Paper›PMID 31537407›Full record

ArticleEuropean urology2019

Augmented Bladder Tumor Detection Using Deep Learning.

Eugene Shkolyar, Xiao Jia, Timothy C Chang, Dharati Trivedi, Kathleen E Mach, Max Q-H Meng, Lei Xing, Joseph C Liao

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

Article in European urology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 2 pooled it
18.1field-weighted citation impact, top 1% 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

90 citing papers in PubMed, 2 syntheses or guidelines pooled it, 188 citations in OpenAlex.

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30 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Eugene ShkolyarDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Xiao JiaDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
Timothy C ChangDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Dharati TrivediDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Kathleen E MachDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Max Q-H MengDepartment of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
Lei XingDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: lei@stanford.edu.
Joseph C LiaoDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA. Electronic address: jliao@stanford.edu.
Stanford University · USChinese University of Hong Kong · CNVA Palo Alto Health Care System · US

Funding

Dual Modality X-ray Luminescence CT for in vivo Cancer ImagingR01CA227713 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2019 to 2023
$2.9M
NCI NIH HHS R01 CA227713
6 · The paper itself

Abstract

Adequate tumor detection is critical in complete transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence, but up to 20% of bladder tumors are missed by standard white light cystoscopy. Deep learning augmented cystoscopy may improve tumor localization, intraoperative navigation, and surgical resection of bladder cancer. We aimed to develop a deep learning algorithm for augmented cystoscopic detection of bladder cancer. Patients undergoing cystoscopy/TURBT were recruited and white light videos were recorded. Video frames containing histologically confirmed papillary urothelial carcinoma were selected and manually annotated. We constructed CystoNet, an image analysis platform based on convolutional neural networks, for automated bladder tumor detection using a development dataset of 95 patients for algorithm training and five patients for testing. Diagnostic performance of CystoNet was validated prospectively in an additional 54 patients. In the validation dataset, per-frame sensitivity and specificity were 90.9% (95% confidence interval [CI], 90.3-91.6%) and 98.6% (95% CI, 98.5-98.8%), respectively. Per-tumor sensitivity was 90.9% (95% CI, 90.3-91.6%). CystoNet detected 39 of 41 papillary and three of three flat bladder cancers. With high sensitivity and specificity, CystoNet may improve the diagnostic yield of cystoscopy and efficacy of TURBT. PATIENT SUMMARY: Conventional cystoscopy has recognized shortcomings in bladder cancer detection, with implications for recurrence. Cystoscopy augmented with artificial intelligence may improve cancer detection and resection.

Indexed as

Deep LearningCarcinoma, Transitional CellCystoscopyHumansUrinary Bladder NeoplasmsBladder cancerComputer-assisted image analysisCystoscopyDeep learningDiagnostic imaging

Identifiers

PMID31537407
PMCPMC6889816
OpenAlexW2974120146

What OpenQuestion holds

Textmetadata
LicenceTDM
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.