Evidence map›Paper›PMID 41701890›Full record

ArticleCanadian Urological Association journal = Journal de l'Association des urologues du Canada2026

Automated vs. manual segmentation for small renal mass imaging.

Kristen McAlpine, Nikhil Mirajkar, Dominik Deniffel, Andres Kohan, Satheesh Krishna, Girish Kulkarni, Emily Seto, Antonio Finelli, Masoom A Haider

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Article in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2026. 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

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

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

9 authors.

Kristen McAlpineDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Nikhil MirajkarDepartment of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdon.
Dominik DeniffelDepartment of Diagnostic and Interventional Radiology, Cantonal Hospital Frauenfeld, Frauenfeld, Switzerland.
Andres KohanJoint Department of Medical Imaging, Sinai Health System, Princess Margaret Hospital, University of Toronto, Toronto, ON, Canada.
Satheesh KrishnaJoint Department of Medical Imaging, Sinai Health System, Princess Margaret Hospital, University of Toronto, Toronto, ON, Canada.
Girish KulkarniDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Emily SetoInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Antonio FinelliDivision of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Masoom A HaiderJoint Department of Medical Imaging, Sinai Health System, Princess Margaret Hospital, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAutomated segmentation using artificial intelligence (AI) has the potential to rapidly perform three-dimensional (3D) segmentation of small renal masses (SRM). The objective of this study was to test for clinically and statistically significant differences in time spent segmenting, accuracy, and reliability when comparing manual and automated segmentation of computed tomography (CT) scans with SRM.

methodsPatients with a CT scan, SRM <4 cm, and renal neoplasm were identified through an institutional database. Of the 854 patients identified, 184 were excluded. Forty test cases were randomly selected. There were 630 cases for training (using nnU-Net) to which 488 cases from the KiTS23 open-source dataset were added. Each of the test cases was segmented by a radiologist, a urologist, and the AI model. Time to segment and Dice coefficients were compared. Deidentified segmented CTs were provided to two independent radiologists, who attempted to identify the segmentor and rated the acceptability of the segmented images on a five-point Likert scale.

resultsThere were 39 cases with complete timing data. The median time for the AI model to segment was one-third of the radiologist's (152.4 s, interquartile range [IQR] 120.9-177.8 vs. 450 s, IQR 318.8-551.2) and about one-fifth of the urologist's (800.0 s, IQR 492.0-1538.0) (p<0.001). There was a high degree of inter-rater reliability (median Dice coefficients 0.86-0.90, p=0.09). The scoring radiologists were able to correctly identify the true segmentor in 61.6% of cases (p <0.001). The AI segmentations were scored highest among the three segmentors (median score 4.1/5, standard deviation [SD] 1.0) compared to 3.8 (SD 0.7) for the radiologist, and 3.3 (SD 0.7) for the urologist.

conclusionsAutomated segmentation of CT scans for patients with SRM was efficient, accurate, and acceptable in this study. This approach has the potential to greatly improve the clinical use of radiomics to assess medical images for these patients.

Identifiers

PMID41701890
PMCPMC13225122

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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.