Evidence map›Paper›PMID 32001164›Full record

ArticleAcademic radiology2020

Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance.

Vignesh Selvakumaran, Rui Hou, Jay A Baker, Sora C Yoon, Sujata V Ghate, Ruth Walsh, Tyler P Litton, Lucy Xiangxi Lu, Amrita Devalapalli, Connie Kim and 4 more

Abstract read
In one paragraph

Article in Academic radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

14 authors.

Vignesh SelvakumaranDuke Medical School, DUMC 3878, Durham, North Carolina.
Rui HouDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Jay A BakerDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Sora C YoonDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Sujata V GhateDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Ruth WalshDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Tyler P LittonDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Lucy Xiangxi LuDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Amrita DevalapalliDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Connie KimDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Mary Scott SooDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
E Shelley HwangDepartment of Surgery, Duke University Medical Center, Durham, North Carolina.
Joseph Y LoDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710.
Lars J GrimmDepartment of Radiology, Duke University Medical Center, DUMC Box 3808, Durham, NC, 27710. Electronic address: lars.grimm@duke.edu.

Funding

(PQC3) Genomic Diversity and Microenvironment as Drivers of Metastasis in DCISR01CA185138 · NCI · DUKE UNIVERSITY · PI HWANG, E.SHELLEY, MALEY, CARLO · 2014 to 2017
$2.0M
NCI NIH HHS R01 CA185138
6 · The paper itself

Abstract

RATIONALE AND

objectivesThe purpose of this study is to quantify breast radiologists' performance at predicting occult invasive disease when ductal carcinoma in situ (DCIS) presents as calcifications on mammography and to identify imaging and histopathological features that are associated with radiologists' performance. MATERIALS AND

methodsMammographically detected calcifications that were initially diagnosed as DCIS on core biopsy and underwent definitive surgical excision between 2010 and 2015 were identified. Thirty cases of suspicious calcifications upstaged to invasive ductal carcinoma and 120 cases of DCIS confirmed at the time of definitive surgery were randomly selected. Nuclear grade, estrogen and progesterone receptor status, patient age, calcification long axis length, and breast density were collected. Ten breast radiologists who were blinded to all clinical and pathology data independently reviewed all cases and estimated the likelihood that the DCIS would be upstaged to invasive disease at surgical excision. Subgroup analysis was performed based on nuclear grade, long axis length, breast density and after exclusion of microinvasive disease.

resultsReader performance to predict upstaging ranged from an area under the receiver operating characteristic curve (AUC) of 0.541-0.684 with a mean AUC of 0.620 (95%CI: 0.489-0.751). Performances improved for lesions smaller than 2 cm (AUC: 0.676 vs 0.500; p = 0.002). The exclusion of microinvasive cases also improved performance (AUC: 0.651 vs 0.620; p = 0.005). There was no difference in performance based on breast density (p = 0.850) or nuclear grade (p = 0.270)

conclusionRadiologists were able to predict invasive disease better than chance, particularly for smaller DCIS lesions (<2 cm) and after the exclusion of microinvasive disease.

Indexed as

Breast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, Intraductal, NoninfiltratingHumansMammographyNeoplasm InvasivenessRadiologistsRetrospective StudiesBreast cancerCalcificationsDuctal carcinoma in situMammographyUpstaging

Identifiers

PMID32001164
PMCPMC7382977

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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.