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Breast cancers missed during screening in a tertiary-care hospital mammography facility.

ABSTRACT: BACKGROUND:Breast cancer is the most common cancer in females worldwide. Screening with mammography for early breast cancer detection is standard community practice in many countries. OBJECTIVE:Identify causes of missed breast cancers during screening. DESIGN:Retrospective, observational. SETTING:Department of radiology at a tertiary-care hospital mammographic screening facility. PATIENTS AND METHODS:All women who came with initial negative screens from July 2015 to July 2018 were retrospectively reviewed and followed-up for their second or subsequent mammographic screening. Missed breast cancer was defined as a cancer that was detected on a subsequent mammogram with an initial negative screen. Mammograms were interpreted by two radiologists as per BIRADS (Breast Imaging Reporting and Data System) lexicon. Causes of missed breast cancers were categorized as imaging acquisition (IA), imaging feature (IF) and imaging interpretation (II). True (occult) incident breast cancers were also documented. Percentage estimations for these causes were calculated. MAIN OUTCOME MEASURES:Breast cancer detection on follow-up screening. SAMPLE SIZE:943 women. RESULTS:Of 15 (1.6%) screening-detected breast cancers, 7 cases (46.6%) were missed on the initial screen; 3 (43%) of these were II related, 2 (28.5%) of each were IA and IF. The remaining true (occult) cases were detected on either the second (5 cases) or third screens (3 cases). CONCLUSION:Improved screening facilities, quality mammographic acquisition and interpretation, double reading, and implementation of an organized screening program may help to avoid missed breast cancers. LIMITATIONS:Retrospective, small sample, single center, and short duration study. CONFLICT OF INTEREST:None.

SUBMITTER: Waheed KB

PROVIDER: S-EPMC6838646 | biostudies-literature | 2019 Jul-Aug

REPOSITORIES: biostudies-literature

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Breast cancers missed during screening in a tertiary-care hospital mammography facility.

Waheed Khawaja Bilal KB Hassan Muhammad Zia Ul MZU Hassan Donya Al DA Shamrani Alaa Ali Ghaithan Al AAGA Bassam Muneera Al MA Elbyali Ahmed Aly AA Shams Tamer Mohamed TM Demiati Zainab Ahmed ZA Arulanatham Zechriah Jebakumar ZJ

Annals of Saudi medicine 20190701 4

<h4>Background</h4>Breast cancer is the most common cancer in females worldwide. Screening with mammography for early breast cancer detection is standard community practice in many countries.<h4>Objective</h4>Identify causes of missed breast cancers during screening.<h4>Design</h4>Retrospective, observational.<h4>Setting</h4>Department of radiology at a tertiary-care hospital mammographic screening facility.<h4>Patients and methods</h4>All women who came with initial negative screens from July 2 ...[more]

PMID: 31381361

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Project description:PurposeTo investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT.Materials and methodsIn this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics.ResultsThe highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18).ConclusionAlmost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.

Project description:To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists' sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with a panel of seven radiologists using a cancer-enriched data set from 122 patients that included 90 false-negative mammograms obtained up to 5.8 years prior to diagnosis and 32 BIRADS 1 and 2 patients with a 2-year follow-up of negative diagnosis. The mammograms were performed between February 7, 2008 (earliest) and January 8, 2016 (latest), and were all originally interpreted as negative in conjunction with R2 ImageChecker CAD, version 10.0. In this study, the readers analyzed the 122 studies before and after review of cmAssist™, an AI-CAD software for mammography. The statistical significance of our findings was evaluated using Student's t test and bootstrap statistical analysis. There was a substantial and significant improvement in radiologist accuracy with use of cmAssist, as demonstrated in the 7.2% increase in the area-under-the-curve (AUC) of the receiver operating characteristic (ROC) curve with two-sided p value?<?0.01 for the reader group. All radiologists showed a significant improvement in their cancer detection rate (CDR) with the use of cmAssist (two-sided p value?=?0.030, confidence interval?=?95%). The readers detected between 25 and 71% (mean 51%) of the early cancers without assistance. With cmAssist, the overall reader CDR was 41 to 76% (mean 62%). The percentage increase in CDR for the reader panel was significant, ranging from 6 to 64% (mean 27%) with the use of cmAssist. There was less than 1% increase in the readers' false-positive recalls with use of cmAssist. With the use of cmAssist TM, there was a substantial and statistically significant improvement in radiologists' accuracy and sensitivity for detection of cancers that were originally missed. The percentage increase in CDR for the radiologists in the reader panel ranged from 6 to 64% (mean 27%) with the use of cmAssist, with negligible increase in false-positive recalls.

Dataset Information

Breast cancers missed during screening in a tertiary-care hospital mammography facility.

Publications

Breast cancers missed during screening in a tertiary-care hospital mammography facility.

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