Literature DB >> 25961633

Breast Cancer: Computer-aided Detection with Digital Breast Tomosynthesis.

Lia Morra1, Daniela Sacchetto1, Manuela Durando1, Silvano Agliozzo1, Luca Alessandro Carbonaro1, Silvia Delsanto1, Barbara Pesce1, Diego Persano1, Giovanna Mariscotti1, Vincenzo Marra1, Paolo Fonio1, Alberto Bert1.   

Abstract

PURPOSE: To evaluate a commercial tomosynthesis computer-aided detection (CAD) system in an independent, multicenter dataset.
MATERIALS AND METHODS: Diagnostic and screening tomosynthesis mammographic examinations (n = 175; cranial caudal and mediolateral oblique) were randomly selected from a previous institutional review board-approved trial. All subjects gave informed consent. Examinations were performed in three centers and included 123 patients, with 132 biopsy-proven screening-detected cancers, and 52 examinations with negative results at 1-year follow-up. One hundred eleven lesions were masses and/or microcalcifications (72 masses, 22 microcalcifications, 17 masses with microcalcifications) and 21 were architectural distortions. Lesions were annotated by radiologists who were aware of all available reports. CAD performance was assessed as per-lesion sensitivity and false-positive results per volume in patients with negative results.
RESULTS: Use of the CAD system showed per-lesion sensitivity of 89% (99 of 111; 95% confidence interval: 81%, 94%), with 2.7 ± 1.8 false-positive rate per view, 62 of 72 lesions detected were masses, 20 of 22 were microcalcification clusters, and 17 of 17 were masses with microcalcifications. Overall, 37 of 39 microcalcification clusters (95% sensitivity, 95% confidence interval: 81%, 99%) and 79 of 89 masses (89% sensitivity, 95% confidence interval: 80%, 94%) were detected with the CAD system. On average, 0.5 false-positive rate per view were microcalcification clusters, 2.1 were masses, and 0.1 were masses and microcalcifications.
CONCLUSION: A digital breast tomosynthesis CAD system can allow detection of a large percentage (89%, 99 of 111) of breast cancers manifesting as masses and microcalcification clusters, with an acceptable false-positive rate (2.7 per breast view). Further studies with larger datasets acquired with equipment from multiple vendors are needed to replicate the findings and to study the interaction of radiologists and CAD systems. (©) RSNA, 2015.

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Year:  2015        PMID: 25961633     DOI: 10.1148/radiol.2015141959

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  10 in total

1.  Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

Authors:  Ravi K Samala; Heang-Ping Chan; Lubomir Hadjiiski; Mark A Helvie; Jun Wei; Kenny Cha
Journal:  Med Phys       Date:  2016-12       Impact factor: 4.071

2.  The changing face of cancer diagnosis: From computational image analysis to systems biology.

Authors:  Fabian Kiessling
Journal:  Eur Radiol       Date:  2018-02-27       Impact factor: 5.315

3.  Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

Authors:  Kayla Mendel; Hui Li; Deepa Sheth; Maryellen Giger
Journal:  Acad Radiol       Date:  2018-08-01       Impact factor: 3.173

4.  Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.

Authors:  Ravi K Samala; Lubomir Hadjiiski; Mark A Helvie; Caleb D Richter; Kenny H Cha
Journal:  IEEE Trans Med Imaging       Date:  2019-03       Impact factor: 10.048

5.  The ANDROMEDA prospective cohort study: predictive value of combined criteria to tailor breast cancer screening and new opportunities from circulating markers: study protocol.

Authors:  Livia Giordano; Federica Gallo; Elisabetta Petracci; Giovanna Chiorino; Nereo Segnan
Journal:  BMC Cancer       Date:  2017-11-22       Impact factor: 4.430

6.  Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study.

Authors:  Liang Yongping; Zhang Juan; Ping Zhou; Zhao Yongfeng; Wengang Liu; Yifan Shi
Journal:  JMIR Med Inform       Date:  2020-05-05

7.  Current Available Computer-Aided Detection Catches Cancer but Requires a Human Operator.

Authors:  Florentino Saenz Rios; Giri Movva; Hari Movva; Quan D Nguyen
Journal:  Cureus       Date:  2020-12-19

8.  YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings.

Authors:  Alexey Kolchev; Dmitry Pasynkov; Ivan Egoshin; Ivan Kliouchkin; Olga Pasynkova; Dmitrii Tumakov
Journal:  J Imaging       Date:  2022-03-24

Review 9.  Synthesized Mammography: Clinical Evidence, Appearance, and Implementation.

Authors:  Melissa A Durand
Journal:  Diagnostics (Basel)       Date:  2018-04-04

10.  Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network.

Authors:  Bingbing Xiao; Haotian Sun; You Meng; Yunsong Peng; Xiaodong Yang; Shuangqing Chen; Zhuangzhi Yan; Jian Zheng
Journal:  Biomed Eng Online       Date:  2021-07-28       Impact factor: 2.819

  10 in total

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