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Giorgi Rossi P, Mancuso P, Pattacini P, Campari C, Nitrosi A, Iotti V, Ponti A, Frigerio A, Correale L, Riggi E, Giordano L, Segnan N, Di Leo G, Magni V, Sardanelli F, Fornasa F, Romanucci G, Montemezzi S, Falini P, Auzzi N, Zappa M, Ottone M, Mantellini P, Duffy SW, Armaroli P, Coriani C, Pescarolo M, Stefanelli G, Tondelli G, Beretti F, Caffarri S, Marchesi V, Canovi L, Colli M, Boschini M, Bertolini M, Ragazzi M, Pattacini P, Giorgi Rossi P, Iotti V, Ginocchi V, Ravaioli S, Vacondio R, Campari C, Caroli S, Nitrosi A, Braglia L, Cavuto S, Mancuso P, Djuric O, Venturelli F, Vicentini M, Braghiroli MB, Lonetti J, Davoli E, Bonelli E, Fornasa F, Montemezzi S, Romanucci G, Lucchi I, Martello G, Rossati C, Mantellini P, Ambrogetti D, Iossa A, Carnesciali E, Mazzalupo V, Falini P, Puliti D, Zappa M, Battisti F, Auzzi N, Verdi S, Degl'Innocenti C, Tramalloni D, Cavazza E, Busoni S, Betti E, Peruzzi F, Regini F, Sardanelli F, Di Leo G, Carbonaro LA, Magni V, Cozzi A, Spinelli D, Monaco CG, Schiaffino S, Benedek A, Menicagli L, Ferraris R, Favettini E, Dettori D, Falco P, Presti P, Segnan N, Ponti A, Frigerio A, Armaroli P, Correale L, Marra V, Milanesio L, Artuso F, Di Leo A, Castellano I, Riggi E, Casella D, Pitarella S, Vergini V, Giordano L, Duffy SW, Graewingholt A, Lang K, Falcini F. Comparing accuracy of tomosynthesis plus digital mammography or synthetic 2D mammography in breast cancer screening: baseline results of the MAITA RCT consortium. Eur J Cancer 2024; 199:113553. [PMID: 38262307 DOI: 10.1016/j.ejca.2024.113553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/01/2024] [Accepted: 01/06/2024] [Indexed: 01/25/2024]
Abstract
AIM The analyses here reported aim to compare the screening performance of digital tomosynthesis (DBT) versus mammography (DM). METHODS MAITA is a consortium of four Italian trials, REtomo, Proteus, Impeto, and MAITA trial. The trials adopted a two-arm randomised design comparing DBT plus DM (REtomo and Proteus) or synthetic-2D (Impeto and MAITA trial) versus DM; multiple vendors were included. Women aged 45 to 69 years were individually randomised to one round of DBT or DM. FINDINGS From March 2014 to February 2022, 50,856 and 63,295 women were randomised to the DBT and DM arm, respectively. In the DBT arm, 6656 women were screened with DBT plus synthetic-2D. Recall was higher in the DBT arm (5·84% versus 4·96%), with differences between centres. With DBT, 0·8/1000 (95% CI 0·3 to 1·3) more women received surgical treatment for a benign lesion. The detection rate was 51% higher with DBT, ie. 2·6/1000 (95% CI 1·7 to 3·6) more cancers detected, with a similar relative increase for invasive cancers and ductal carcinoma in situ. The results were similar below and over the age of 50, at first and subsequent rounds, and with DBT plus DM and DBT plus synthetic-2D. No learning curve was appreciable. Detection of cancers >= 20 mm, with 2 or more positive lymph nodes, grade III, HER2-positive, or triple-negative was similar in the two arms. INTERPRETATION Results from MAITA confirm that DBT is superior to DM for the detection of cancers, with a possible increase in recall rate. DBT performance in screening should be assessed locally while waiting for long-term follow-up results on the impact of advanced cancer incidence.
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Affiliation(s)
| | | | | | - Cinzia Campari
- Screening coordinating centre, AUSL - IRCCS di Reggio Emilia, Italy
| | - Andrea Nitrosi
- Medical Physics unit, AUSL - IRCCS di Reggio Emilia, Italy
| | | | - Antonio Ponti
- SSD Epidemiologia e Screening. AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | - Alfonso Frigerio
- SSD Senologia di Screening AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | - Loredana Correale
- SSD Epidemiologia e Screening. AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | - Emilia Riggi
- SSD Epidemiologia e Screening. AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | - Livia Giordano
- SSD Epidemiologia e Screening. AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | - Nereo Segnan
- SSD Epidemiologia e Screening. AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | - Giovanni Di Leo
- IRCC Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
| | - Veronica Magni
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Francesco Sardanelli
- IRCC Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Francesca Fornasa
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, Via Circonvallazione, 1, 37047 San Bonifacio, VR, Italy
| | - Giovanna Romanucci
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, Via Circonvallazione, 1, 37047 San Bonifacio, VR, Italy
| | | | - Patrizia Falini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Firenze, Italy
| | - Noemi Auzzi
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Firenze, Italy
| | - Marco Zappa
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Firenze, Italy
| | - Marta Ottone
- Epidemiology Unit, AUSL - IRCCS di Reggio Emilia, Italy
| | - Paola Mantellini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Firenze, Italy
| | - Stephen W Duffy
- Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Paola Armaroli
- SSD Epidemiologia e Screening. AOU Città della Salute e della Scienza, CPO Piemonte Torino, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Francesca Fornasa
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, Via Circonvallazione, 1, 37047 San Bonifacio, VR, Italy
| | | | - Giovanna Romanucci
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, Via Circonvallazione, 1, 37047 San Bonifacio, VR, Italy
| | - Ilaria Lucchi
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, Via Circonvallazione, 1, 37047 San Bonifacio, VR, Italy
| | - Gessica Martello
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, Via Circonvallazione, 1, 37047 San Bonifacio, VR, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Giovanni Di Leo
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | | | - Veronica Magni
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Andrea Cozzi
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Diana Spinelli
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | | | | | - Adrienn Benedek
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Laura Menicagli
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Axel Graewingholt
- Mammographiescreening-Zentrum Paderborn, Breast Cancer Screening, Paderborn, NRW, Germany
| | - Kristina Lang
- Departement of Translational Medicine, Lund University, Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden
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Chan HP, Helvie MA, Gao M, Hadjiiski L, Zhou C, Garver K, Klein KA, McLaughlin C, Oudsema R, Rahman WT, Roubidoux MA. Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images. Med Phys 2023; 50:6177-6189. [PMID: 37145996 PMCID: PMC10592580 DOI: 10.1002/mp.16439] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). PURPOSE We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT. METHODS We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250 mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. RESULTS The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p < 0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9 ± 4.6, 2.8 ± 3.7, and 2.7 ± 3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p ≤ 0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. CONCLUSIONS This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.
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Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mark A Helvie
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mingjie Gao
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kim Garver
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Katherine A Klein
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Carol McLaughlin
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Rebecca Oudsema
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - W Tania Rahman
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
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