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1. Towards Out-of-Distribution Detection for Breast Cancer Classification in Point-of-Care Ultrasound Imaging

Author

Karlsson, Jennie, Wodrich, Marisa, Overgaard, Niels Christian, Sahlin, Freja, Lång, Kristina, Heyden, Anders, Arvidsson, Ida, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antonacopoulos, Apostolos, editor, Chaudhuri, Subhasis, editor, Chellappa, Rama, editor, Liu, Cheng-Lin, editor, Bhattacharya, Saumik, editor, and Pal, Umapada, editor

Published
2025
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2. Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging

Author

Karlsson, Jennie, Wodrich, Marisa, Overgaard, Niels Christian, Sahlin, Freja, Lång, Kristina, Heyden, Anders, and Arvidsson, Ida

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guaranteed. Detecting out-of-distribution (OOD) samples is a crucial step towards building a safe classifier. Following a previous study, showing that it is possible to classify breast cancer in point-of-care ultrasound images, this study investigates OOD detection using three different methods: softmax, energy score and deep ensembles. All methods are tested on three different OOD data sets. The results show that the energy score method outperforms the softmax method, performing well on two of the data sets. The ensemble method is the most robust, performing the best at detecting OOD samples for all three OOD data sets.

Published
2024

7. Comparing accuracy of tomosynthesis plus digital mammography or synthetic 2D mammography in breast cancer screening: baseline results of the MAITA RCT consortium

Author

Giorgi Rossi, Paolo, Mancuso, Pamela, Pattacini, Pierpaolo, Campari, Cinzia, Nitrosi, Andrea, Iotti, Valentina, Ponti, Antonio, Frigerio, Alfonso, Correale, Loredana, Riggi, Emilia, Giordano, Livia, Segnan, Nereo, Di Leo, Giovanni, Magni, Veronica, Sardanelli, Francesco, Fornasa, Francesca, Romanucci, Giovanna, Montemezzi, Stefania, Falini, Patrizia, Auzzi, Noemi, Zappa, Marco, Ottone, Marta, Mantellini, Paola, Duffy, Stephen W., Armaroli, Paola, Coriani, Chiara, Pescarolo, Manuela, Stefanelli, Gloria, Tondelli, Giulio, Beretti, Filippo, Caffarri, Sabrina, Marchesi, Vanessa, Canovi, Laura, Colli, Marco, Boschini, Mirco, Bertolini, Marco, Ragazzi, Moira, Ginocchi, Vladimiro, Ravaioli, Sara, Vacondio, Rita, Caroli, Stefania, Braglia, Luca, Cavuto, Silvio, Mancuso., Pamela, Djuric, Olivera, Venturelli, Francesco, Vicentini, Massimo, Braghiroli, Maria Barbara, Lonetti, Jasmine, Davoli, Elena, Bonelli, Efrem, Lucchi, Ilaria, Martello, Gessica, Rossati, Claudia, Ambrogetti, Daniela, Iossa, Anna, Carnesciali, Eva, Mazzalupo, Vincenzo, Puliti, Donella, Battisti, Francesca, Auzzi., Noemi, Verdi, Simona, Degl’Innocenti, Carolina, Tramalloni, Daniela, Cavazza, Elena, Busoni, Simone, Betti, Elisa, Peruzzi, Francesca, Regini, Francesco, Sardanelli., Francesco, Di Leo., Giovanni, Carbonaro, Luca Alessandro, Magni., Veronica, Cozzi, Andrea, Spinelli, Diana, Monaco, Cristian Giuseppe, Schiaffino, Simone, Benedek, Adrienn, Menicagli, Laura, Ferraris, Raffaella, Favettini, Elisabetta, Dettori, Davide, Falco, Paolo, Presti, Pietro, Segnan., Nereo, Armaroli., Paola, Marra, Vincenzo, Milanesio, Luisella, Artuso, Franca, Di Leo, Aurora, Castellano, Isabella, Riggi., Emilia, Casella, Denise, Pitarella, Sabina, Vergini, Viviana, Graewingholt, Axel, Lang, Kristina, and Falcini, Fabio

Published
2024
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12. Breast Imaging

Author

Lång, Kristina, Sklair Levy, Miri, Kaidar-Person, Orit, editor, Meattini, Icro, editor, and Poortmans, Philip, editor

Published
2022
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20. Towards clinical grating-interferometry mammography

Author

Arboleda, Carolina, Wang, Zhentian, Jefimovs, Konstantins, Koehler, Thomas, Van Stevendaal, Udo, Kuhn, Norbert, David, Bernd, Prevrhal, Sven, Lång, Kristina, Forte, Serafino, Kubik-Huch, Rahel Antonia, Leo, Cornelia, Singer, Gad, Marcon, Magda, Boss, Andreas, Roessl, Ewald, and Stampanoni, Marco

Published
2020
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22. Breast Density Assessment Using Breast Tomosynthesis Images

Author

Timberg, Pontus, Fieselmann, Andreas, Dustler, Magnus, Petersson, Hannie, Sartor, Hanna, Lång, Kristina, Förnvik, Daniel, Zackrisson, Sophia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tingberg, Anders, editor, Lång, Kristina, editor, and Timberg, Pontus, editor

Published
2016
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23. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

Author

Rodriguez-Ruiz, Alejandro, Lång, Kristina, Gubern-Merida, Albert, Teuwen, Jonas, Broeders, Mireille, Gennaro, Gisella, Clauser, Paola, Helbich, Thomas H., Chevalier, Margarita, Mertelmeier, Thomas, Wallis, Matthew G., Andersson, Ingvar, Zackrisson, Sophia, Sechopoulos, Ioannis, and Mann, Ritse M.

Published
2019
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24. AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis

Author

Larsen, Marthe, primary, Olstad, Camilla F., additional, Koch, Henrik W., additional, Martiniussen, Marit A., additional, Hoff, Solveig R., additional, Lund-Hanssen, Håkon, additional, Solli, Helene S., additional, Mikalsen, Karl Øyvind, additional, Auensen, Steinar, additional, Nygård, Jan, additional, Lång, Kristina, additional, Chen, Yan, additional, and Hofvind, Solveig, additional

Published
2023
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25. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study

Author

Lång, Kristina, primary, Josefsson, Viktoria, additional, Larsson, Anna-Maria, additional, Larsson, Stefan, additional, Högberg, Charlotte, additional, Sartor, Hanna, additional, Hofvind, Solveig, additional, Andersson, Ingvar, additional, and Rosso, Aldana, additional

Published
2023
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30. Mammographic density and interval cancers in mammographic screening:Moving towards more personalized screening

Author

Larsen, Marthe, Lynge, Elsebeth, Lee, Christoph I., Lång, Kristina, Hofvind, Solveig, Larsen, Marthe, Lynge, Elsebeth, Lee, Christoph I., Lång, Kristina, and Hofvind, Solveig

Abstract

Purpose: The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. Methods: Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1–4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%–7.4% as VDG2, 7.5%–15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. Results: Median time from screening to interval cancer was 496 (IQR: 391–587) days for VDG1, 500 (IQR: 350–616) for VDG2, 482 (IQR: 309–595) for VDG3 and 427 (IQR: 266–577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. Conclusions: Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible.

Published
2023

31. Anticipating Artificial Intelligence in Mammography Screening : Views of Swedish Breast Radiologists

Author

Högberg, Charlotte, Larsson, Stefan, Lång, Kristina, Högberg, Charlotte, Larsson, Stefan, and Lång, Kristina

Abstract

Objectives: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession.Methods:We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI’s impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach.Results:Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making.Conclusions:Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

Published
2023

32. Breast cancer detection accuracy of AI in an entire screening population:a retrospective, multicentre study

Author

Elhakim, Mohammad Talal, Stougaard, Sarah Wordenskjold, Graumann, Ole, Nielsen, Mads, Lång, Kristina, Gerke, Oke, Larsen, Lisbet Brønsro, Rasmussen, Benjamin Schnack Brandt, Elhakim, Mohammad Talal, Stougaard, Sarah Wordenskjold, Graumann, Ole, Nielsen, Mads, Lång, Kristina, Gerke, Oke, Larsen, Lisbet Brønsro, and Rasmussen, Benjamin Schnack Brandt

Abstract

Background: Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population. Methods: We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AIsens) and specificity (AIspec). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar’s test or exact binomial test. Results: Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AIsens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AIspec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AIsens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as hig

Published
2023

34. Malmö Breast ImaginG database: objectives and development

Author

Dahlblom, Victor, primary, Dustler, Magnus, additional, Bolejko, Anetta, additional, Bakic, Predrag R., additional, Granberg, Henrik, additional, Johnson, Kristin, additional, Förnvik, Daniel, additional, Lång, Kristina, additional, Tingberg, Anders, additional, and Zackrisson, Sophia, additional

Published
2023
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36. Differences in Radiologists’ Experiences and Performance in Breast Tomosynthesis

Author

Svahn, Tony, Lång, Kristina, Andersson, Ingvar, Zackrisson, Sophia, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Maidment, Andrew D. A., editor, Bakic, Predrag R., editor, and Gavenonis, Sara, editor

Published
2012
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37. Correction to: Towards clinical grating-interferometry mammography

Author

Arboleda, Carolina, Wang, Zhentian, Jefimovs, Konstantins, Koehler, Thomas, Van Stevendaal, Udo, Kuhn, Norbert, David, Bernd, Prevrhal, Sven, Lång, Kristina, Forte, Serafino, Kubik-Huch, Rahel Antonia, Leo, Cornelia, Singer, Gad, Marcon, Magda, Boss, Andreas, Roessl, Ewald, and Stampanoni, Marco

Published
2020
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38. Effects of tamoxifen on normal breast tissue histological composition: Results from a randomised six‐arm placebo‐controlled trial in healthy women

Author

Gabrielson, Marike, primary, Hammarström, Mattias, additional, Bäcklund, Magnus, additional, Bergqvist, Jenny, additional, Lång, Kristina, additional, Rosendahl, Ann H, additional, Borgquist, Signe, additional, Hellgren, Roxanna, additional, Czene, Kamila, additional, and Hall, Per, additional

Published
2023
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39. The Safety of an Artificial Intelligence Supported Screen-Reading Procedure Versus Standard Double Reading in the Mammography Screening with Artificial Intelligence (MASAI) Trial: A Randomised, Controlled, Screening Accuracy Study

Author

Lång, Kristina, primary, Josefsson, Viktoria, additional, Larsson, Anna-Maria, additional, Larsson, Stefan, additional, Högberg, Charlotte, additional, Sartor, Hanna, additional, Hofvind, Solveig, additional, Andersson, Ingvar, additional, and Rosso, Aldana, additional

Published
2023
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40. Mammographic calcifications undergoing percutaneous biopsy: outcome in women with and without a personal history of breast cancer

Author

Bertani, Valeria, Berger, Nicole, Eberhard, Matthias, Lång, Kristina, Urbani, Martina, La Grassa, Manuela, Balestreri, Luca, Boss, Andreas, Frauenfelder, Thomas, Marcon, Magda, and University of Zurich

Subjects
10042 Clinic for Diagnostic and Interventional Radiology, 610 Medicine & health, Radiology, Nuclear Medicine and imaging, General Medicine
Abstract

Purpose To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC). Materials and methods In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann–Whitney U tests and Chi-square tests. Results Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50–65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P = .715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1–2 years (25%, 1 of 4), 2–5 years (20%, 2 of 10) and 5–10 years (0%, of 13) from the first breast cancer (P = .005). Conclusion PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years.

Published
2022

48. Low-dose tamoxifen for mammographic density reduction:A randomized controlled trial

Author

Eriksson, Mikael, Eklund, Martin, Borgquist, Signe, Hellgren, Roxanna, Margolin, Sara, Thoren, Linda, Rosendahl, Ann, Lång, Kristina, Tapia, José, Bäcklund, Magnus, Discacciati, Andrea, Crippa, Alessio, Gabrielson, Marike, Hammarström, Mattias, Wengström, Yvonne, Czene, Kamila, and Hall, Per

Subjects
CHEMOPREVENTION, Adult, Breast Density/drug effects, WOMEN, Middle Aged, THERAPY, Tamoxifen/administration & dosage, HIGH-RISK, Double-Blind Method, Antineoplastic Agents, Hormonal/administration & dosage, Humans, BREAST-CANCER PREVENTION, Female, Breast Neoplasms/prevention & control, FOLLOW-UP, Early Detection of Cancer, Mammography, Aged
Abstract

PURPOSE Tamoxifen prevents breast cancer in high-risk women and reduces mortality in the adjuvant setting. Mammographic density change is a proxy for tamoxifen therapy response. We tested whether lower doses of tamoxifen were noninferior to reduce mammographic density and associated with fewer symptoms. PATIENTS AND METHODS Women, 40-74 years of age, participating in the Swedish mammography screening program were invited to the 6-month double-blind six-arm randomized placebo-controlled noninferiority dose-determination KARISMA phase II trial stratified by menopausal status (EudraCT 2016-000882-22). In all, 1,439 women were accrued with 1,230 participants accessible for intention-to-treat analysis. The primary outcome was proportion of women treated with placebo, 1, 2.5, 5, and 10 mg whose mammographic density decreased at least as much as the median reduction in the 20 mg arm. The noninferior margin was 17%. Secondary outcome was reduction of symptoms. Post hoc analyses were performed by menopausal status. Per-protocol population and full population were analyzed in sensitivity analysis. RESULTS The 1,439 participants, 566 and 873 pre- and postmenopausal women, respectively, were recruited between October 1, 2016, and September 30, 2019. The participants had noninferior mammographic density reduction following 2.5, 5, and 10 mg tamoxifen compared with the median 10.1% decrease observed in the 20 mg group, a reduction confined to premenopausal women. Severe vasomotor symptoms (hot flashes, cold sweats, and night sweats) were reduced by approximately 50% in the 2.5, 5, and 10 mg groups compared with the 20 mg group. CONCLUSION Premenopausal women showed noninferior magnitude of breast density decrease at 2.5 mg of tamoxifen, but fewer side effects compared with the standard dose of 20 mg. Future studies should test whether 2.5 mg of tamoxifen reduces the risk of primary breast cancer.

Published
2021
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