Marcel Blum

Last Name

Blum

First name

Marcel

marcel.blum@unisg.ch

ORCID

0009-0008-9243-3896

Now showing 1 - 8 of 8

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Retrospective evaluation of interval breast cancer screening mammograms by radiologists and AI
(Springer Science and Business Media LLC, 2025-08-04)
Jonas Subelack
;
Morant, Rudolf
;
Marcel Blum
;
Gräwingholt, Axel
;
Justus Vogel
Objectives To determine whether an AI system can identify breast cancer risk in interval breast cancer (IBC) screening mammograms. Materials and methods IBC screening mammograms from a Swiss screening program were retrospectively analyzed by radiologists/an AI system. Radiologists determined whether the IBC mammogram showed human visible signs of breast cancer (potentially missed IBCs) or not (IBCs without retrospective abnormalities). The AI system provided a case score and a prognostic risk category per mammogram. Results 119 IBC cases (mean age 57.3 (5.4)) were available with complete retrospective evaluations by radiologists/the AI system. 82 (68.9%) were classified as IBCs without retrospective abnormalities and 37 (31.1%) as potentially missed IBCs. 46.2% of all IBCs received a case score ≥ 25, 25.2% ≥ 50, and 13.4% ≥ 75. Of the 25.2% of the IBCs ≥ 50 (vs. 13.4% of a no breast cancer population), 45.2% had not been discussed during a consensus conference, reflecting 11.4% of all IBC cases. The potentially missed IBCs received significantly higher case scores and risk classifications than IBCs without retrospective abnormalities (case score mean: 54.1 vs. 23.1; high risk: 48.7% vs. 14.7%; p < 0.05). 13.4% of the IBCs without retrospective abnormalities received a case score ≥ 50, of which 62.5% had not been discussed during a consensus conference. Conclusion An AI system can identify IBC screening mammograms with a higher risk for breast cancer, particularly in potentially missed IBCs but also in some IBCs without retrospective abnormalities where radiologists did not see anything, indicating its ability to improve mammography screening quality. Key Points Question AI presents a promising opportunity to enhance breast cancer screening in general, but evidence is missing regarding its ability to reduce interval breast cancers. Findings The AI system detected a high risk of breast cancer in most interval breast cancer screening mammograms where radiologists retrospectively detected abnormalities. Clinical relevance Utilization of an AI system in mammography screening programs can identify breast cancer risk in many interval breast cancer screening mammograms and thus potentially reduce the number of interval breast cancers.
Type:journal article
Journal:European Radiology
DOI:10.1007/s00330-025-11833-5
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123245
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Risk factors for interval breast cancer: insights from a decade of a mammography screening program
(Springer Science and Business Media LLC, 2025-02-12)
Jonas Subelack
;
Rudolf Morant
;
Marcel Blum
;
Alena Eichenberger
;
Alexander Geissler
Purpose Breast cancer remains a major global health issue, with mammography screening programs (MSPs) being critical for early detection to improve survival. Interval breast cancers (IBC) are an important quality criterion and have been linked with increased mortality. We aimed to identify risk factors for IBC diagnoses, based on MSP data. Methods In this retrospective cohort study, we merged data from the Swiss MSP “donna” with data from cancer registries from 2010 to 2019 to categorize cases as IBC or screen-detected breast cancer (SBC). We compared the incidence, tumor characteristics, and survival proportions of women with IBC versus SBC. We used a multivariable Poisson regression with robust errors to identify risk factors for IBC diagnoses. Results We identified 1134 breast cancer cases, specifically 251 IBC and 883 SBC. The 7-year survival proportions significantly deviated with 92.9% for women with IBC and 96.4% for women with SBC (p < 0.05). Women with IBC are diagnosed with significantly higher tumor stages (p < 0.05) and have a worse tumor biology in multiple dimensions e.g. larger tumor size or more often triple negative (p < 0.05). Higher breast density (BI-RADS d risk ratio (RR): 3.293), certain age groups (55–59 years RR: 1.345), and a family breast cancer history (RR: 1.299) were identified as significant (p < 0.05) risk factors for IBC diagnoses. Conclusions Women with IBC had lower overall survival proportions than women with SBC, possibly due to higher stages at diagnosis. Increased breast density and a positive family history of breast cancer could encourage MSPs to personalize their screening process (e.g. additional diagnostics).
Type:journal article
Journal:Breast Cancer Research and Treatment
DOI:10.1007/s10549-025-07619-4
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121984
Scopus© Citations 2
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Can AI detect interval breast cancer in screening mammograms where radiologists do not identify abnormalities?
(2025-03)
Jonas Subelack
;
Marcel Blum
;
Morant, Rudolf
;
Justus Vogel
;
Alexander Geissler
Type:conference poster
URL:https://med.unisg.ch/fileadmin/user_upload/HSG_ROOT/Institut_MED/20250312_SGBCC_AI_IBC.pdf
DOI:10.1016/j.breast.2025.103978
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122114
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Ability of artificial intelligence to reduce the number of interval breast cancer cases: Explorative analysis based on a decade of a Swiss mammography screening program
(2024)
Jonas Subelack
;
Morant, Rudolf
;
Marcel Blum
;
Eichenberger, Alena
;
Justus Vogel
Type:conference poster
URL:https://med.unisg.ch/fileadmin/user_upload/HSG_ROOT/Institut_MED/20240824_AI_IBC_Poster_vf.pdf
DOI:10.1186/s13058-024-01841-6
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121509
Scopus© Citations 10
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UTILIZATION OF AN AI DIAGNOSTIC SOFTWARE IN A MAMMOGRAPHY SCREENING PROGRAM SHOWS POTENTIAL FOR HIGHER SCREENING EFFICIENCY AND EFFECTIVENESS
(2024-09-12)
Marcel Blum
;
Alexander Geissler
;
David Ehlig
;
Justus Vogel
;
Jonas Subelack
Type:conference poster
URL:https://med.unisg.ch/fileadmin/user_upload/HSG_ROOT/Institut_MED/Poster_pAI_donna_v2.pdf
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121739
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Interval Carcinomas in the mammography screening program "Donna" (St.Gallen & Graubünden): A retrospective evaluation for quality control and improvement
(2023)
Subelack, Jonas
;
Geissler, Alexander
;
Marcel Blum
;
Morant, Rudolf
;
Eichenberger, Alena
Type:conference poster
URL:https://med.unisg.ch/fileadmin/user_upload/HSG_ROOT/Institut_MED/Forschung/Management_im_Gesundheitswesen/230913_Poster_IC_vf.pdf
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121512
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Optimal utilization of an AI diagnostic software in a mammography screening program in Switzerland
(2025-02-27)
Blum, Marcel
;
Morant, Rudolf
;
Geissler, Alexander
;
Vogel, Justus
;
Subelack, Jonas
Type:conference speech
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122097
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Retrospective evaluation of interval breast cancer with AI diagnostic software
(2024)
Subelack, Jonas
;
Geissler, Alexander
;
Vogel, Justus
;
Morant, Rudolf
;
Eichenberger, Alena
Type:conference speech
URL:https://med.unisg.ch/fileadmin/user_upload/HSG_ROOT/Institut_MED/20240303_ESR_Presentation_vP.pdf
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121511

Marcel Blum

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