Implementing a Resource-Light and Low-Code Large Language Model System for Information Extraction from Mammography Reports: A Pilot Study Article Swipe
Fabio Dennstädt
,
Susanne Fauser
,
Nikola Čihorić
,
Max Schmerder
,
Paolo Lombardo
,
Grazia M. Cereghetti
,
Sandro von Däniken
,
Thomas Minder
,
Jaro Meyer
,
Lawrence Chiang
,
Roberto Gaio
,
Luc Lerch
,
Irina Filchenko
,
Daniel Reichenpfader
,
Kerstin Denecke
,
Caslav Vojvodic
,
Igor Tatalovic
,
André Sander
,
Janna Hastings
,
Daniel M. Aebersold
,
Hendrik von Tengg‐Kobligk
,
Knud Nairz
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10278-025-01659-4
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10278-025-01659-4
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure. Seventy-nine CDEs were defined by an interdisciplinary expert panel, reflecting real-world reporting practice. Sixty-one reports were classified by two independent researchers to establish ground truth. Five different open-source LLMs deployable on a single GPU were used for data extraction using the general-classifier Python package. Extractions were performed for five different prompt approaches with calculation of overall accuracy, micro-recall and micro-F1. Additional analyses were conducted using thresholds for the relative probability of classifications. High inter-rater agreement was observed between manual classifiers (Cohen’s kappa 0.83). Using default prompts, the LLMs achieved accuracies of 59.2–72.9%. Chain-of-thought prompting yielded mixed results, while few-shot prompting led to decreased accuracy. Adaptation of the default prompts to precisely define classification tasks improved performance for all models, with accuracies of 64.7–85.3%. Setting certainty thresholds further improved accuracies to > 90% but reduced the coverage rate to < 50%. Locally deployed open-source LLMs can effectively extract information from mammography reports, maintaining compatibility with limited computational resources. Selection and evaluation of the model and prompting strategy are critical. Clear, task-specific instructions appear crucial for high performance. Using a CDE-based framework provides clear semantics and structure for the data extraction.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10278-025-01659-4
- https://link.springer.com/content/pdf/10.1007/s10278-025-01659-4.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414108623
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414108623Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10278-025-01659-4Digital Object Identifier
- Title
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Implementing a Resource-Light and Low-Code Large Language Model System for Information Extraction from Mammography Reports: A Pilot StudyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-10Full publication date if available
- Authors
-
Fabio Dennstädt, Susanne Fauser, Nikola Čihorić, Max Schmerder, Paolo Lombardo, Grazia M. Cereghetti, Sandro von Däniken, Thomas Minder, Jaro Meyer, Lawrence Chiang, Roberto Gaio, Luc Lerch, Irina Filchenko, Daniel Reichenpfader, Kerstin Denecke, Caslav Vojvodic, Igor Tatalovic, André Sander, Janna Hastings, Daniel M. Aebersold, Hendrik von Tengg‐Kobligk, Knud NairzList of authors in order
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https://doi.org/10.1007/s10278-025-01659-4Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s10278-025-01659-4.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10278-025-01659-4.pdfDirect OA link when available
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.two | 76 |
| abstract_inverted_index.via | 24 |
| abstract_inverted_index.was | 133 |
| abstract_inverted_index.> | 188 |
| abstract_inverted_index.< | 196 |
| abstract_inverted_index.50%. | 197 |
| abstract_inverted_index.CDEs | 59 |
| abstract_inverted_index.Five | 83 |
| abstract_inverted_index.High | 130 |
| abstract_inverted_index.LLMs | 22, 86, 145, 201 |
| abstract_inverted_index.Most | 16 |
| abstract_inverted_index.been | 6 |
| abstract_inverted_index.data | 10, 45, 54, 95, 245 |
| abstract_inverted_index.five | 106 |
| abstract_inverted_index.from | 12, 47, 206 |
| abstract_inverted_index.have | 5 |
| abstract_inverted_index.high | 232 |
| abstract_inverted_index.rate | 194 |
| abstract_inverted_index.used | 8, 93 |
| abstract_inverted_index.were | 19, 60, 73, 92, 103, 120 |
| abstract_inverted_index.with | 21, 110, 177, 211 |
| abstract_inverted_index.LLMs, | 37 |
| abstract_inverted_index.Large | 1 |
| abstract_inverted_index.Using | 141, 234 |
| abstract_inverted_index.clear | 239 |
| abstract_inverted_index.kappa | 139 |
| abstract_inverted_index.local | 41 |
| abstract_inverted_index.mixed | 153 |
| abstract_inverted_index.model | 220 |
| abstract_inverted_index.tasks | 171 |
| abstract_inverted_index.using | 35, 51, 97, 122 |
| abstract_inverted_index.while | 155 |
| abstract_inverted_index.(API). | 29 |
| abstract_inverted_index.(LLMs) | 4 |
| abstract_inverted_index.0.83). | 140 |
| abstract_inverted_index.Clear, | 226 |
| abstract_inverted_index.Python | 100 |
| abstract_inverted_index.appear | 229 |
| abstract_inverted_index.common | 53 |
| abstract_inverted_index.define | 169 |
| abstract_inverted_index.expert | 65 |
| abstract_inverted_index.ground | 81 |
| abstract_inverted_index.manual | 136 |
| abstract_inverted_index.models | 3 |
| abstract_inverted_index.panel, | 66 |
| abstract_inverted_index.prompt | 108 |
| abstract_inverted_index.single | 90 |
| abstract_inverted_index.truth. | 82 |
| abstract_inverted_index.Locally | 198 |
| abstract_inverted_index.Setting | 181 |
| abstract_inverted_index.between | 135 |
| abstract_inverted_index.crucial | 230 |
| abstract_inverted_index.current | 17 |
| abstract_inverted_index.default | 142, 165 |
| abstract_inverted_index.defined | 61 |
| abstract_inverted_index.element | 55 |
| abstract_inverted_index.extract | 204 |
| abstract_inverted_index.further | 184 |
| abstract_inverted_index.limited | 40, 212 |
| abstract_inverted_index.models, | 176 |
| abstract_inverted_index.overall | 113 |
| abstract_inverted_index.prompts | 166 |
| abstract_inverted_index.reduced | 191 |
| abstract_inverted_index.reports | 72 |
| abstract_inverted_index.studies | 18 |
| abstract_inverted_index.yielded | 152 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accessed | 23 |
| abstract_inverted_index.achieved | 146 |
| abstract_inverted_index.analyses | 119 |
| abstract_inverted_index.coverage | 193 |
| abstract_inverted_index.deployed | 38, 199 |
| abstract_inverted_index.few-shot | 156 |
| abstract_inverted_index.hardware | 42 |
| abstract_inverted_index.improved | 172, 185 |
| abstract_inverted_index.language | 2 |
| abstract_inverted_index.observed | 134 |
| abstract_inverted_index.package. | 101 |
| abstract_inverted_index.prompts, | 143 |
| abstract_inverted_index.provides | 238 |
| abstract_inverted_index.relative | 126 |
| abstract_inverted_index.reports, | 50, 208 |
| abstract_inverted_index.reports. | 15 |
| abstract_inverted_index.results, | 154 |
| abstract_inverted_index.strategy | 223 |
| abstract_inverted_index.CDE-based | 236 |
| abstract_inverted_index.Selection | 215 |
| abstract_inverted_index.Sixty-one | 71 |
| abstract_inverted_index.accuracy, | 114 |
| abstract_inverted_index.accuracy. | 161 |
| abstract_inverted_index.agreement | 132 |
| abstract_inverted_index.certainty | 182 |
| abstract_inverted_index.conducted | 20, 121 |
| abstract_inverted_index.critical. | 225 |
| abstract_inverted_index.decreased | 160 |
| abstract_inverted_index.different | 84, 107 |
| abstract_inverted_index.establish | 80 |
| abstract_inverted_index.evaluated | 31 |
| abstract_inverted_index.framework | 237 |
| abstract_inverted_index.free-text | 13, 48 |
| abstract_inverted_index.interface | 28 |
| abstract_inverted_index.micro-F1. | 117 |
| abstract_inverted_index.performed | 104 |
| abstract_inverted_index.practice. | 70 |
| abstract_inverted_index.precisely | 168 |
| abstract_inverted_index.prompting | 151, 157, 222 |
| abstract_inverted_index.radiology | 14 |
| abstract_inverted_index.reporting | 69 |
| abstract_inverted_index.resources | 43 |
| abstract_inverted_index.semantics | 240 |
| abstract_inverted_index.structure | 242 |
| abstract_inverted_index.(Cohen’s | 138 |
| abstract_inverted_index.Adaptation | 162 |
| abstract_inverted_index.Additional | 118 |
| abstract_inverted_index.accuracies | 147, 178, 186 |
| abstract_inverted_index.approaches | 109 |
| abstract_inverted_index.classified | 74 |
| abstract_inverted_index.deployable | 87 |
| abstract_inverted_index.evaluation | 217 |
| abstract_inverted_index.extraction | 11, 46, 96 |
| abstract_inverted_index.real-world | 68 |
| abstract_inverted_index.reflecting | 67 |
| abstract_inverted_index.resources. | 214 |
| abstract_inverted_index.structure. | 57 |
| abstract_inverted_index.thresholds | 123, 183 |
| abstract_inverted_index.(CDE)-based | 56 |
| abstract_inverted_index.Extractions | 102 |
| abstract_inverted_index.application | 26 |
| abstract_inverted_index.calculation | 111 |
| abstract_inverted_index.classifiers | 137 |
| abstract_inverted_index.effectively | 203 |
| abstract_inverted_index.extraction. | 246 |
| abstract_inverted_index.feasibility | 33 |
| abstract_inverted_index.independent | 77 |
| abstract_inverted_index.information | 205 |
| abstract_inverted_index.inter-rater | 131 |
| abstract_inverted_index.maintaining | 209 |
| abstract_inverted_index.mammography | 49, 207 |
| abstract_inverted_index.open-source | 36, 85, 200 |
| abstract_inverted_index.performance | 173 |
| abstract_inverted_index.probability | 127 |
| abstract_inverted_index.programming | 27 |
| abstract_inverted_index.researchers | 78 |
| abstract_inverted_index.Seventy-nine | 58 |
| abstract_inverted_index.instructions | 228 |
| abstract_inverted_index.micro-recall | 115 |
| abstract_inverted_index.performance. | 233 |
| abstract_inverted_index.successfully | 7 |
| abstract_inverted_index.59.2–72.9%. | 149 |
| abstract_inverted_index.64.7–85.3%. | 180 |
| abstract_inverted_index.compatibility | 210 |
| abstract_inverted_index.computational | 213 |
| abstract_inverted_index.task-specific | 227 |
| abstract_inverted_index.classification | 170 |
| abstract_inverted_index.Chain-of-thought | 150 |
| abstract_inverted_index.classifications. | 129 |
| abstract_inverted_index.interdisciplinary | 64 |
| abstract_inverted_index.general-classifier | 99 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5020748054 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 22 |
| corresponding_institution_ids | https://openalex.org/I118564535, https://openalex.org/I2801112126 |
| citation_normalized_percentile.value | 0.95759773 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |