A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images Article Swipe
Hongyi Duanmu
,
Shristi Bhattarai
,
Hongxiao Li
,
Zhan Shi
,
Fusheng Wang
,
George Teodoro
,
Keerthi Gogineni
,
Preeti Subhedar
,
Umay Kiraz
,
Emiel A. M. Janssen
,
Ritu Aneja
,
Jun Kong
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btac558
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btac558
Motivation Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. Results The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. Availability and implementation The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers Supplementary information Supplementary data are available at Bioinformatics online.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btac558
- https://academic.oup.com/bioinformatics/article-pdf/38/19/4605/46222009/btac558.pdf
- OA Status
- bronze
- Cited By
- 38
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4291250580
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4291250580Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bioinformatics/btac558Digital Object Identifier
- Title
-
A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology imagesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-08-13Full publication date if available
- Authors
-
Hongyi Duanmu, Shristi Bhattarai, Hongxiao Li, Zhan Shi, Fusheng Wang, George Teodoro, Keerthi Gogineni, Preeti Subhedar, Umay Kiraz, Emiel A. M. Janssen, Ritu Aneja, Jun KongList of authors in order
- Landing page
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https://doi.org/10.1093/bioinformatics/btac558Publisher landing page
- PDF URL
-
https://academic.oup.com/bioinformatics/article-pdf/38/19/4605/46222009/btac558.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/bioinformatics/article-pdf/38/19/4605/46222009/btac558.pdfDirect OA link when available
- Concepts
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Deep learning, Computer science, Artificial intelligence, Digital pathology, H&E stain, Breast cancer, Triple-negative breast cancer, Interpretability, Machine learning, Biopsy, Pattern recognition (psychology), Pathology, Medicine, Cancer, Immunohistochemistry, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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38Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 15, 2023: 7, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.model | 198 |
| abstract_inverted_index.other | 207 |
| abstract_inverted_index.prior | 70 |
| abstract_inverted_index.three | 104, 117 |
| abstract_inverted_index.tumor | 107 |
| abstract_inverted_index.work, | 38 |
| abstract_inverted_index.(TNBC) | 15 |
| abstract_inverted_index.PHH3). | 66 |
| abstract_inverted_index.biopsy | 111 |
| abstract_inverted_index.blocks | 112 |
| abstract_inverted_index.breast | 13, 106 |
| abstract_inverted_index.cancer | 14 |
| abstract_inverted_index.codes, | 222 |
| abstract_inverted_index.direly | 19 |
| abstract_inverted_index.domain | 71 |
| abstract_inverted_index.images | 54, 177 |
| abstract_inverted_index.inform | 91 |
| abstract_inverted_index.models | 94 |
| abstract_inverted_index.needed | 20 |
| abstract_inverted_index.pixels | 155 |
| abstract_inverted_index.serial | 52, 105 |
| abstract_inverted_index.source | 233 |
| abstract_inverted_index.stains | 119 |
| abstract_inverted_index.strong | 31 |
| abstract_inverted_index.system | 44, 137 |
| abstract_inverted_index.tissue | 97, 108, 140 |
| abstract_inverted_index.voting | 192 |
| abstract_inverted_index.Results | 142 |
| abstract_inverted_index.dataset | 145, 175 |
| abstract_inverted_index.enhance | 75 |
| abstract_inverted_index.example | 226 |
| abstract_inverted_index.making. | 24, 217 |
| abstract_inverted_index.maximum | 191 |
| abstract_inverted_index.online. | 244 |
| abstract_inverted_index.overall | 34 |
| abstract_inverted_index.patches | 152, 164, 178 |
| abstract_inverted_index.predict | 46 |
| abstract_inverted_index.propose | 40 |
| abstract_inverted_index.spatial | 87 |
| abstract_inverted_index.stained | 55, 115 |
| abstract_inverted_index.support | 68 |
| abstract_inverted_index.system, | 81 |
| abstract_inverted_index.testing | 187 |
| abstract_inverted_index.treated | 160 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.achieves | 199 |
| abstract_inverted_index.clinical | 22, 215 |
| abstract_inverted_index.complete | 4 |
| abstract_inverted_index.consists | 146 |
| abstract_inverted_index.dataset. | 188 |
| abstract_inverted_index.decision | 23, 216 |
| abstract_inverted_index.guidance | 73 |
| abstract_inverted_index.learning | 43, 80, 93 |
| abstract_inverted_index.patient, | 103 |
| abstract_inverted_index.patients | 16, 159, 169 |
| abstract_inverted_index.proposed | 197 |
| abstract_inverted_index.randomly | 166 |
| abstract_inverted_index.regarded | 28 |
| abstract_inverted_index.regions. | 141 |
| abstract_inverted_index.response | 5 |
| abstract_inverted_index.results, | 195 |
| abstract_inverted_index.sections | 109 |
| abstract_inverted_index.selected | 167 |
| abstract_inverted_index.systems, | 209 |
| abstract_inverted_index.training | 174 |
| abstract_inverted_index.triplets | 130 |
| abstract_inverted_index.accuracy, | 203 |
| abstract_inverted_index.attention | 88, 125 |
| abstract_inverted_index.available | 229, 241 |
| abstract_inverted_index.baselines | 205 |
| abstract_inverted_index.different | 118 |
| abstract_inverted_index.interest. | 100 |
| abstract_inverted_index.introduce | 83 |
| abstract_inverted_index.mechanism | 89 |
| abstract_inverted_index.pathology | 53, 150 |
| abstract_inverted_index.potential | 213 |
| abstract_inverted_index.predictor | 32 |
| abstract_inverted_index.resulting | 123 |
| abstract_inverted_index.survival. | 35 |
| abstract_inverted_index.1000×1000 | 154 |
| abstract_inverted_index.Motivation | 1 |
| abstract_inverted_index.Predicting | 2 |
| abstract_inverted_index.accurately | 17 |
| abstract_inverted_index.biomarkers | 63 |
| abstract_inverted_index.prediction | 136 |
| abstract_inverted_index.prognostic | 139 |
| abstract_inverted_index.sectioned, | 114 |
| abstract_inverted_index.suggesting | 210 |
| abstract_inverted_index.hematoxylin | 57 |
| abstract_inverted_index.information | 126, 237 |
| abstract_inverted_index.informative | 96 |
| abstract_inverted_index.integrated. | 121 |
| abstract_inverted_index.neoadjuvant | 8 |
| abstract_inverted_index.patch-level | 194 |
| abstract_inverted_index.Availability | 218 |
| abstract_inverted_index.chemotherapy | 9 |
| abstract_inverted_index.experimental | 144 |
| abstract_inverted_index.pathological | 3 |
| abstract_inverted_index.Supplementary | 236, 238 |
| abstract_inverted_index.comprehensive | 124 |
| abstract_inverted_index.documentation | 224 |
| abstract_inverted_index.outperforming | 204 |
| abstract_inverted_index.patient-level | 202 |
| abstract_inverted_index.Bioinformatics | 243 |
| abstract_inverted_index.implementation | 220 |
| abstract_inverted_index.knowledge-based | 72 |
| abstract_inverted_index.triple-negative | 12 |
| abstract_inverted_index.interpretability | 76 |
| abstract_inverted_index.state-of-the-art | 208 |
| abstract_inverted_index.knowledge-derived | 86 |
| abstract_inverted_index.immunohistochemical | 62 |
| abstract_inverted_index.https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers | 235 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5090147550, https://openalex.org/A5066949209 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I150468666, https://openalex.org/I181565077, https://openalex.org/I59553526 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.96339403 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |