Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery Article Swipe
Caroline Truntzer
,
Dina Ouahbi
,
Titouan Huppé
,
David Rageot
,
Alis Ilie
,
Chloé Molimard
,
Françoise Beltjens
,
Anthony Bergeron
,
Angélique Vienot
,
Christophe Borg
,
Franck Monnien
,
Frédéric Bibeau
,
Valentin Dérangère
,
François Ghiringhelli
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/biomedicines12122754
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/biomedicines12122754
Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a cancer with very poor prognosis despite early surgical management. To date, only clinical variables are used to predict outcome for decision-making about adjuvant therapy. We sought to generate a deep learning approach based on hematoxylin and eosin (H&E) or hematoxylin, eosin and saffron (HES) whole slides to predict patients’ outcome, compare these new entities with known molecular subtypes and question their biological significance; Methods: We used as a training set a retrospective private cohort of 206 patients treated by surgery for PDAC cancer and a validation cohort of 166 non-metastatic patients from The Cancer Genome Atlas (TCGA) PDAC project. We estimated a multi-instance learning survival model to predict relapse in the training set and evaluated its performance in the validation set. RNAseq and exome data from the TCGA PDAC database were used to describe the transcriptomic and genomic features associated with deep learning classification; Results: Based on the estimation of an attention-based multi-instance learning survival model, we identified two groups of patients with a distinct prognosis. There was a significant difference in progression-free survival (PFS) between these two groups in the training set (hazard ratio HR = 0.72 [0.54;0.96]; p = 0.03) and in the validation set (HR = 0.63 [0.42;0.94]; p = 0.01). Transcriptomic and genomic features revealed that the poor prognosis group was associated with a squamous phenotype. Conclusions: Our study demonstrates that deep learning could be used to predict PDAC prognosis and offer assistance in better choosing adjuvant treatment.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/biomedicines12122754
- OA Status
- gold
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404906361
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404906361Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/biomedicines12122754Digital Object Identifier
- Title
-
Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following SurgeryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-02Full publication date if available
- Authors
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Caroline Truntzer, Dina Ouahbi, Titouan Huppé, David Rageot, Alis Ilie, Chloé Molimard, Françoise Beltjens, Anthony Bergeron, Angélique Vienot, Christophe Borg, Franck Monnien, Frédéric Bibeau, Valentin Dérangère, François GhiringhelliList of authors in order
- Landing page
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https://doi.org/10.3390/biomedicines12122754Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/biomedicines12122754Direct OA link when available
- Concepts
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Medicine, Pancreatic cancer, Oncology, H&E stain, Internal medicine, Hazard ratio, Cohort, Proportional hazards model, Cancer, Bioinformatics, Machine learning, Immunohistochemistry, Biology, Computer science, Confidence intervalTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.cohort | 80, 93 |
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| corresponding_author_ids | https://openalex.org/A5011315376, https://openalex.org/A5043463827 |
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| corresponding_institution_ids | https://openalex.org/I154526488, https://openalex.org/I4210094685, https://openalex.org/I4210139033 |
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| citation_normalized_percentile.is_in_top_10_percent | False |