Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer Article Swipe
Lorenzo Nibid
,
Carlo Greco
,
Ermanno Cordelli
,
Giovanna Sabarese
,
Michele Fiore
,
Charles Z. Liu
,
Edy Ippolito
,
Rosa Sicilia
,
M. Miele
,
Matteo Tortora
,
Chiara Taffon
,
Mehrdad Rakaee
,
Paolo Soda
,
Sara Ramella
,
Giuseppe Perrone
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0294259
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0294259
Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)—AlexNet, VGG, MobileNet, GoogLeNet, and ResNet—using a leave-two patient-out cross validation approach, and we evaluated the networks’ performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient’s response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0294259
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294259&type=printable
- OA Status
- gold
- Cited By
- 12
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389077438Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0294259Digital Object Identifier
- Title
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Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancerWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-28Full publication date if available
- Authors
-
Lorenzo Nibid, Carlo Greco, Ermanno Cordelli, Giovanna Sabarese, Michele Fiore, Charles Z. Liu, Edy Ippolito, Rosa Sicilia, M. Miele, Matteo Tortora, Chiara Taffon, Mehrdad Rakaee, Paolo Soda, Sara Ramella, Giuseppe PerroneList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0294259Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294259&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0294259&type=printableDirect OA link when available
- Concepts
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Stage (stratigraphy), Lung cancer, Cancer, Medicine, Oncology, Biology, Internal medicine, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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