Predicting Lung Cancer Survival with Attention-based CT Slices Combination Article Swipe
Domenico Paolo
,
Carlo Greco
,
Edy Ippolito
,
Michele Fiore
,
Sara Ramella
,
Paolo Soda
,
Matteo Tortora
,
Alessandro Bria
,
Rosa Sicilia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5374277/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5374277/v1
Accurate prognosis of Non-Small Cell Lung Cancer (NSCLC) is crucial for enhancing patient care and treatment outcomes. Despite the advancements in deep learning, the task of overall survival prediction in NSCLC has not fully leveraged these techniques, yet. This study introduces a novel methodology for predicting 2-year overall survival (OS) in NSCLC patients using CT scans. Our approach integrates CT scan representations produced by EfficientNetB0 with a soft attention mechanism to identify the most relevant slices for survival risk prediction, which are then analyzed by a risk-assessment network. To validate our method and ensure reproducibility, we employed the public LUNG1 dataset and a smaller private dataset. Our approach was compared to a benchmark 3D network and two variants of our methodology: on the LUNG1 it outperformed the competitors achieving a mean Ctd-index of 0.584 over 10-fold cross-validation. On the LUNG1 we also demonstrated the adaptability of our method with 4 other 2D backbones replacing the EfficientNetB0, confirming that our mechanism of combining 2D slice representations to construct a 3D volume representation is more effective for OS prediction compared to a traditional 3D approach. Finally, we used transfer learning on the private dataset, showing that it can significantly enhance performance in limited data scenarios, increasing the Ctd-index by 0.076 compared to model without transfer learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5374277/v1
- https://www.researchsquare.com/article/rs-5374277/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- DOI
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https://doi.org/10.21203/rs.3.rs-5374277/v1Digital Object Identifier
- Title
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Predicting Lung Cancer Survival with Attention-based CT Slices CombinationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-14Full publication date if available
- Authors
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Domenico Paolo, Carlo Greco, Edy Ippolito, Michele Fiore, Sara Ramella, Paolo Soda, Matteo Tortora, Alessandro Bria, Rosa SiciliaList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-5374277/v1Publisher landing page
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https://www.researchsquare.com/article/rs-5374277/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-5374277/latest.pdfDirect OA link when available
- Concepts
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Lung cancer, Cancer, Medicine, Lung, Oncology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.slice | 164 |
| abstract_inverted_index.study | 40 |
| abstract_inverted_index.these | 36 |
| abstract_inverted_index.using | 54 |
| abstract_inverted_index.which | 81 |
| abstract_inverted_index.2-year | 47 |
| abstract_inverted_index.Cancer | 7 |
| abstract_inverted_index.ensure | 94 |
| abstract_inverted_index.method | 92, 148 |
| abstract_inverted_index.public | 99 |
| abstract_inverted_index.scans. | 56 |
| abstract_inverted_index.slices | 76 |
| abstract_inverted_index.volume | 170 |
| abstract_inverted_index.(NSCLC) | 8 |
| abstract_inverted_index.10-fold | 136 |
| abstract_inverted_index.Despite | 18 |
| abstract_inverted_index.crucial | 10 |
| abstract_inverted_index.dataset | 101 |
| abstract_inverted_index.enhance | 198 |
| abstract_inverted_index.limited | 201 |
| abstract_inverted_index.network | 115 |
| abstract_inverted_index.overall | 27, 48 |
| abstract_inverted_index.patient | 13 |
| abstract_inverted_index.private | 105, 191 |
| abstract_inverted_index.showing | 193 |
| abstract_inverted_index.smaller | 104 |
| abstract_inverted_index.without | 212 |
| abstract_inverted_index.Accurate | 1 |
| abstract_inverted_index.Finally, | 184 |
| abstract_inverted_index.analyzed | 84 |
| abstract_inverted_index.approach | 58, 108 |
| abstract_inverted_index.compared | 110, 178, 209 |
| abstract_inverted_index.dataset, | 192 |
| abstract_inverted_index.dataset. | 106 |
| abstract_inverted_index.employed | 97 |
| abstract_inverted_index.identify | 72 |
| abstract_inverted_index.learning | 188 |
| abstract_inverted_index.network. | 88 |
| abstract_inverted_index.patients | 53 |
| abstract_inverted_index.produced | 63 |
| abstract_inverted_index.relevant | 75 |
| abstract_inverted_index.survival | 28, 49, 78 |
| abstract_inverted_index.transfer | 187, 213 |
| abstract_inverted_index.validate | 90 |
| abstract_inverted_index.variants | 118 |
| abstract_inverted_index.Ctd-index | 132, 206 |
| abstract_inverted_index.Non-Small | 4 |
| abstract_inverted_index.achieving | 129 |
| abstract_inverted_index.approach. | 183 |
| abstract_inverted_index.attention | 69 |
| abstract_inverted_index.backbones | 153 |
| abstract_inverted_index.benchmark | 113 |
| abstract_inverted_index.combining | 162 |
| abstract_inverted_index.construct | 167 |
| abstract_inverted_index.effective | 174 |
| abstract_inverted_index.enhancing | 12 |
| abstract_inverted_index.learning, | 23 |
| abstract_inverted_index.learning. | 214 |
| abstract_inverted_index.leveraged | 35 |
| abstract_inverted_index.mechanism | 70, 160 |
| abstract_inverted_index.outcomes. | 17 |
| abstract_inverted_index.prognosis | 2 |
| abstract_inverted_index.replacing | 154 |
| abstract_inverted_index.treatment | 16 |
| abstract_inverted_index.confirming | 157 |
| abstract_inverted_index.increasing | 204 |
| abstract_inverted_index.integrates | 59 |
| abstract_inverted_index.introduces | 41 |
| abstract_inverted_index.predicting | 46 |
| abstract_inverted_index.prediction | 29, 177 |
| abstract_inverted_index.scenarios, | 203 |
| abstract_inverted_index.competitors | 128 |
| abstract_inverted_index.methodology | 44 |
| abstract_inverted_index.performance | 199 |
| abstract_inverted_index.prediction, | 80 |
| abstract_inverted_index.techniques, | 37 |
| abstract_inverted_index.traditional | 181 |
| abstract_inverted_index.adaptability | 145 |
| abstract_inverted_index.advancements | 20 |
| abstract_inverted_index.demonstrated | 143 |
| abstract_inverted_index.methodology: | 121 |
| abstract_inverted_index.outperformed | 126 |
| abstract_inverted_index.significantly | 197 |
| abstract_inverted_index.EfficientNetB0 | 65 |
| abstract_inverted_index.representation | 171 |
| abstract_inverted_index.EfficientNetB0, | 156 |
| abstract_inverted_index.representations | 62, 165 |
| abstract_inverted_index.risk-assessment | 87 |
| abstract_inverted_index.reproducibility, | 95 |
| abstract_inverted_index.cross-validation. | 137 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.3787021 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |