Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Open accessAbstract
BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. METHODS AND FINDINGS: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks. CONCLUSIONS: Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.
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Deep learning can successfully be used for identifying and segmenting objects within the 3-dimensional image space.
“Especially in medical imaging there is a large potential as deep learning can successfully be used for identifying and segmenting objects within the 3-dimensional (3D) image space13–16.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [15]
The Introduction (page unavailable) says CNNs have shown potential in detection and segmentation, but notes that most work addressed 2D problems and only a few studies used entire 3D tumor volumes. This study’s Methods and Results (pages unavailable) describe manually contoured tumors and a 3D network trained to predict survival, not to identify or segment objects in 3D. The source therefore supports the general imaging applications, but not the specific 3D identification-and-segmentation claim. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDeep-learning methods are robust to differences in acquisition and segmentation variability and provide improved performance.
“Deep-learning methods, in comparison, are robust to differences in acquisition and segmentation variability and provide improved performance10.”
From Foundation Model for Cancer Imaging Biomarkers (2024) · cites this paper as [10]
Many studies investigating imaging-based biomarkers incorporate supervised deep-learning algorithms, but they are typically applied in scenarios where large datasets are available for training and testing.
“While many studies investigating imaging-based biomarkers incorporate supervised deep-learning algorithms into their models9–11, they are typically applied in scenarios where large datasets are available for training and testing.”
From Foundation Model for Cancer Imaging Biomarkers (2024) · cites this paper as [10]
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- Authors
- Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L.
- Affiliations
- Brigham and Women's Hospital (US)
- Radboud University Medical Center (NL)
- Moffitt Cancer Center (US)
- Corresponding author
- Aerts, Hugo J. W. L.
- ORCID
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-11-30
- Volume
- 15
- Issue
- 11
- Pages
- e1002711
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC6269088
- OpenAlex
- W2903150666
- Cited by
- 579
- References
- 95
- Citations by year
- 2026: 30 · 2025: 61 · 2024: 78 · 2023: 93 · 2022: 95 · 2021: 103 · 2020: 84 · 2019: 33 · 2018: 1 · 2014: 1
- FWCI
- 38.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (U24CA194354, U01CA190234)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- non-small cell lung cancer, outcome prediction, radiomics, 3D convolutional neural networks, computed tomography, transfer learning, overall survival, radiotherapy, surgical resection, patient stratification, deep learning interpretability, random forest comparison, prognostic signature
- MeSH terms
- Clinical Decision-Making, Preliminary Data, Deep Learning, Adult, Aged, Aged, 80 and over, Carcinoma, Non-Small-Cell Lung, Diagnosis, Computer-Assisted, Female, Humans, Lung Neoplasms, Male, Middle Aged, Neoplasm Staging, Predictive Value of Tests, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, Risk Factors, Tomography, X-Ray Computed, Reproducibility of Results, Risk Assessment
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