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Cited by 32Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentAI in cancer detection

Histopathologic basis for a chest CT deep learning survival prediction model in patients with lung adenocarcinoma

Nam, Ju Gang, Park, Samina, Park, Chang Min, Jeon, Yoon Kyung, Chung, Doo Hyun, Goo, Jin Mo, Kim, Young Tae, Kim, Hyungjin·2022·Radiology
DOI 10.1148/radiol.213262
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Abstract

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model–driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57–71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.

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Crossref · 10.1148/radiol.213262

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  • These efforts include histology and CT-based models for survival prediction and correlation of CT imaging features with histology risk factors.

    “These efforts include histology (102) and CT-based (103) models for survival prediction, and the correlation of CT imaging features with histology risk factors (104).”

    From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [104]

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Authors
Nam, J. G., Park, S., Park, C. M., Jeon, Y. K., Chung, D. H., Goo, J. M., Kim, Y. T., & Kim, H.
Affiliations
Seoul National University (KR)
ORCID
Venue
Radiology
Publisher
Radiological Society of North America (RSNA)
Published
2022-11
Volume
305
Issue
2
Pages
441-451
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
DOI
10.1148/radiol.213262
OpenAlex
W4283809060
Cited by
32
References
30
Citations by year
2026: 5 · 2025: 7 · 2024: 12 · 2023: 8
FWCI
3.84
Type
journal article
Language
en
Open access
closed
Locations
  • Radiology (publishedVersion)
  • PubMed (publishedVersion)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
Funders
  • National Research Foundation of Korea
  • Ministry of Science and ICT (NRF-2020R1C1C1003684)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
lung adenocarcinoma, disease-free survival, venous invasion, visceral pleural invasion, mediastinal lymph node metastasis, perineural invasion, EGFR mutation, unsupervised clustering, model interpretability, curative resection, prognostic model
MeSH terms
Adenocarcinoma of Lung, Deep Learning, Adenocarcinoma, Aged, Female, Humans, Lung Neoplasms, Male, Middle Aged, Retrospective Studies, Tomography, X-Ray Computed, ErbB Receptors

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