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Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study

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Abstract

Objectives

To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.

Methods

DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis.

Results

In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01–1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002–1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005–1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13).

Conclusion

The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas.

Clinical relevance statement

Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management.

Key Points

• A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma.

• The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage.

• Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

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Abbreviations

DL:

Deep learning

FFR:

Freedom from recurrence

HR:

Hazard ratio

IQR:

Interquartile range

OS:

Overall survival

ROC:

Receiver operating characteristic

TD-AUC:

Time-dependent area under the ROC curve

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Funding

This study was supported by the Seoul National University Hospital Research Fund (grant number: 04-2020-2040 and 03-2022-2170) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00207978). However, the funders had no role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication.

Author information

Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Taehee Lee, Jong Hyuk Lee, Jin Mo Goo & Hyungjin Kim

  2. Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Kyung Hee Lee, Jong Hyuk Lee, Jin Mo Goo & Hyungjin Kim

  3. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea

    Kyung Hee Lee

  4. Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Samina Park & Young Tae Kim

  5. Seoul National University Cancer Research Institute, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Young Tae Kim & Jin Mo Goo

  6. Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Jin Mo Goo

Authors
  1. Taehee Lee
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Corresponding author

Correspondence to Hyungjin Kim.

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Guarantor

The scientific guarantor of this publication is Hyungjin Kim.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Hyungjin Kim) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some patients in our datasets have been reported previously (Lee et al [19]; 2717/3181); Lim et al [20]; 681/3181)). However, none of the prior studies dealt with the multitask learning of both morphological and histopathological features for prognostication.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Lee, T., Lee, K.H., Lee, J.H. et al. Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10306-x

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