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Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning–assisted nodule segmentation

  • Oncology
  • Published:
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Abstract

Objective

To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning–assisted nodule segmentation.

Methods

Between June 2012 and June 2019, 95 resected SSNs with preoperative long-term follow-up were enrolled in this retrospective study. SSN detection and segmentation were performed on preoperative follow-up CTs using the deep learning–based Dr. Wise system. SSNs were categorized into invasive adenocarcinoma (IAC, n = 47) and non-IAC (n = 48) groups; according to the interval change during the preoperative follow-up, SSNs were divided into growth (n = 68), nongrowth (n = 22), and new emergence (n = 5) groups. We analyzed the cumulative percentages and pattern of SSN growth and identified significant factors for IAC diagnosis and SSN growth.

Results

The mean preoperative follow-up was 42.1 ± 17.0 months. More SSNs showed growth or new emergence in the IAC than in the non-IAC group (89.4% vs. 64.6%, p = 0.009). Volume doubling time was non-significantly shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days, p = 0.077). Median mass doubling time was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). Lobulated sign (p = 0.002) and SSN mass (p = 0.004) were significant factors for differentiating IACs. IACs showed significantly higher cumulative growth percentages than non-IACs in the first 70 months of follow-up. The growth pattern of SSNs may conform to the exponential model. The initial volume (p = 0.042) was a predictor for SSN growth.

Conclusions

IACs appearing as SSNs showed an indolent course. The mean growth rate was larger for IACs than for non-IACs. SSNs with larger initial volume are more likely to grow.

Key Points

• Invasive adenocarcinomas (IACs) appearing as subsolid nodules (SSNs), with a mean volume doubling time (VDT) of 1436.0 ± 1188.2 days and median mass doubling time (MDT) of 821.7 days, showed an indolent course.

• The VDT was shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days), but the difference was not significant (p = 0.077). The median MDT was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001).

• SSNs with lobulated sign and larger mass (> 390.5 mg) may very likely be IACs. SSNs with larger initial volume are more likely to grow.

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Abbreviations

AAH:

Atypical adenomatous hyperplasia

AIS:

Adenocarcinoma in situ

CI:

Confidence interval

EGFR:

Epidermal growth factor receptor

FF:

Focal fibrosis

IAC:

Invasive adenocarcinoma

MDT:

Mass doubling time

MIA:

Minimally invasive adenocarcinoma

OR:

Odds ratio

pGGN:

Pure ground-glass nodule

PSN:

Part-solid nodule

SSN:

Subsolid nodule

VDT:

Volume doubling time

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Acknowledgements

We would like to thank Dr. Chang-Fa Xia (Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) and Dr. Zhang-Yan Lyu (Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) for their statistical assistance.

Funding

This study has received funding from the National Key R&D Program of China (2017YFC1308700), the National Natural Science Foundation of China (81771830), the National Natural Science Foundation of China (81971616), the CAMS Innovation Fund for Medical Sciences (2017-I2M-1-005), the CAMS Innovation Fund for Medical Sciences (2019-I2M-2-002), the National Key Technology Support Program (2014BAI09B01), and the Innovation Foundation for Doctoral Candidates of Peking Union Medical College (2018-1002-02-21).

Author information

Author notes

  1. Lin-Lin Qi and Jian-Wei Wang contributed equally to this work, as co-first authors.

Authors and Affiliations

  1. Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China

    Lin-Lin Qi, Jian-Wei Wang, Yao Huang, Shi-Jun Zhao, Wei Tang & Ning Wu

  2. Department of Diagnostic Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China

    Lin Yang

  3. PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China

    Yu-Jing Jin, Ze-Wei Zhang & Ning Wu

  4. School of Electronic Engineering and Computer Science, Peking University, No. 5 Yiheyuan Rd., Haidian District, Beijing, 100871, China

    Zhen Zhou

  5. Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China

    Yi-Zhou Yu

  6. Center on Frontiers of Computing Studies, Department of Computer Science, Peking University, No. 5 Yiheyuan Rd., Haidian District, Beijing, 100871, China

    Yi-Zhou Wang

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  1. Lin-Lin Qi
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Corresponding author

Correspondence to Ning Wu.

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Guarantor

The scientific guarantor of this publication is Ning Wu.

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

Dr. Chang-Fa Xia (Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) and Dr. Zhang-Yan Lyu (Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) kindly provided statistical advice for this manuscript.

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

Nineteen study subjects have been previously reported in our previous study [Qi LL, Wu BT, Tang W et al (2020) Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation. Eur Radiol 30(2):744–755].

However, the objectives and inclusion criteria of these two studies differed. The previous study focused on the long-term follow-up of persistent pulmonary pure ground-glass nodules (pGGNs), most of which were not resected and pathologically confirmed. In contrast, in the current study, we focused on the natural history of these pathologically confirmed pulmonary subsolid nodules (SSNs) and explored the risk factors for invasive adenocarcinoma diagnosis and SSN growth.

Methodology

• retrospective

• observational

• performed at one institution

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Qi, LL., Wang, JW., Yang, L. et al. Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning–assisted nodule segmentation. Eur Radiol 31, 3884–3897 (2021). https://doi.org/10.1007/s00330-020-07450-z

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