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Deep learning–based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules



To evaluate a deep learning–based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size.


Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2 cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis.


The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size.


The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists’ measurements of solid portion size.

Key Points

• A deep learning–based model differentiated IPA from preinvasive lesions or MIA with AUCs of 0.914 and 0.956 for the training and tuning sets, respectively.

In the validation set including subsolid nodules of 2 cm or smaller, the model showed an AUC of 0.833, being on par with the performance of the solid portion size measurements made by the radiologists (AUC, 0.835; p = 0.97).

SSNs with a solid portion measuring > 10 mm on CT showed a high probability of being IPA (positive predictive value, 93.5–100.0%).

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Fig. 1
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Fig. 4



Atypical adenomatous hyperplasia


Adenocarcinoma in situ


Area under the receiver-operating characteristics curve


Confidence interval


Convolutional neural network


Intra-class correlation coefficient


Invasive pulmonary adenocarcinoma


Minimally invasive adenocarcinoma


Subsolid nodule


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This study received funding from the Industrial Strategic technology development program (10072064, Development of Novel Artificial Intelligence Technologies To Assist Imaging Diagnosis of Pulmonary, Hepatic, and Cardiac Diseases and Their Integration into Commercial Clinical PACS Platforms) funded by the Ministry of Trade Industry and Energy (MI, Korea).

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Correspondence to Sang Min Lee.

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The scientific guarantor of this publication is Sang Min Lee.

Conflict of interest

Three of the authors of this manuscript (Gwangbeen Park, Hyunho Park, and Kyuhwan Jung) belong to VUNO Inc., Seoul, South Korea.

Statistics and biometry

Seonok Kim, who is a statistician in Asan medical center, provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.


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Park, S., Park, G., Lee, S.M. et al. Deep learning–based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules. Eur Radiol 31, 6239–6247 (2021).

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  • Tomography, X-ray computed
  • Deep learning
  • Adenocarcinoma of lung
  • Neoplasm