Abstract
Simple SummaryThe high-grade pattern (micropapillary or solid pattern, MPSol) in lung adenocarcinoma affects the patient’s poor prognosis. We aimed to develop a deep learning (DL) model for predicting any high-grade patterns in lung adenocarcinoma and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant of definitive concurrent chemoradiation therapy (CCRT). Our model considering both tumor and peri-tumoral area showed area under the curve value of 0.8. DL model worked well in independent validation set of advanced lung cancer, stratifying their survival significantly. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death. Thus, our DL model can be useful in estimating high-grade histologic patterns in lung adenocarcinomas and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal–training and internal–validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16–2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.
Highlights
Invasive lung adenocarcinoma (ADC) has been classified by the 2011 classification system of International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) into five distinct histological patterns: lepidic, acinar, papillary, micropapillary, and solid [1]
Eleven patients died during follow-up, and six of these patients died from recurrent lung cancer
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Summary
Invasive lung adenocarcinoma (ADC) has been classified by the 2011 classification system of International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) into five distinct histological patterns: lepidic, acinar, papillary, micropapillary, and solid [1]. Lung ADC is divided into low-, intermediate, and high-grade prognostic groups according to the most predominant pattern detected by histopathology [2,3,4]. Even among lung ADCs with the same most predominant pattern, the spectrum of actual prognosis varies widely [3,5,6,7]. Regardless of the predominant pattern, the presence of any high-grade pattern such as a micropapillary and solid pattern is known to have a poor prognosis [6,8]. Identifying the presence of any high-grade pattern in lung ADCs before surgery can help predict a patient’s prognosis and determine a treatment policy.
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