LungPath: artificial intelligence-driven histologic pattern recognition for improved diagnosis of early-stage invasive lung adenocarcinoma

Abstract

Background: Early-stage invasive lung adenocarcinoma (ADC) characterized by a predominant micropapillary or solid pattern exhibit an elevated risk of recurrence following sub-lobar resection, thus determining histological subtype of early-stage invasive ADC prior surgery is important for formulating lobectomy or sub-lobar resection. This study aims to develop a deep learning algorithm and assess its clinical capability in distinguishing high-risk or low-risk histologic patterns in early-stage invasive ADC based on preoperative computed tomography (CT) scans.

Methods: Two retrospective cohorts were included: development cohort 1 and external test cohort 2, comprising patients diagnosed with T1 stage invasive ADC. Electronic medical records and CT scans of all patients were documented. Patients were stratified into two risk groups. High-risk group: comprising cases with a micropapillary component ≥5% or a predominant solid pattern. Low-risk group: encompassing cases with a micropapillary component <5% and an absence of a predominant solid pattern. The overall segmentation model was modified based on Mask Region-based Convolutional Neural Network (Mask-RCNN), and Residual Network 50 (ResNet50)_3D was employed for image classification.

Results: A total of 432 patients participated in this study, with 385 cases in cohort 1 and 47 cases in cohort 2. The fine-outline results produced by the auto-segmentation model exhibited a high level of agreement with manual segmentation by human experts, yielding a mean dice coefficient of 0.86 [95% confidence interval (CI): 0.85-0.87] in cohort 1 and 0.84 (95% CI: 0.82-0.85) in cohort 2. Furthermore, the deep learning model effectively differentiated the high-risk group from the low-risk group, achieving an area under the curve (AUC) of 0.89 (95% CI: 0.88-0.90) in cohort 1. In the external validation conducted in cohort 2, the deep learning model displayed an AUC of 0.87 (95% CI: 0.84-0.88) in distinguishing the high-risk group from the low-risk group. The average diagnostic time was 16.00±3.2 seconds, with an accuracy of 0.82 (95% CI: 0.81-0.83).

Conclusions: We have developed a deep learning algorithm, LungPath, for the automated segmentation of pulmonary nodules and prediction of high-risk histological patterns in early-stage lung ADC based on CT scans.

Keywords: Artificial intelligence (AI); early-stage invasive lung adenocarcinoma (early-stage invasive lung ADC); histologic patterns.

Figure 1

The patient inclusion of study. ADC, lung adenocarcinoma.

Figure 2

The work flow and design of the whole deep learning pipeline. CT, computed tomography; ROI, region of interest; W, width; H, height.

Figure 3

The work flow and design of auto-segmentation algorithm. RPN, region proposal network; ResNeXt-50, Residual Network 50.

Figure 4

The work flow and design of classification algorithm. ResNeXt-50, Residual Network 50.

Figure 5

The performance of auto-segmentation deep learning model; the sections were stained as hematoxylin-eosin staining in 5× magnification (low-risk group D) and 10× magnification (high-risk group D). (A) The original CT scan of the nodules; (B) the CT segmentation by radiologists; (C) the CT segmentation by deep learning algorithm; (D) the corresponding pathological performance of the CT scan. CT, computed tomography.

Figure 6

Dice on validation data set changing with training epochs.

Figure 7

Performance of the deep learning algorithm for classifying high- and low-risk lung adenocarcinoma based on CT in test group of cohort 1 and cohort 2; orange curve represents the AUC of cohort 2 while the blue curve represents the AUC of cohort 1. MAUC, mean area under the curve; CI, confidence interval; TPR, true positive rate; FPR, false positive rate; CT, computed tomography; AUC, area under the curve.

Figure 8

Performance of the deep learning algorithm for classifying high- and low-risk lung adenocarcinoma based on CT and EMR data in test set of cohort 1. MAUC, mean area under the curve; CI, confidence interval; TPR, true positive rate; FPR, false positive rate; CT, computed tomography; EMR, electronic medical records.