Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study

Abstract

Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients.

Methods: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype.

Findings: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all).

Interpretation: The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients.

Funding: This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).

Keywords: CT image; Multi-task learning; Prognosis; Resected NSCLC; Self-supervised pre-training.

Fig. 1

Study design for the development and validation of the IPES. IPES consists of self-supervised pre-training, multi-task learning-based finetuning, and internal and external validation.

Fig. 2

IPES performance on predicting OS risk of stage I-III resected NSCLC patients. (a) and (b) Kaplan–Meier curves of the low- and high-risk subgroups predicted by IPES for stage I-III resected NSCLC patients in the (a) internal validation set and (b) external validation set. (c) and (d) Kaplan–Meier curves of the low- and high-risk subgroups predicted by IPES for resected NSCLC patients in (c) stage I and (d) stage II-III in the internal validation set. In the Kaplan–Meier curves, the horizontal axis represents survival time (months) and the vertical axis represents survival probability.

Fig. 3

IPES performance on EGFR genotype prediction and prognosis analysis of stage I-III resected NSCLC patients with EGFR mutation or EGFR wild-type status. (a) and (b) ROC curves represent the EGFR genotype detection performance of IPES, metadata-based model and the combined model in the (a) internal validation set and (b) external validation set. (c) and (d) Kaplan–Meier curves of the low- and high-risk subgroups predicted by IPES for stage I-III resected NSCLC patients with (c) EGFR mutation and (d) EGFR wild-type status in the internal validation set. In the Kaplan–Meier curves, the horizontal axis represents survival time (months) and the vertical axis represents survival probability.

Fig. 4

IPES performance on predicting OS risk of stage I and II-III resected NSCLC patients with EGFR mutation or EGFR wild-type status. (a) and (b) Kaplan–Meier curves of the low- and high-risk subgroups predicted by IPES for resected NSCLC patients with EGFR mutation in (a) stage I and (b) stage II-III in the internal validation set. (c) and (d) Kaplan–Meier curves of the low- and high-risk subgroups predicted by IPES for resected NSCLC patients with EGFR wild-type status in (c) stage I and (d) stage II-III in the internal validation set. In the Kaplan–Meier curves, the horizontal axis represents survival time (months) and the vertical axis represents survival probability.

Fig. 5

Network visualization and interpretation. Visual explanations of the areas in the CT images identified by IPES. The first column in subfigure is the original CT image, the second column in subfigure is the saliency map overlaying the original CT image. In each original CT image of (a–f), the lung tumor has been marked with a red bounding box.