Deep learning of bone metastasis in small cell lung cancer: A large sample-based study

Qing Chen; Haifeng Liang; Lei Zhou; Hongwei Lu; Fancheng Chen; Yuxiang Ge; Zhichao Hu; Ben Wang; Annan Hu; Wei Hong; Libo Jiang; Jian Dong

doi:10.3389/fonc.2023.1097897

Deep learning of bone metastasis in small cell lung cancer: A large sample-based study

Front Oncol. 2023 Jan 27:13:1097897. doi: 10.3389/fonc.2023.1097897. eCollection 2023.

Authors

Qing Chen^{1

2}, Haifeng Liang^{1

2}, Lei Zhou^{1

2}, Hongwei Lu^{1

2

3

4}, Fancheng Chen^{1

2

3

4}, Yuxiang Ge³, Zhichao Hu^{1

2}, Ben Wang^{1

2}, Annan Hu^{1

2}, Wei Hong^{5

6}, Libo Jiang^{1

2}, Jian Dong^{1

2

4}

Affiliations

¹ Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
² Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
³ Department of Orthopedics Surgery, Minhang Hospital, Fudan University, Shanghai, China.
⁴ Department of Orthopedic Surgery, Zhongshan Hospital Wusong Branch, Fudan University, Shanghai, China.
⁵ Department of Geriatrics and Gerontology, Huadong Hospital, Fudan University, Shanghai, China.
⁶ Shanghai Key Laboratory of Clinical Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, China.

Abstract

Introduction: Bone is a common metastatic site for small cell lung cancer (SCLC). Bone metastasis (BM) in patients have are known to show poor prognostic outcomes. We explored the epidemiological characteristics of BM in SCLC patients and create a new deep learning model to predict outcomes for cancer-specific survival (CSS) and overall survival (OS).

Materials and methods: Data for SCLC patients diagnosed with or without BM from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox proportional hazards regression models were used to evaluate the effects of prognostic variables on OS and CSS. Through integration of these variables, nomograms were created for the prediction of CSS and OS rates at 3-month,6- month,and 12-month. Harrell's coordination index, calibration curves,and time- dependent ROC curves were used to assess the nomograms' accuracy. Decision tree analysis was used to evaluate the clinical application value of the established nomogram.

Results: In this study, 4201 patients were enrolled. Male sex, tumor size 25 but <10, brain and liver metastases, as well as chemotherapy were associated with a high risk for BM. Tumor size, Age, N stage, gender, liver metastasis, radiotherapy as well as chemotherapy were shown to be prognostic variables for OS, and the prognostic variables for CSS were added to the tumor number in addition. Based on these results, nomograms for CSS and OS were established separately. Internal as well as external validation showed that the C-index, calibration cuurve and DCA had good constructive correction effect and clinical application value. Decision tree analysis further confirmed the prognostic factors of OS and CSS.

Discussion: The nomogram and decision tree models developed in this study effectively guided treatment decisions for SCLC patients with BM. The creation of prediction models for BM SCLC patients may be facilitated by deep learning models.

Keywords: bone metastasis; decision tree; nomogram; small cell lung cancer; survival.

Grants and funding

This study was supported by the National Natural Science Foundation of China (grant no. 81972508, 82172738).