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Machine Learning-Based Computational Models Derived from Large-Scale Radiographic-Radiomic Images Can Help to Predict Adverse Histopathological Status of Gastric Cancer

45 Pages Posted: 8 Mar 2019

See all articles by Qiong Li

Qiong Li

Nanjing Medical University

Liang Qi

Nanjing Medical University

Qiu-Xia Feng

Nanjing Medical University

Chang Liu

Nanjing Medical University

Shu-Wen Sun

Nanjing Medical University

Jing Zhang

East China Normal University (ECNU)

Guang Yang

East China Normal University (ECNU) - Shanghai Key Laboratory of Magnetic Resonance

Ying-Qian Ge

Siemens Healthcare

Yu-Dong Zhang

Nanjing Medical University - Department of Radiology

Xi-Sheng Liu

Nanjing Medical University - Department of Radiology

More...

Abstract

Background: Adverse histopathologic status (AHS) decreases surgical outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced CT, to predict AHS and clinical outcomes of patients with GC.    

Methods: 554 GC patients (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six Radiomic scores (R-scores) related to pT stage, pN stage, Lauren& Borrmann (L&B) classification, WHO grade, lymphatic vascular infiltration (LVI) and an overall histopathologic score (H-score) were respectively built from 7000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS and outcomes of patients after surgery.

Findings: Radiomics related to tumor gray level intensity, size and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (p < 0.001). Regression analysis identified 5 independent predictors for pT and pN stage, 2 predictors for L&B classification, WHO grade and LVI, and 3 predictors for H-score, respectively. Area under curve (AUC) of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69 and 0.84/0.77, respectively. Patients' recurrence-free survival (RFS) and overall survival (OS) were significantly different between predicted ASH-absent and ASH-present groups. H-score, histopathologic pT and pN status were independently associated with disease-special recurrence and mortality of the patients after surgery.      

Interpretation: The developed computational approach demonstrates good performance for successfully decoding ASH of GC and preoperatively predicting disease clinical outcomes.

Funding Statement: This study is supported by China Postdoctoral Fund (2015M580453, YDZ) and a Key Social Development Program for the Ministry of Science and Technology of Jiangsu Province (BE2017756, YDZ)

Declaration of Interests: We declare that all authors have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript.

Ethics Approval Statement: Ethics committee approval was granted by local institutional ethics review board with a waiver of written informed consent. All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments.

Keywords: Computed tomography; Histopathological status; Gastric cancer; Radiography; Radiomics

Suggested Citation

Li, Qiong and Qi, Liang and Feng, Qiu-Xia and Liu, Chang and Sun, Shu-Wen and Zhang, Jing and Yang, Guang and Ge, Ying-Qian and Zhang, Yu-Dong and Liu, Xi-Sheng, Machine Learning-Based Computational Models Derived from Large-Scale Radiographic-Radiomic Images Can Help to Predict Adverse Histopathological Status of Gastric Cancer (March 6, 2019). Available at SSRN: https://ssrn.com/abstract=3347895 or http://dx.doi.org/10.2139/ssrn.3347895

Qiong Li

Nanjing Medical University ( email )

300 Guangzhou Road
Nanjing, Jiangsu 210029
China

Liang Qi

Nanjing Medical University ( email )

300 Guangzhou Road
Nanjing, Jiangsu 210029
China

Qiu-Xia Feng

Nanjing Medical University ( email )

300 Guangzhou Road
Nanjing, Jiangsu 210029
China

Chang Liu

Nanjing Medical University ( email )

300 Guangzhou Road
Nanjing, Jiangsu 210029
China

Shu-Wen Sun

Nanjing Medical University ( email )

300 Guangzhou Road
Nanjing, Jiangsu 210029
China

Jing Zhang

East China Normal University (ECNU) ( email )

North Zhongshan Road Campus
3663 N. Zhongshan Rd.
Shanghai, 200062
China

Guang Yang

East China Normal University (ECNU) - Shanghai Key Laboratory of Magnetic Resonance ( email )

China

Ying-Qian Ge

Siemens Healthcare ( email )

Shanghai
China

Yu-Dong Zhang (Contact Author)

Nanjing Medical University - Department of Radiology ( email )

No. 300, Guangzhou Road
Nanjing, 210029
China

Xi-Sheng Liu

Nanjing Medical University - Department of Radiology ( email )

No. 300, Guangzhou Road
Nanjing, 210029
China

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