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Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging

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Abstract

Objectives

Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer learning (DTL) from contrast-enhanced CT (CECT) imaging.

Methods

A total of 1062 GC patients (728 training and 334 testing) between Jan 2014 and Dec 2018 undergoing gastrectomy were retrospectively included. Based on CECT imaging, we built two gastric imaging (GI) markers, GI-marker-1 from radiomics and GI-marker-2 from DTL features, to decode LVI status. We then integrated demographics, clinical data, GI markers, radiologic interpretation, and biopsies into a Gastric Cancer Risk (GRISK) model for predicting LVI. The performance of GRISK model was tested and applied to predict survival outcomes in GC patients. Furthermore, the prognosis between LVI (+) and LVI (−) patients was compared in chemotherapy and non-chemotherapy cohorts, respectively.

Results

GI-marker-1 and GI-marker-2 yield similar performance in predicting LVI in training and testing dataset. The GRISK model yields the diagnostic performance with AUC of 0.755 (95% CI 0.719–0.790) and 0.725 (95% CI 0.669–0.781) in training and testing dataset. Patients with LVI (+) trend toward lower progression-free survival (PFS) and overall survival (OS). The difference of prognosis between LVI (+) and LVI (−) was more noticeable in non-chemotherapy than that in chemotherapy group.

Conclusion

Radiomics and deep transfer learning features on CECT demonstrate potential power for predicting LVI in GC patients. Prospective use of a GRISK model can help to optimize individualized treatment decisions and predict survival outcomes.

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Funding

This study is supported by the Key Social Development Program for the Ministry of Science and Technology of Jiangsu Province (BE2017756, YDZ).

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Authors and Affiliations

  1. Department of Radiology, the First Affiliated Hospital With Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210000, Jiangsu, China

    Qiong Li, Qiu-Xia Feng, Liang Qi, Chang Liu, Yu-Dong Zhang & Xi-Sheng Liu

  2. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China

    Jing Zhang & Guang Yang

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  1. Qiong Li
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Contributions

Conception and design: YDZ and XSL. Development of methodology: QL, QXF, CL, LQ, JZ, GY, YDZ, and XSL. Acquisition of data (acquired and managed patients, provided facilities, etc.): QL, QXF, CL, LQ, YDZ, and XSL. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): QL, LQ, QXF, CL, JZ, GY, YDZ, and XSL. Writing, reviewing, and/or revision of the manuscript: QL and YDZ. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): QL, LQ, QXF, CL, JZ, GY, YDZ, and XSL. Study supervision and guarantors: YDZ and XSL.

Corresponding authors

Correspondence to Yu-Dong Zhang or Xi-Sheng Liu.

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Conflict of interest

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 entitled “Prognostic Aspects of Lymphovascular Invasion in Localized Gastric Cancer: New Insights Into the Radiomics and Deep Transfer Learning from Contrast-Enhanced CT Imaging.” We declare that we had full access to all of the data in this study and take complete responsibility for the integrity of the data and the accuracy of the data analysis.

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Li, Q., Feng, QX., Qi, L. et al. Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging. Abdom Radiol 47, 496–507 (2022). https://doi.org/10.1007/s00261-021-03309-z

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