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Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection

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Abstract

Background

Controversy still exists on the optimal surgical resection for potentially curable gastric cancer (GC). Use of radiologic evaluation and machine learning algorithms might predict extent of lymphadenectomy to limit unnecessary surgical treatment. We purposed to design a machine learning-based clinical decision-support model for predicting extent of lymphadenectomy (D1 vs. D2) in local advanced GC.

Methods

Clinicoradiologic features available from routine clinical assignments in 557 patients with GCs were retrospectively interpreted by an expert panel blinded to all histopathologic information. All patients underwent surgery using standard D2 resection. Decision models were developed with a logistic regression (LR), support vector machine (SVM) and auto-encoder (AE) algorithm in 371 training and tested in 186 test data, respectively. The primary end point was to measure diagnostic performance of decision model and a Japanese gastric cancer treatment guideline version 4th (JPN 4th) criteria for discriminate D1 (pT1 + pN0) versus D2 (≥ pT1 + ≥ pN1) lymphadenectomy.

Results

The decision model with AE analysis produced highest area under ROC curve (train: 0.965, 95% confidence interval (CI) 0.948–0.978; test: 0.946, 95% CI 0.925–0.978), followed by SVM (train: 0.925, 95% CI 0.902–0.944; test: 0.942, 95% CI 0.922–0.973) and LR (train: 0.886, 95% CI 0.858–0.910; test: 0.891, 95% CI 0.891–0.952). By this improvement, overtreatment was reduced from 21.7% (121/557) by treat-all pattern, to 15.1% (84/557) by JPN 4th criteria, and to 0.7–0.9% (4–5/557) by the new approach.

Conclusions

The decision model with machine learning analysis demonstrates high accuracy for identifying patients who are candidates for D1 versus D2 resection. Its approximate 14–20% improvements in overtreatment compared to treat-all pattern and JPN 4th criteria potentially increase the number of patients with local advanced GCs who can safely avoid unnecessary lymphadenectomy.

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Funding

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

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Author notes

  1. Dr. CL and Dr. LQ contribute equally to the article in development of methodology, acquisition and analysis of data. They are co-first authors.

Authors and Affiliations

  1. Department of Radiology, TongDe Hospital of ZheJiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China

    Chang Liu

  2. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210009, Jiangsu, China

    Liang Qi, Qiu-Xia Feng, Shu-Wen Sun, Yu-Dong Zhang & Xi-Sheng Liu

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

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The study was approved by the Institutional Review Board of the First Affiliated Hospital of Nanjing Medical University.

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Liu, C., Qi, L., Feng, QX. et al. Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection. Abdom Radiol 44, 3019–3029 (2019). https://doi.org/10.1007/s00261-019-02098-w

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