search button
Open Access

Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning

  • Authors:
    • Zhi-Wei Hu
    • Pan Liang
    • Zhi-Li Li
    • Liu-Liang Yong
    • Hao Lu
    • Rui Wang
    • Jian-Bo Gao

  • View Affiliations

  • Published online on: May 22, 2023     https://doi.org/10.3892/ol.2023.13879
  • Article Number: 293

  • Copyright: © Hu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Vessel invasion (VI) is an important factor affecting the prognosis of gastric cancer (GC), and the accurate determination of preoperative VI for locally advanced GC is of great clinical significance. Traditional methods for the evaluation of VI require postoperative pathological examination. Noninvasive preoperative evaluation of VI is therefore crucial to determine the best treatment strategy. To determine the value of preoperative prediction of gastric VI based on portal venous phase computed tomography (CT) radiomic features and machine‑learning models, a retrospective analysis of 296 patients with locally advanced GC confirmed through pathological examination was performed. They were divided into two groups, VI+ (n=213) and VI‑ (n=83), based on pathological results. Using pyradiomics to extract two‑dimensional radiomic features of the portal venous stage of locally advanced GC, data were divided into training (n=207) and validation sets (n=89), with a ratio of 7:3, and three feature selection methods were cascaded and merged. Finally, least absolute shrinkage and selection operator (LASSO) regression was used for feature screening to obtain the optimal feature subset. Four current representative machine‑learning algorithms were used to construct the prediction model, the receiver operating characteristic curve was constructed to evaluate the predictive performance of the model, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. The differentiation degree, and the Lauren's and CA199 classifications were independent risk factors for locally advanced GC VI. Pyradiomics extracted 864 quantitative features of portal vein images of locally advanced GC. After filtering out low variance features using R, 236 features remained. Next, 18 features were screened using the LASSO algorithm. Extreme gradient boosting (XGBoost), logistic regression, Gaussian naive Bayes, and support vector machine models were constructed based on the 18 best features screened out of the portal venous CT images of advanced GC and three independent risk factors of GC VI in clinical features predicted the training set AUC values of 0.914, 0.897, 0.880, and 0.814, respectively. The predicted validation set AUC values were 0.870, 0.877, 0.859, and 0.773, respectively. The DeLong test results indicated no statistically significant difference in AUC values between the XGBoost and logistic regression models in the training and validation sets. The four machine‑learning models showed high predictive performance. The logistic regression model had the highest AUC value in the validation set (0.877), and the accuracy and F1 score were 77 and 87.6%, respectively. CT radiomic features and machine‑learning models based on the portal venous phase can be used as a noninvasive imaging method for the preoperative prediction of VI in locally advanced GC. The logistic regression model exhibited the highest diagnostic performance.
View Figures
View References

Related Articles

Journal Cover

July-2023
Volume 26 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

  • Journal Metrics
  • Impact Factor: 2.9
  • Ranked Oncology
    (total number of cites)
  • CiteScore: 6.3
  • CiteScore Rank: Q2: #119/366 Oncology
  • Source Normalized Impact per Paper (SNIP): 0.725
  • SCImago Journal Rank (SJR): 0.639
International Symposium on Molecular Medicine Conference Banner
The Cancer Story
World Congress on Advances in Oncology and International Symposium on Molecular Medicine Conference Banner
World Academy Of Sciences Banner
Heyday Road Banner

0

Copy and paste a formatted citation
x
Spandidos Publications style
Hu Z, Liang P, Li Z, Yong L, Lu H, Wang R and Gao J: Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning. Oncol Lett 26: 293, 2023
APA
Hu, Z., Liang, P., Li, Z., Yong, L., Lu, H., Wang, R., & Gao, J. (2023). Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning. Oncology Letters, 26, 293. https://doi.org/10.3892/ol.2023.13879
MLA
Hu, Z., Liang, P., Li, Z., Yong, L., Lu, H., Wang, R., Gao, J."Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning". Oncology Letters 26.1 (2023): 293.
Chicago
Hu, Z., Liang, P., Li, Z., Yong, L., Lu, H., Wang, R., Gao, J."Preoperative prediction of vessel invasion in locally advanced gastric cancer based on computed tomography radiomics and machine learning". Oncology Letters 26, no. 1 (2023): 293. https://doi.org/10.3892/ol.2023.13879