Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.
In: Clinical and translational gastroenterology, Jg. 10 (2019-10-01), Heft 10, S. e00079
Online
academicJournal
Zugriff:
Introduction: Adverse histopathological status (AHS) decreases 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 computed tomography, to predict AHS and clinical outcomes of patients with GC.
Methods: Five hundred fifty-four patients with GC (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, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes.
Results: 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 stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve 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. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification.
Discussion: The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.
Titel: |
Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.
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Autor/in / Beteiligte Person: | Li, Q ; Qi, L ; Feng, QX ; Liu, C ; Sun, SW ; Zhang, J ; Yang, G ; Ge, YQ ; Zhang, YD ; Liu, XS |
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Zeitschrift: | Clinical and translational gastroenterology, Jg. 10 (2019-10-01), Heft 10, S. e00079 |
Veröffentlichung: | <2019-> : [Philadelphia, PA] : Wolters Kluwer Health ; <i>Original Publication</i>: New York, NY : Nature Pub. Group, 2019 |
Medientyp: | academicJournal |
ISSN: | 2155-384X (electronic) |
DOI: | 10.14309/ctg.0000000000000079 |
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