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. 2018 May;8(4):410-420.
doi: 10.21037/qims.2018.05.01.

Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis

Zhen Hou  1 , Yang Yang  2 , Shuangshuang Li  2 , Jing Yan  2 , Wei Ren  2 , Juan Liu  2 , Kangxin Wang  2 , Baorui Liu  2 , Suiren Wan  1
Affiliations

Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis

Zhen Hou et al. Quant Imaging Med Surg. 2018 May.

Abstract

Background: To determine the feasibility of radiomic analysis for predicting the therapeutic response of gastric carcinoma (GC) with abdominal cavity metastasis (GCACM) to pulsed low dose rate radiotherapy (PLDRT) using contrast-enhanced computed tomography (CECT) images.

Methods: Pretreatment CECT images of 43 GCACM patients were analyzed. Patients with complete response (CR) and partial response (PR) were considered responders, while stable disease (SD) and progressive disease (PD) as non-responders. A total of 1,117 image features were quantified from tumor region that segmented from arterial phase CT images. Intra-class correlation coefficient (ICC) and absolute correlation coefficient (ACC) were calculated for selecting influential feature subset. The capability of each influential feature on treatment response classification was assessed using Kruskal-Wallis test and receiver operating characteristic (ROC) analysis. Moreover, artificial neural network (ANN) and k-nearest neighbor (KNN) predictive models were constructed based on the training set (18 responders, 14 non-responders) and the testing set (6 responders, 5 non-responders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test.

Results: The analyses showed that 6 features (1 first order-based, 1 texture-based, 1 LoG-based, and 3 wavelet-based) were significantly different between responders and non-responders (AUCs range from 0.686 to 0.728). Both two prediction models based on features extracted from CECT showed potential in predicting the treatment response with higher accuracies (ANN: 0.714, KNN: 0.749 for the training set; ANN: 0.816, KNN: 0.816 for the testing set). No statistical difference was observed between the performance of ANN and KNN (P=0.999).

Conclusions: Pretreatment radiomic analysis using CECT can potentially provide important information regarding the therapeutic response to PLDRT for GCACM, improving risk stratification.

Keywords: Gastric carcinoma (GC); abdominal cavity metastasis; predictor; pulsed low dose rate radiotherapy (PLDRT); radiomic analysis; treatment response.

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Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Workflow of radiomic analysis for gastric carcinoma with abdominal cavity metastasis.
Figure 2
Figure 2
Box plots of the amplitude features, successfully differentiating non-responders [stable disease (SD), progressive disease (PD)] from responders [complete response (CR), partial response (PR)]. (A) F_Skewness (P=0.010); (B) LoG2.5_glszm_SALGLE (P=0.013); (C) WHLH_F_Mean (P=0.013); (D) glcm_CS (P=0.037); (E) WLLL_F_Skewness (P=0.037); (F) WHLH_glszm_SAE (P=0.047).
Figure 3
Figure 3
Receiver operating characteristic (ROC) curves on the basis of the significant features.
See this image and copyright information in PMC

References

    1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136:E359-E386. 10.1002/ijc.29210 - DOI - PubMed
    1. Wobb J, Krueger SA, Kane JL, Galoforo S, Grills IS, Wilson GD, Marples B. The effects of pulsed radiation therapy on tumor oxygenation in 2 murine models of head and neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys 2015;92:820-8. 10.1016/j.ijrobp.2015.04.002 - DOI - PubMed
    1. Zhang P, Wang B, Chen X, Cvetkovic D, Chen L, Lang J, Ma CM. Local Tumor Control and Normal Tissue Toxicity of Pulsed Low-Dose Rate Radiotherapy for Recurrent Lung Cancer: An In Vivo Animal Study. Dose Response 2015;13:1559325815588507. 10.1177/1559325815588507 - DOI - PMC - PubMed
    1. Meyer K, Krueger SA, Kane JL, Wilson TG, Hanna A, Dabjan M, Hege KM, Wilson GD, Grills I, Marples B. Pulsed radiation therapy with concurrent cisplatin results in superior tumor growth delay in a head and neck squamous cell carcinoma murine model. Int J Radiat Oncol Biol Phys 2016;96:161-9. 10.1016/j.ijrobp.2016.04.031 - DOI - PubMed
    1. Richards GM, Tomé WA, Robins HI, Stewart JA, Welsh JS, Mahler PA, Howard SP. Pulsed reduced dose-rate radiotherapy: a novel locoregional retreatment strategy for breast cancer recurrence in the previously irradiated chest wall, axilla, or supraclavicular region. Breast Cancer Res Treat 2009;114:307-13. 10.1007/s10549-008-9995-3 - DOI - PubMed