Predictive value of CT for neoadjuvant chemotherapy in advanced gastric cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHAO Fukun ¹ , DONG Hexiang ¹ , ZHU Xiang ¹ , WANG Weigang ¹ ,  YU Yongjiang ^1,2 , ZHANG Xiaobei ^1,2

1. The First Clinical Medical College of Lanzhou University, Lanzhou 730000, P. R. China;
2. Department of Gastroenterology, Hernia, and Abdominal Wall Surgery, First Hospital of Lanzhou University, Lanzhou 730000, P. R. China;

Corresponding author：

YU Yongjiang, Email: ylongy@163.com

Keywords：

gastric cancer; multi slice spiral CT; neoadjuvant chemotherapy; imaging omics

DOI：

10.7507/1007-9424.202301001

Video：

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Objective To explore the value of multi-slice spiral CT (MSCT) 3D imaging in evaluating the efficacy of neoadjuvant chemotherapy for advanced gastric cancer. Methods Sixty-one patients with gastric cancer diagnosed by gastroscopy and pathological examination at the First Hospital of Lanzhou University from January 2019 to March 2022 were divided into chemotherapy effective group (n=39) and ineffective group (n=22) according to postoperative pathological regression grade (tumor regression grade, TRG) standards. MSCT was performed before neoadjuvant chemotherapy and before undergoing surgical treatment after neoadjuvant chemotherapy. The independent predictors related to the efficacy of chemotherapy were screened by binary logistics regression analysis of CT conventional observation indexes (including maximum tumor thickness, gastric wall motility, enhancement mode, lymph node metastasis, distant metastasis, peritoneal thickening or peritoneal nodules). Tumor volume and maximum tumor thickness were measured with the imaging histology software ITK-snap, and the diagnostic efficacy of tumor volume and CT conventional observation indexes was analyzed. Results In the evaluation of chemotherapy efficacy, tumor volume reduction rate and tumor maximum thickness reduction rate can evaluate the efficacy of chemotherapy to a certain extent (P< 0.01). The statistically significant indicators (tumor maximum thickness reduction rate, gastric wall motility, lesion intensification mode and peritoneal thickening and nodules) were analyzed by univariate analysis and binary logistic regression. The results showed that gastric wall motility [OR=0.294, 95%CI (0.093, 0.928), P=0.037] and maximum tumor thickness reduction rate [OR=0.282, 95%CI (0.083, 0.957), P=0.042] were independent predictors of the efficacy of neoadjuvant chemotherapy for progressive gastric cancer. Receiver operating characteristic (ROC) curve were plotted based on the predicted probability variable obtained from both and the results showed that the area under curve (AUC=0.900) , sensitivity (83.3%), and specificity (99.8%) of the tumor volume reduction rate were all higher than those of CT clinical index prediction probability variables (AUC=0.802, sensitivity was 58.3%, specificity was 85.7%). Conclusion The measurement of tumor volume by MSCT combined with the imaging omics software ITK-snap provides an objective basis for the prediction of the efficacy of neoadjuvant chemotherapy, and its diagnostic efficacy is better.

Citation： ZHAO Fukun, DONG Hexiang, ZHU Xiang, WANG Weigang, YU Yongjiang, ZHANG Xiaobei. Predictive value of CT for neoadjuvant chemotherapy in advanced gastric cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2023, 30(6): 715-720. doi: 10.7507/1007-9424.202301001 Copy

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2.	Smyth EC, Nilsson M, Grabsch HI, et al. Gastric cancer. Lancet, 2020, 396(10251): 635-648.
3.	Takahari D, Mizusawa J, Koizumi W, et al. Validation of the JCOG prognostic index in advanced gastric cancer using individual patient data from the SPIRITS and G-SOX trials. Gastric Cancer, 2017, 20(5): 757-763.
4.	马东. SOX新辅助化疗方案联合胃癌根治术治疗进展期胃癌的临床评价. 医学理论与实践, 2020, 33(18): 3032-3034.
5.	Chen L, Hao Y, Zhu L, et al. Monocyte to lymphocyte ratio predicts survival in patients with advanced gastric cancer undergoing neoadjuvant chemotherapy. Onco Targets Ther, 2017, 10: 4007-4016.
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7.	Pavel M, Öberg K, Falconi M, et al. Gastroenteropancreatic neuroendocrine neoplasms: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2020, 31(7): 844-860.
8.	中国抗癌协会胃癌专业委员会. 局部进展期胃癌围手术期治疗中国专家共识(2021版). 中华胃肠外科杂志, 2021, 24(9): 741-748.
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10.	Chen C, Dong H, Shou C, et al. The correlation between computed tomography volumetry and prognosis of advanced gastric cancer treated with neoadjuvant chemotherapy. Cancer Manag Res, 2020, 12: 759-768.
11.	Chen XL, Pu H, Yin LL, et al. CT volumetry for gastric adenocarcinoma: association with lymphovascular invasion and T-stages. Oncotarget, 2017, 9(15): 12432-12442.
12.	Wang ZC, Wang C, Ding Y, et al. CT volumetry can potentially predict the local stage for gastric cancer after chemotherapy. Diagn Interv Radiol, 2017, 23(4): 257-262.
13.	Lee SM, Kim SH, Lee JM, et al. Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer. Abdom Imaging, 2009, 34(4): 430-440.
14.	Lundsgaard Hansen M, Fallentin E, Lauridsen C, et al. Computed tomography (CT) perfusion as an early predictive marker for treatment response to neoadjuvant chemotherapy in gastroesophageal junction cancer and gastric cancer-a prospective study. PLoS One, 2014, 9(5): e97605. doi: 10.1371/journal.pone.0097605.
15.	Giganti F, Antunes S, Salerno A, et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol, 2017, 27(5): 1831-1839.
16.	Chen CY, Hsu JS, Wu DC, et al. Gastric cancer: preoperative local staging with 3D multi-detector row CT-correlation with surgical and histopathologic results. Radiology, 2007, 242(2): 472-482.
17.	Kim SH, Kim SH, Kim MA, et al. CT differentiation of poorly-differentiated gastric neuroendocrine tumours from well-differentiated neuroendocrine tumours and gastric adenocarcinomas. Eur Radiol, 2015, 25(7): 1946-1957.
18.	Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264.
19.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
20.	Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol, 2017, 28(6): 1191-1206.
21.	Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164.
22.	Choi ER, Lee HY, Jeong JY, et al. Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma. Oncotarget, 2016, 7(41): 67302-67313.
23.	Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol, 2017, 72(1): 3-10.

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin, 2020, 70(1): 7-30.
2. Smyth EC, Nilsson M, Grabsch HI, et al. Gastric cancer. Lancet, 2020, 396(10251): 635-648.
3. Takahari D, Mizusawa J, Koizumi W, et al. Validation of the JCOG prognostic index in advanced gastric cancer using individual patient data from the SPIRITS and G-SOX trials. Gastric Cancer, 2017, 20(5): 757-763.
4. 马东. SOX新辅助化疗方案联合胃癌根治术治疗进展期胃癌的临床评价. 医学理论与实践, 2020, 33(18): 3032-3034.
5. Chen L, Hao Y, Zhu L, et al. Monocyte to lymphocyte ratio predicts survival in patients with advanced gastric cancer undergoing neoadjuvant chemotherapy. Onco Targets Ther, 2017, 10: 4007-4016.
6. 王辉, 孟松, 李超, 等. SOX方案新辅助化疗在进展期胃癌中的疗效. 临床医学研究与实践, 2019, 4(8): 7-9.
7. Pavel M, Öberg K, Falconi M, et al. Gastroenteropancreatic neuroendocrine neoplasms: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2020, 31(7): 844-860.
8. 中国抗癌协会胃癌专业委员会. 局部进展期胃癌围手术期治疗中国专家共识(2021版). 中华胃肠外科杂志, 2021, 24(9): 741-748.
9. Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada[J]. J Natl Cancer Inst. 2000.92(03): 205-216.
10. Chen C, Dong H, Shou C, et al. The correlation between computed tomography volumetry and prognosis of advanced gastric cancer treated with neoadjuvant chemotherapy. Cancer Manag Res, 2020, 12: 759-768.
11. Chen XL, Pu H, Yin LL, et al. CT volumetry for gastric adenocarcinoma: association with lymphovascular invasion and T-stages. Oncotarget, 2017, 9(15): 12432-12442.
12. Wang ZC, Wang C, Ding Y, et al. CT volumetry can potentially predict the local stage for gastric cancer after chemotherapy. Diagn Interv Radiol, 2017, 23(4): 257-262.
13. Lee SM, Kim SH, Lee JM, et al. Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer. Abdom Imaging, 2009, 34(4): 430-440.
14. Lundsgaard Hansen M, Fallentin E, Lauridsen C, et al. Computed tomography (CT) perfusion as an early predictive marker for treatment response to neoadjuvant chemotherapy in gastroesophageal junction cancer and gastric cancer-a prospective study. PLoS One, 2014, 9(5): e97605. doi: 10.1371/journal.pone.0097605.
15. Giganti F, Antunes S, Salerno A, et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol, 2017, 27(5): 1831-1839.
16. Chen CY, Hsu JS, Wu DC, et al. Gastric cancer: preoperative local staging with 3D multi-detector row CT-correlation with surgical and histopathologic results. Radiology, 2007, 242(2): 472-482.
17. Kim SH, Kim SH, Kim MA, et al. CT differentiation of poorly-differentiated gastric neuroendocrine tumours from well-differentiated neuroendocrine tumours and gastric adenocarcinomas. Eur Radiol, 2015, 25(7): 1946-1957.
18. Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264.
19. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
20. Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol, 2017, 28(6): 1191-1206.
21. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, 34(18): 2157-2164.
22. Choi ER, Lee HY, Jeong JY, et al. Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma. Oncotarget, 2016, 7(41): 67302-67313.
23. Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol, 2017, 72(1): 3-10.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Predictive value of CT for neoadjuvant chemotherapy in advanced gastric cancer

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