Preliminary study on prediction model based on CT for pathological complete response of rectal cancer after neoadjuvant chemotherapy_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LI Qian ^1,2 , ZHOU Yifei ³ , LI Zhengyan ⁴ ,  WANG Xiaodong ¹ , GAO Shaobing ³ , LI Li ¹

1. Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. West China School of Medicine in Sichuan University, Chengdu 610041, P. R. China;
3. School of Computer Science, Sichuan University, Chengdu 610065, P. R. China;
4. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

WANG Xiaodong, Email: lockwan@163.com

Keywords：

rectal cancer; neoadjuvant chemotherapy; radiomics; artificial intelligence; decision tree; pathological complete response

DOI：

10.7507/1007-9424.202003073

Video：

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Objective To explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).Methods The clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.Results According to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).Conclusions In this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.

Citation： LI Qian, ZHOU Yifei, LI Zhengyan, WANG Xiaodong, GAO Shaobing, LI Li. Preliminary study on prediction model based on CT for pathological complete response of rectal cancer after neoadjuvant chemotherapy. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(5): 606-611. doi: 10.7507/1007-9424.202003073 Copy

1.	Perez K, Safran H, Sikov W, et al. Complete neoadjuvant treatment for rectal cancer: The Brown University Oncology Group CONTRE Study. Am J Clin Oncol, 2017, 40(3): 283-287.
2.	Rödel C, Martus P, Papadoupolos T, et al. Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. J Clin Oncol, 2005, 23(34): 8688-8696.
3.	Wen B, Zhang L, Wang C, et al. Prognostic significance of clinical and pathological stages on locally advanced rectal carcinoma after neoadjuvant chemoradiotherapy. Radiat Oncol, 2015, 10: 124.
4.	Park IJ, You YN, Agarwal A, et al. Neoadjuvant treatment response as an early response indicator for patients with rectal cancer. J Clin Oncol, 2012, 30(15): 1770-1776.
5.	Maas M, Beets-Tan RG, Lambregts DM, et al. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer. J Clin Oncol, 2011, 29(35): 4633-4640.
6.	Stipa F, Chessin DB, Shia J, et al. A pathologic complete response of rectal cancer to preoperative combined-modality therapy results in improved oncological outcome compared with those who achieve no downstaging on the basis of preoperative endorectal ultrasonography. Ann Surg Oncol, 2006, 13(8): 1047-1053.
7.	Appelt AL, Pløen J, Harling H, et al. High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Lancet Oncol, 2015, 16(8): 919-927.
8.	Habr-Gama A, Gama-Rodrigues J, São Julião GP, et al. Local recurrence after complete clinical response and watch and wait in rectal cancer after neoadjuvant chemoradiation: impact of salvage therapy on local disease control. Int J Radiat Oncol Biol Phys, 2014, 88(4): 822-828.
9.	Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res, 2017, 23(23): 7253-7262.
10.	Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology, 2018, 287(3): 833-843.
11.	Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol, 2019, 29(3): 1211-1220.
12.	Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with. Eur J Nucl Med Mol Imaging, 2019, 46(4): 878-888.
13.	Bibault JE, Giraud P, Housset M, et al. Author correction: deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 16914.
14.	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.
15.	Meng Y, Zhang Y, Dong D, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging, 2018 Feb 13. doi: 10.1002/jmri.25968.
16.	Lovinfosse P, Polus M, Van Daele D, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging, 2018, 45(3): 365-375.
17.	刘健博, 由屹先, 邹雨恒, 等. 数据库辅助研究: 结直肠癌新辅助治疗的应用—基于 DACCA 的华西真实世界数据报道. 中国普外基础与临床杂志, 2019, 26(12): 1467-1475.
18.	刘健博, 吕炘沂, 邹雨恒, 等. 多周期新辅助化疗联合阿帕替尼对进展期直肠癌的疗效评价: 基于 DACCA 数据库的辅助研究. 中国普外基础与临床杂志, 2019, 26(6): 728-734.
19.	Quinlan R. Introduction of decision trees. Machine Learning, 1986, 1: 84-100.
20.	Han JW, Kamber M著; 范明, 孟小峰译. 数据挖掘: 概念与技术. 第3版. 北京: 机械工业出版社, 2008.
21.	Breiman L, Friedman J, Stone CJ, et al. Classification and regression trees. New York: Chapman and Hall, 1984.
22.	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.
23.	于瑞璇, 何欣林, 张秋露, 等. 华西DACCA的辅助研究: 单独的新辅助化疗不会影响直肠癌术后近期的肛门功能. 中国普外基础与临床杂志, 2019, 26(5): 595-601.
24.	Callender GG, Das P, Rodriguez-Bigas MA, et al. Local excision after preoperative chemoradiation results in an equivalent outcome to total mesorectal excision in selected patients with T3 rectal cancer. Ann Surg Oncol, 2010, 17(2): 441-447.
25.	Belluco C, De Paoli A, Canzonieri V, et al. Long-term outcome of patients with complete pathologic response after neoadjuvant chemoradiation for cT3 rectal cancer: implications for local excision surgical strategies. Ann Surg Oncol, 2011, 18(13): 3686-3693.
26.	王鸣飞, 张惠茅. 结直肠癌术前 N 分期的随机森林预测模型的建立与验证. 吉林大学, 2019.
27.	萨莎. 结直肠癌术前 T 分期的随机森林预测模型的建立与验证. 吉林大学, 2018.
28.	陈斌, 苏一丹, 黄山. 基于 KM-SMOTE 和随机森林的不平衡数据分类. 计算机技术与发展, 2015, 25(9): 17-21.
29.	吴琼, 李运田, 郑献卫. 面向非平衡训练集分类的随机森林算法优化. 工业控制计算机, 2013, 26(7): 89-90.
30.	Das P, Skibber JM, et al. Predictors of tumor response and downstaging in patients who receive preoperative chemoradiation for rectal cancer. Cancer, 2007, 109(9): 1750-1755.
31.	Yoon SM, Kim DY, Kim TH, et al. Clinical parameters predicting pathologic tumor response after preoperative chemoradiotherapy for rectal cancer. Int J Radiat Oncol Biol Phys, 2007, 69(4): 1167-1172.
32.	汪晓东, 刘健博, 李立. 数据库建设第五部分•结直肠癌的肿瘤特征—模块的设计(二). 中国普外基础与临床杂志, 优先出版. 2020-03-15.
33.	孟闫凯, 周纯武. 磁共振、影像组学在直肠癌新辅助放化疗疗效评估及预后中的应用. 北京协和医学院, 2018.

1. Perez K, Safran H, Sikov W, et al. Complete neoadjuvant treatment for rectal cancer: The Brown University Oncology Group CONTRE Study. Am J Clin Oncol, 2017, 40(3): 283-287.
2. Rödel C, Martus P, Papadoupolos T, et al. Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. J Clin Oncol, 2005, 23(34): 8688-8696.
3. Wen B, Zhang L, Wang C, et al. Prognostic significance of clinical and pathological stages on locally advanced rectal carcinoma after neoadjuvant chemoradiotherapy. Radiat Oncol, 2015, 10: 124.
4. Park IJ, You YN, Agarwal A, et al. Neoadjuvant treatment response as an early response indicator for patients with rectal cancer. J Clin Oncol, 2012, 30(15): 1770-1776.
5. Maas M, Beets-Tan RG, Lambregts DM, et al. Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer. J Clin Oncol, 2011, 29(35): 4633-4640.
6. Stipa F, Chessin DB, Shia J, et al. A pathologic complete response of rectal cancer to preoperative combined-modality therapy results in improved oncological outcome compared with those who achieve no downstaging on the basis of preoperative endorectal ultrasonography. Ann Surg Oncol, 2006, 13(8): 1047-1053.
7. Appelt AL, Pløen J, Harling H, et al. High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study. Lancet Oncol, 2015, 16(8): 919-927.
8. Habr-Gama A, Gama-Rodrigues J, São Julião GP, et al. Local recurrence after complete clinical response and watch and wait in rectal cancer after neoadjuvant chemoradiation: impact of salvage therapy on local disease control. Int J Radiat Oncol Biol Phys, 2014, 88(4): 822-828.
9. Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res, 2017, 23(23): 7253-7262.
10. Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology, 2018, 287(3): 833-843.
11. Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol, 2019, 29(3): 1211-1220.
12. Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with. Eur J Nucl Med Mol Imaging, 2019, 46(4): 878-888.
13. Bibault JE, Giraud P, Housset M, et al. Author correction: deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 16914.
14. 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.
15. Meng Y, Zhang Y, Dong D, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging, 2018 Feb 13. doi: 10.1002/jmri.25968.
16. Lovinfosse P, Polus M, Van Daele D, et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging, 2018, 45(3): 365-375.
17. 刘健博, 由屹先, 邹雨恒, 等. 数据库辅助研究: 结直肠癌新辅助治疗的应用—基于 DACCA 的华西真实世界数据报道. 中国普外基础与临床杂志, 2019, 26(12): 1467-1475.
18. 刘健博, 吕炘沂, 邹雨恒, 等. 多周期新辅助化疗联合阿帕替尼对进展期直肠癌的疗效评价: 基于 DACCA 数据库的辅助研究. 中国普外基础与临床杂志, 2019, 26(6): 728-734.
19. Quinlan R. Introduction of decision trees. Machine Learning, 1986, 1: 84-100.
20. Han JW, Kamber M著; 范明, 孟小峰译. 数据挖掘: 概念与技术. 第3版. 北京: 机械工业出版社, 2008.
21. Breiman L, Friedman J, Stone CJ, et al. Classification and regression trees. New York: Chapman and Hall, 1984.
22. 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.
23. 于瑞璇, 何欣林, 张秋露, 等. 华西DACCA的辅助研究: 单独的新辅助化疗不会影响直肠癌术后近期的肛门功能. 中国普外基础与临床杂志, 2019, 26(5): 595-601.
24. Callender GG, Das P, Rodriguez-Bigas MA, et al. Local excision after preoperative chemoradiation results in an equivalent outcome to total mesorectal excision in selected patients with T3 rectal cancer. Ann Surg Oncol, 2010, 17(2): 441-447.
25. Belluco C, De Paoli A, Canzonieri V, et al. Long-term outcome of patients with complete pathologic response after neoadjuvant chemoradiation for cT3 rectal cancer: implications for local excision surgical strategies. Ann Surg Oncol, 2011, 18(13): 3686-3693.
26. 王鸣飞, 张惠茅. 结直肠癌术前 N 分期的随机森林预测模型的建立与验证. 吉林大学, 2019.
27. 萨莎. 结直肠癌术前 T 分期的随机森林预测模型的建立与验证. 吉林大学, 2018.
28. 陈斌, 苏一丹, 黄山. 基于 KM-SMOTE 和随机森林的不平衡数据分类. 计算机技术与发展, 2015, 25(9): 17-21.
29. 吴琼, 李运田, 郑献卫. 面向非平衡训练集分类的随机森林算法优化. 工业控制计算机, 2013, 26(7): 89-90.
30. Das P, Skibber JM, et al. Predictors of tumor response and downstaging in patients who receive preoperative chemoradiation for rectal cancer. Cancer, 2007, 109(9): 1750-1755.
31. Yoon SM, Kim DY, Kim TH, et al. Clinical parameters predicting pathologic tumor response after preoperative chemoradiotherapy for rectal cancer. Int J Radiat Oncol Biol Phys, 2007, 69(4): 1167-1172.
32. 汪晓东, 刘健博, 李立. 数据库建设第五部分•结直肠癌的肿瘤特征—模块的设计(二). 中国普外基础与临床杂志, 优先出版. 2020-03-15.
33. 孟闫凯, 周纯武. 磁共振、影像组学在直肠癌新辅助放化疗疗效评估及预后中的应用. 北京协和医学院, 2018.

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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Preliminary study on prediction model based on CT for pathological complete response of rectal cancer after neoadjuvant chemotherapy

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