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.	R?del 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, Pl?en 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, S?o Juli?o 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.	劉健博, 由屹先, 鄒雨恒, 等. 數據庫輔助研究: 結直腸癌新輔助治療的應用—基于 DACCA 的華西真實世界數據報道. 中國普外基礎與臨床雜志, 2019, 26(12): 1467-1475.
18.	劉健博, 呂炘沂, 鄒雨恒, 等. 多周期新輔助化療聯合阿帕替尼對進展期直腸癌的療效評價: 基于 DACCA 數據庫的輔助研究. 中國普外基礎與臨床雜志, 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, 孟小峰譯. 數據挖掘: 概念與技術. 第3版. 北京: 機械工業出版社, 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, 張秋露, 等. 華西DACCA的輔助研究: 單獨的新輔助化療不會影響直腸癌術后近期的肛門功能. 中國普外基礎與臨床雜志, 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.	王鳴飛, 張惠茅. 結直腸癌術前 N 分期的隨機森林預測模型的建立與驗證. 吉林大學, 2019.
27.	薩莎. 結直腸癌術前 T 分期的隨機森林預測模型的建立與驗證. 吉林大學, 2018.
28.	陳斌, 蘇一丹, 黃山. 基于 KM-SMOTE 和隨機森林的不平衡數據分類. 計算機技術與發展, 2015, 25(9): 17-21.
29.	吳瓊, 李運田, 鄭獻衛. 面向非平衡訓練集分類的隨機森林算法優化. 工業控制計算機, 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.	汪曉東, 劉健博, 李立. 數據庫建設第五部分?結直腸癌的腫瘤特征—模塊的設計(二). 中國普外基礎與臨床雜志, 優先出版. 2020-03-15.
33.	孟閆凱, 周純武. 磁共振、影像組學在直腸癌新輔助放化療療效評估及預后中的應用. 北京協和醫學院, 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. R?del 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, Pl?en 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, S?o Juli?o 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. 劉健博, 由屹先, 鄒雨恒, 等. 數據庫輔助研究: 結直腸癌新輔助治療的應用—基于 DACCA 的華西真實世界數據報道. 中國普外基礎與臨床雜志, 2019, 26(12): 1467-1475.
18. 劉健博, 呂炘沂, 鄒雨恒, 等. 多周期新輔助化療聯合阿帕替尼對進展期直腸癌的療效評價: 基于 DACCA 數據庫的輔助研究. 中國普外基礎與臨床雜志, 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, 孟小峰譯. 數據挖掘: 概念與技術. 第3版. 北京: 機械工業出版社, 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, 張秋露, 等. 華西DACCA的輔助研究: 單獨的新輔助化療不會影響直腸癌術后近期的肛門功能. 中國普外基礎與臨床雜志, 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. 王鳴飛, 張惠茅. 結直腸癌術前 N 分期的隨機森林預測模型的建立與驗證. 吉林大學, 2019.
27. 薩莎. 結直腸癌術前 T 分期的隨機森林預測模型的建立與驗證. 吉林大學, 2018.
28. 陳斌, 蘇一丹, 黃山. 基于 KM-SMOTE 和隨機森林的不平衡數據分類. 計算機技術與發展, 2015, 25(9): 17-21.
29. 吳瓊, 李運田, 鄭獻衛. 面向非平衡訓練集分類的隨機森林算法優化. 工業控制計算機, 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. 汪曉東, 劉健博, 李立. 數據庫建設第五部分?結直腸癌的腫瘤特征—模塊的設計(二). 中國普外基礎與臨床雜志, 優先出版. 2020-03-15.
33. 孟閆凱, 周純武. 磁共振、影像組學在直腸癌新輔助放化療療效評估及預后中的應用. 北京協和醫學院, 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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