Database research part <b>Ⅵ</b>: staging strategies for colorectal cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

WANG Xiaodong ¹ , LIU Jianbo ^1,2 , HE Xinlin ^1,2 , ZOU Yuheng ^1,2 ,  LI Li ¹

1. Department of Gastrointestinal Surgery, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
2. West China School of Medicine, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

LI Li, Email: drlili116@126.com

Keywords：

colorectal cancer; database; bigdata; staging; pathology; imageology

DOI：

10.7507/1007-9424.202005023

Video：

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Objective To analyze the staging methods of colorectal cancer data in the current version of the Database from Colorectal Cancer (DACCA).Methods The DACCA version selected for this data analysis was updated at April 16th, 2020. The columns included stage during surgery, comprehensive stage of clinical, pathologic and imaging (cpi comprehensive stage), TNM stage, pathologic T stage, imaging T stage, nerves involvement, pathologic anus stage, clinical anus stage, imaging anus stage, pathologic mesentery stage, clinical mesentery stage, imaging mesentery stage, pathologic N stage, imaging N stage, positive lymph nodes ratio, cancerous nodules, M stage, cancerous emboli, pathologic vessel stage, clinical vessel stage, imaging vessel stage, cancerous contamination, and high-risk factors. Extracted data were statistically analyzed.Results The total number of data medical records (data rows) that met the criteria was 6 474, the valid data of TNM stage was 4 511 (69.7%), the valid data of stage during surgery was 5 684 (87.8%), and the valid data of cpi comprehensive stage was 4 045 (62.5%). 1 540 data (41.6%) were consistent with stage during surgery and TNM stage, and 2 884 data (76.7%) were consistent with cpi comprehensive stage and TNM stage. According to the data of T, N, and M stage, the proportion of patients with pathologic T4a stage was the highest (40.5%), followed by T3 stage (24.8%); the most T4a stage (31.9%) on the image, followed by T4b stage (28.7%). The pathologic N stage with lymph node metastasis was about 41.9% (N1 and N2), and the imaging N stage lymph node metastasis was about 51.4%. There were a total of 4 745 valid data in the M stage (73.3%). There were 4 313 valid data in the nerves involvement (66.7%), suspected involvement and confirmed involvement, were 691 (16.0%) and 253 (5.9%) respectively. The valid data of anal pathology, clinical, and imaging stage were 4 115 (63.6%), 599 (9.3%), and 598 (9.2%), and only 30 (0.7%), 8 (1.3%), and 13 (2.2%) on muscle involvement respectively. The valid data of pathologic, clinical, and imaging mesentery stage were 732 (11.3%), 589 (9.1%), and 592 (9.1%). There were 4 458 (68.9%) valid data of positive lymph nodes ratio, and 2 908 (44.9%) valid data of cancerous nodules. There were 4 286 valid data of cancerous emboli (66.2%). A total of 244 data (41.1%) of increased blood vessels around tumors in the imaging vessel stage, 274 data (46.4%) of that in clinical vessel stage, and only 1 063 (27.7%) of pathologic vessel stage. There were 3 865 valid data (59.7%) of the cancerous contamination, and the proportion of the third level (746/2 753, 27.1%) in the high-risk factors was the highest.Conclusion Through detailed analysis of the DACCA database, it is hoped that a more complete and accurate evaluation system of tumor severity can be established, and high-risk factors can provide some ideas for judging prognosis.

Citation： WANG Xiaodong, LIU Jianbo, HE Xinlin, ZOU Yuheng, LI Li. Database research part Ⅵ: staging strategies for colorectal cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(6): 739-746. doi: 10.7507/1007-9424.202005023 Copy

1.	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.
2.	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.
3.	汪晓东, 安丽珉, 李立. 数据库研究第二部分: 结直肠癌的住院流程管理. 中国普外基础与临床杂志, 2019, 26(4): 469-475.
4.	汪晓东, 李立. 数据库建设第一部分: 个人数据的标签与结构化. 中国普外基础与临床杂志, 2019, 26(3): 335-342.
5.	汪晓东, 李立. 真实场景与大数据下的整体微创理念, 大幅提高结直肠癌远期生存率. 中国普外基础与临床杂志, 2019, 26(1): 92-95.
6.	汪晓东, 李希, 何欣林, 等. 数据库研究第一部分: 区域性医疗中心的结直肠癌与人群特征. 中国普外基础与临床杂志, 2019, 26(2): 212-220.
7.	汪晓东, 刘健博, 安丽珉, 等. 数据库建设第四部分: 结直肠癌的术前专科检查与评估的标签与结构化. 中国普外基础与临床杂志, 2019, 26(11): 1337-1341.
8.	汪晓东, 刘健博, 安丽珉, 等. 数据库研究第四部分: 结直肠癌的术前专科检查与评估. 中国普外基础与临床杂志, 2019, 26(10): 1230-1238.
9.	汪晓东, 刘健博, 李立. 数据库建设第三部分: 结直肠癌内外科合并症及术前体质状态的标签与结构化. 中国普外基础与临床杂志, 2019, 26(9): 1110-1115.
10.	汪晓东, 刘健博, 张诗, 等. 数据库研究第三部分: 结直肠癌的内外科合并症及术前体质状态. 中国普外基础与临床杂志, 2019, 26(8): 985-991.
11.	汪晓东, 吕炘沂, 黄明君, 等. 数据库建设第二部分: 结直肠癌住院流程管理的标签与结构化. 中国普外基础与临床杂志, 2019, 26(7): 852-855.
12.	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.
13.	汪晓东, 陈梦然, 曾渝, 等. 数据库研究第五部分: 结直肠癌的肿瘤特征. 中国普外基础与临床杂志, 2020, 27(2): 196-208.
14.	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, 2020, 27(4): 483-488.
15.	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 (一). 中国普外基础与临床杂志, 2020, 27(3): 345-349.
16.	Weiser MR. AJCC 8th edition: colorectal cancer. Ann Surg Oncol, 2018, 25(6): 1454-1455.
17.	Rudy DR, Zdon MJ. Update on colorectal cancer. Am Fam Physician, 2000, 61(6): 1759-1770, 1773-1774.
18.	Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet, 2014, 383(9927): 1490-1502.
19.	Maguire A, Sheahan K. Controversies in the pathological assessment of colorectal cancer. World J Gastroenterol, 2014, 20(29): 9850-9861.
20.	Shia J, Klimstra DS, Bagci P, et al. TNM staging of colorectal carcinoma: issues and caveats. Semin Diagn Pathol, 2012, 29(3): 142-153.
21.	Berho M, Bejarano PA. Rectal cancer and the pathologist. Minerva Chir, 2018, 73(6): 534-547.
22.	Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities. J Pathol Inform, 2018, 9: 38.
23.	Nayak J, Goel S. Revised TNM staging for colorectal cancer: did we miss the golden opportunity to do right by the staging? Clin Colorectal Cancer, 2011, 10(3): 207-209.
24.	Cavallaro U, Christofori G. Molecular mechanisms of tumor angiogenesis and tumor progression. J Neurooncol, 2000, 50(1-2): 63-70.
25.	Wang X, Yu Y, Meng W, et al. Total neoadjuvant treatment (CAPOX plus radiotherapy) for patients with locally advanced rectal cancer with high risk factors: a phase 2 trial. Radiother Oncol, 2018, 129(2): 300-305.

1. 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.
2. 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.
3. 汪晓东, 安丽珉, 李立. 数据库研究第二部分: 结直肠癌的住院流程管理. 中国普外基础与临床杂志, 2019, 26(4): 469-475.
4. 汪晓东, 李立. 数据库建设第一部分: 个人数据的标签与结构化. 中国普外基础与临床杂志, 2019, 26(3): 335-342.
5. 汪晓东, 李立. 真实场景与大数据下的整体微创理念, 大幅提高结直肠癌远期生存率. 中国普外基础与临床杂志, 2019, 26(1): 92-95.
6. 汪晓东, 李希, 何欣林, 等. 数据库研究第一部分: 区域性医疗中心的结直肠癌与人群特征. 中国普外基础与临床杂志, 2019, 26(2): 212-220.
7. 汪晓东, 刘健博, 安丽珉, 等. 数据库建设第四部分: 结直肠癌的术前专科检查与评估的标签与结构化. 中国普外基础与临床杂志, 2019, 26(11): 1337-1341.
8. 汪晓东, 刘健博, 安丽珉, 等. 数据库研究第四部分: 结直肠癌的术前专科检查与评估. 中国普外基础与临床杂志, 2019, 26(10): 1230-1238.
9. 汪晓东, 刘健博, 李立. 数据库建设第三部分: 结直肠癌内外科合并症及术前体质状态的标签与结构化. 中国普外基础与临床杂志, 2019, 26(9): 1110-1115.
10. 汪晓东, 刘健博, 张诗, 等. 数据库研究第三部分: 结直肠癌的内外科合并症及术前体质状态. 中国普外基础与临床杂志, 2019, 26(8): 985-991.
11. 汪晓东, 吕炘沂, 黄明君, 等. 数据库建设第二部分: 结直肠癌住院流程管理的标签与结构化. 中国普外基础与临床杂志, 2019, 26(7): 852-855.
12. 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.
13. 汪晓东, 陈梦然, 曾渝, 等. 数据库研究第五部分: 结直肠癌的肿瘤特征. 中国普外基础与临床杂志, 2020, 27(2): 196-208.
14. 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, 2020, 27(4): 483-488.
15. 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 (一). 中国普外基础与临床杂志, 2020, 27(3): 345-349.
16. Weiser MR. AJCC 8th edition: colorectal cancer. Ann Surg Oncol, 2018, 25(6): 1454-1455.
17. Rudy DR, Zdon MJ. Update on colorectal cancer. Am Fam Physician, 2000, 61(6): 1759-1770, 1773-1774.
18. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet, 2014, 383(9927): 1490-1502.
19. Maguire A, Sheahan K. Controversies in the pathological assessment of colorectal cancer. World J Gastroenterol, 2014, 20(29): 9850-9861.
20. Shia J, Klimstra DS, Bagci P, et al. TNM staging of colorectal carcinoma: issues and caveats. Semin Diagn Pathol, 2012, 29(3): 142-153.
21. Berho M, Bejarano PA. Rectal cancer and the pathologist. Minerva Chir, 2018, 73(6): 534-547.
22. Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities. J Pathol Inform, 2018, 9: 38.
23. Nayak J, Goel S. Revised TNM staging for colorectal cancer: did we miss the golden opportunity to do right by the staging? Clin Colorectal Cancer, 2011, 10(3): 207-209.
24. Cavallaro U, Christofori G. Molecular mechanisms of tumor angiogenesis and tumor progression. J Neurooncol, 2000, 50(1-2): 63-70.
25. Wang X, Yu Y, Meng W, et al. Total neoadjuvant treatment (CAPOX plus radiotherapy) for patients with locally advanced rectal cancer with high risk factors: a phase 2 trial. Radiother Oncol, 2018, 129(2): 300-305.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Database research part Ⅵ: staging strategies for colorectal cancer

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