ObjectiveTo analyze the neoadjuvant therapy of colorectal cancer in this center in the background of real world data by studying Database from Colorectal Cancer (DACCA) in West China Hospital of Sichuan University.MethodsData was selected from DACCA who was updated on August 15, 2019. After deleting duplicate value, patients whose tumor location and tumor pathologic characteristic showed colon or rectum, as well as adenocarcinoma, mucinous adenocarcinoma, and signet ring cell carcinoma were enrolled.ResultsThere were 2 783, 2 789, 2 790, 2 811, 4 148,3 824, 4 191, 3 676, 4 090, and 499 valid data of T, N, and M stages, clinical stages, tumor site, distance from tumor to anal dentate line, tumor pathologic characteristics, degree of tumor differentiation, neoadjuvant therapy, and compliance, respectively. There were 1 839 lines that " nature of the tumor pathology” was not empty and neoadjuvant scheme for the pure chemotherapy, radiotherapy alone or radiation, and chemotherapy, including 50 lines of signet ring cell carcinoma (2.7%), 299 lines of mucous adenocarcinoma (16.3%), 1 490 lines of adenocarcinoma (81.0%), various kinds of pathology in selection of neoadjuvant therapy difference was statistically significant (χ2=9.138, P=0.041). Except for the data lines with null value in the column of " operation date”, there were 2 234 (82.1%) and 486 (17.9%) effective data lines of " recommended” and " not recommended” for the use of neoadjuvant therapy, respectively. In the years with a large amount of data, among the patients who completed neoadjuvant therapy, the proportion of patients meeting the recommended indications was 27.4%–67.6%, with an average of 47.4%. Patients who did not meet the recommended indications but were recommended (off-label use) accounted for 7.3%–70.0%, with an average of 39.8%. According to regression analysis, the proportion in line with the recommendation (\begin{document}$\hat y $\end{document}=–0.032 5x+66.003 2, P=0.020) varies with the year, and the overall trend shows a gradual decline. The proportion of the use of super indications (\begin{document}$\hat y $\end{document}=–0.054 5x+110.174 6, P=0.002) changed with the year, and the overall trend showed a decline. A total of 1 161 valid data with non-null values of " eoadjuvant therapy regimen” and " recommended or not recommended” showed statistically significant difference in the use rate of neoadjuvant therapy among patients with different recommendation groups (χ2=9.244, P=0.002). " Patient compliance” was shown as " active cooperation” and " passive acceptance”, and " neoadjuvant therapy” was shown as " radiotherapy alone”" chemotherapy alone”, and " chemoradiotherapy” were 470 lines. There was no statistically significant difference in neoadjuvant therapy between patients receiving active and passive treatment (χ2=0.537, P=0.841). The effective data of clinical remission degree meeting the research conditions were 388 lines, including 121 lines of complete response (31.2%), 180 lines of partial response (46.4%), 79 lines of stable disease (20.4%), and 8 lines of progressive disease (2.1%). There was no statistically significant difference in clinical response degree among patients with different neoadjuvant therapy (H=0.435, P=0.783). There were 346 lines with effective data of pathologic tumor regression grade (TRG) meeting the study conditions, including 47 lines with TRG0 (13.6%), 39 lines with TRG1 (11.3%), 180 lines with TRG2 (52.0%), and 80 lines with TRG3 (23.1%). There was no statistical difference in the degree of TRG among patients with different neoadjuvant therapy (H=1.816, P=0.518).ConclusionsThe real world study reflects that in the western regional medical center, the demand for neoadjuvant therapy among the patients with colorectal cancer covered is huge. Although the implementation of neoadjuvant therapy is greatly influenced by the doctor’s recommendation behavior, the selection and recommendation of neoadjuvant therapy according to some specific clinical application guidelines are not fully met. The impact of more behavioral factors requires further in-depth analysis and research.
ObjectiveTo analyze the staging methods of colorectal cancer data in the current version of the Database from Colorectal Cancer (DACCA).MethodsThe 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.ResultsThe 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.ConclusionThrough 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.
Objective To analyze the impact of body mass index (BMI) on surgical difficulty and surgical reaction of patients with colorectal cancer served by West China Hospital, based on the current version of Database from Colorectal Cancer (DACCA). MethodsThe data of DACCA were updated on September 22, 2021. All data items included BMI, operative duration, anatomical difficulty, pelvic stenosis, abdominal obesity, adhesion in surgical area, abnormal mesenteric status, tissue or organ hypertrophy, intestinal quality in surgical area, surgery reaction, and perioperative complications of colorectal cancer module including temperature, flatus, pain, and mental status. The patients were divided into lean (BMI <18.5 kg/m2), normal (BMI 18.5–23.9 kg/m2), overweight (BMI 24.0–27.9 kg/m2), and obesity (BMI ≥28.0 kg/m2) by Chinese four classification method. ResultsAfter scanning, 6 311 data rows were analyzed. ① The effect of BMI on surgical difficulty: BMI was weakly positively correlated with operative duration (rs=0.096, P<0.001). The overweight and obesity patients were more likely to have anatomical abnormalities (rs=0.385, P<0.001 ), pelvic stenosis (rs=0.386, P<0.001), abdominal obesity (rs=0.567, P<0.001), and adhesion in surgical area (rs=0.043, P=0.004). There was difference in tissue or organ hypertrophy among patients with different BMI (χ2=39.988, P<0.001). It was also found that the lean patients were prone to develop ‘very heavy adhesions’ when adhesions occurred in surgical area, and to ‘mesangial contracture, short, fixed’ of abnormal mesenteric status, while the obesity patients were prone to ‘mesangial thickening’ of abnormal mesenteric status. There was no difference in intestinal quality among patients with different BMI (P>0.05). ② The effect of BMI on surgical reaction: BMI was weakly positively correlated with the overall assessment of surgical reaction (rs=0.049, P=0.001), and obesity patients were more likely to have severe surgical reaction. BMI was weakly negatively correlated with pain (rs=–0.058, P<0.001）and the lean patients were more likely to have pain that needed drug control. However, there were no differences in temperature, flatus, and mental status among patients with different BMI (P>0.05). ConclusionsHigh BMI of patients will affect several indicators including operative duration, anatomical difficulty, pelvic stenosis, abdominal obesity, adhesion in surgical area, abnormal mesenteric status, tissue or organ hypertrophy, resulting in increased difficulty of surgery. Although high BMI might affect overall response state after surgery, it will not affect temperature, flatus, and mental status, reflecting a relatively weak impact on surgical reaction.
ObjectiveTo evaluate the efficacy of multicycle neoadjuvant chemotherapy combined with apatinib in the treatment of advanced rectal cancer through the Database from Colorectal Cancer (DACCA).MethodsA total of 173 patients with advanced rectal cancer who underwent surgery after 2–4 cycles of neoadjuvant chemotherapy combined with apatinib were selected from the DACCA (Version January 20, 2019). The patients were grouped by treated cycle and clinical TNM (cTNM) stage, and the overall variation of clinical and pathological indicators before and after treatment were compared.ResultsAmong 173 cases, 63 cases (36.42%), 45 cases (26.01%), and 65 cases (37.57%) were respectively in the 2, 3, and 4-cycle group; 54 cases (31.21%), 91 cases (52.60%), and 28 cases (16.18%) in stage Ⅱ, Ⅲ, and Ⅳ-group. Clinical response degree: complete pathologic response (cPR) was observed in 23 cases (13.29%), and the rate of conversion resection was 100% (173/173). Clinical response grade: complete response (CR) in 58 cases (33.53%), partial response (PR) in 93 cases (53.76%), stable disease (SD) in 21 cases (12.14%), progressive disease (PD) in 1 cases (0.58%). Tumor regression grade: TRG0 in 21 cases (12.57%), TRG1 in 22 cases (13.17%), TRG2 in 84 cases (50.30%), TRG3 in 40 cases (23.95%). There was a statistical difference in CEA before and after the treatment (P<0.001). All cases underwent radical resection, and the successful rate of transformed resection was 100%. There was significant difference on the clinical response grade among the cases of different treatment cycle (H=18.513, P<0.001), and the longer treatment cycle was correlated with better clinical response (G=–0.474, P<0.001). In addition, there was significant difference on the cPR rate among the cases of different cTNM stage (χ2=6.450, P=0.040).ConclusionsMulticycle neoadjuvant chemotherapy combined with apatinib in treating patients with advanced rectal cancer is efficient. More treatment cycles lead to better efficacy. The lower cTNM stage maybe means more chance of achieving cPR and a satisfactory rate of conversion resection.
Objective To analyze the relationship between age and prognosis of colorectal patients in the database from colorectal cancer (DACCA). Methods The DACCA version selected for this data analysis was updated on January 5, 2022. The data items analyzed included age, sex, tumor site, tumor pathological nature, obstruction, pathological TNM (pTNM) stage, positive lymph node ratio, survival status and survival time. According to China’s age segmentation standard, the included data were grouped into younger group (<35 years old), middle-aged group (35–59 years old) and elderly group (>59 years old). Overall survival (OS) and disease specific survival (DFS) were analyzed in three age group, and OS and DSS in three age group were analyzed in pTNM stage stratification. Results Three thousand six hundred and twenty-five rows of data were obtained from DACCA database according to the screening conditions. The survival analysis results of different age groups show that: The middle-aged group had better OS compared with the elderly group at 1-year (97.4% vs. 96.0%, P=0.037), 3-year (90.9% vs. 88.0%, P=0.030) and 5-year (81.7% vs. 75.7%, P=0.002). Also, the middle-age group had better 5-year DSS (82.2% vs. 77.7%, P=0.020). There was no statistical difference in survival between the younger group and the elderly group (P>0.05). The survival analysis results of different age groups in each pTNM stage show that: ① The middle-aged group had better medium-term and long-term OS than the elderly group. In the pTNM Ⅰ stage, the 3- and 5-year OS in the middle-aged group were better than those in the elderly group (100% vs. 93.4%, P=0.004; 100% vs. 91.4%, P=0.005). In the pTNM Ⅱ stage, the 5- and 10-year OS in the middle-aged group were better than those in the elderly group (96.5% vs. 91.3%, P=0.018; 88.2% vs. 54.3%, P<0.001). In pTNM Ⅲ stage, 10-year OS in the middle-aged group was better than that in the elderly group (36.5% vs. 36.0%, P<0.001). In pTNM Ⅳ stage, the 5- and 10- year of OS in the middle-aged group were better than those in the elderly group (67.7% vs. 58.4%, P=0.016; 19.1% vs. 7.2%, P=0.049). ② The middle-aged group had better medium-term and long-term DSS than the elderly group. In the pTNM Ⅰ stage, the 3- and 5- year DSS in the middle-aged group wrer better compared to the elderly group (100% vs. 96.9%, P=0.047; 100% vs. 94.9%, P=0.049). In the pTNM Ⅱ stage, the 10-year DSS in the middle-aged group outperformed that in the elderly group (88.2% vs. 61.9%, P=0.002). In the pTNM Ⅳ stage, the 5- and 10-year DSS in the middle-aged group were better than the elderly group (68.3% vs. 59.1%, P=0.020; 20.9% vs. 7.7%, P=0.040). ③ Except pTNM I stage, there was no significant difference in survival of other pTNM stages between young group and old group (P>0.05). In the pTNM Ⅰ stage, 3- and 5- year OS were better in the younger group compared with the elderly group (100% vs. 93.4%, P=0.004; 100% vs. 91.4%, P=0.005), and better 3- and 5- year DSS in the younger group (100% vs. 96.9%, P=0.047; 100% vs. 94.9%, P=0.049). Conclusions The age of colorectal cancer patients may have an impact on long-term survival. Middle-aged patients have better prognosis compared with elderly patients, and the younger group patients have better prognosis in pTNM stage Ⅰ only.