Dataset Information

Combining pathological risk factors and T, N staging to optimize the assessment for risk stratification and prognostication in low-risk stage III colon cancer.

ABSTRACT:

Background

This study aimed to investigate the combined pathological risk factors (PRFs) to stratify low-risk (pT1-3N1) stage III colon cancer (CC), providing a basis for individualized treatment in the future.

Patients and methods

PRFs for low-risk stage III CC were identified using COX model. Low-risk stage III CC was risk-grouped combining with PRFs, and survival analysis were performed using Kaplan-Meier. The Surveillance, Epidemiology, and End Results (SEER) databases was used for external validation.

Results

Nine hundred sixty-two stage III CC patients were included with 634 (65.9%) as low risk and 328 (34.1%) as high risk. Poor differentiation (OS: P = 0.048; DFS: P = 0.011), perineural invasion (OS: P = 0.003; DFS: P < 0.001) and tumor deposits (OS: P = 0.012; DFS: P = 0.003) were identified as PRFs. The prognosis of low-risk CC combined with 2 PRFs (OS: HR = 3.871, 95%CI, 2.004-7.479, P < 0.001; DFS: HR = 3.479, 95%CI, 2.158-5.610, P < 0.001) or 3 PRFs (OS: HR = 5.915, 95%CI, 1.953-17.420, P = 0.002; DFS: HR = 5.915, 95%CI, 2.623-13.335, P < 0.001) was similar to that of high-risk CC (OS: HR = 3.927, 95%CI, 2.317-6.656, P < 0.001; DFS: HR = 4.132, 95%CI, 2.858-5.974, P < 0.001). In the SEER database, 18,547 CC patients were enrolled with 10,023 (54.0%) as low risk and 8524 (46.0%) as high risk. Low-risk CC combined with 2 PRFs (OS: HR = 1.857, 95%CI, 1.613-2.139, P < 0.001) was similar to that of high-risk CC without PRFs (HR = 1.876, 95%CI, 1.731-2.033, P < 0.001).

Conclusion

Combined PRFs improved the risk stratification of low-risk stage III CC, which could reduce the incidence of undertreatment and guide adjuvant chemotherapy.

SUBMITTER: Xian ZY

PROVIDER: S-EPMC10765648 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Combining pathological risk factors and T, N staging to optimize the assessment for risk stratification and prognostication in low-risk stage III colon cancer.

Xian Zhen-Yu ZY Song Yi-Wen YW Zhang Zong-Jin ZJ Gan Ying-Guo YG Chen Yong-Le YL Hu Tuo T Wen Xiao-Feng XF Mo Tai-Wei TW He Xiao-Wen XW

World journal of surgical oncology 20240104 1

<h4>Background</h4>This study aimed to investigate the combined pathological risk factors (PRFs) to stratify low-risk (pT1-3N1) stage III colon cancer (CC), providing a basis for individualized treatment in the future.<h4>Patients and methods</h4>PRFs for low-risk stage III CC were identified using COX model. Low-risk stage III CC was risk-grouped combining with PRFs, and survival analysis were performed using Kaplan-Meier. The Surveillance, Epidemiology, and End Results (SEER) databases was use ...[more]

PMID: 38178080

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Project description:BackgroundHighly heterogeneity and inconsistency in terms of prognosis are widely identified for early-stage cervical cancer (esCC). Herein, we aim to investigate for an intuitional risk stratification model for better prognostication and decision-making in combination with clinical and pathological variables.MethodsWe enrolled 2071 CC patients with preoperative biopsy-confirmed and clinically diagnosed with FIGO stage IA-IIA who received radical hysterectomy from 2013 to 2018. Patients were randomly assigned to the training set (n = 1450) and internal validation set (n = 621), in a ratio of 7:3. We used recursive partitioning analysis (RPA) to develop a risk stratification model and assessed the ability of discrimination and calibration of the RPA-derived model. The performances of the model were compared with the conventional FIGO 2018 and 9th edition T or N stage classifications.ResultsRPA divided patients into four risk groups with distinct survival: 5-year OS for RPA I to IV were 98%, 95%, 85.5%, and 64.2%, respectively, in training cohort; and 99.5%, 93.2%, 85%, and 68.3% in internal validation cohort (log-rank p < 0.001). Calibration curves confirmed that the RPA-predicted survivals were in good agreement with the actual survivals. The RPA model outperformed the existing staging systems, with highest AUC for OS (training: 0.778 vs. 0.6-0.717; internal validation: 0.772 vs. 0.595-0.704; all p < 0.05), and C-index for OS (training: 0.768 vs. 0.598-0.707; internal validation: 0.741 vs. 0.583-0.676; all p < 0.05). Importantly, there were associations between RPA groups and the efficacy of treatment regimens. No obvious discrepancy was observed among different treatment modalities in RPA I (p = 0.922), whereas significant survival improvements were identified in patients who received adjuvant chemoradiotherapy in RPA II-IV (p value were 0.028, 0.036, and 0.024, respectively).ConclusionWe presented a validated novel clinicopathological risk stratification signature for robust prognostication of esCC, which may be used for streamlining treatment strategies.

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Project description:ObjectiveDespite controversy over its origin and definition, the significance of tumour deposit (TD) has been underestimated in the tumour node metastasis (TNM) staging system for colon cancer, especially in stage III patients. We aimed to further confirm the prognostic value of TD in stage III colon cancer and to establish a more accurate 'coN' staging system combining TD and lymph node metastasis (LNM).MethodsInformation on stage III colon cancer patients with a definite TD status was retrospectively collected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017. The effect of TD on prognosis was estimated using Cox regression analysis. Maximally selected rank statistics were used to select the optimal cut-off value of TD counts. The predictive power of conventional N staging and the new coN staging was evaluated and compared by Akaike's information criterion (AIC), Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves. Clinicopathological data of stage III colon cancer patients in the Xiangya database from 2014 to 2018 were collected to validate the coN staging system.ResultsA total of 39,185 patients with stage III colon cancer were included in our study: 38,446 in the SEER cohort and 739 in the Xiangya cohort. The incidence of TD in stage III colon cancer was approximately 30% (26% in SEER and 30% in the Xiangya database). TD was significantly associated with poorer overall survival (OS) (HR = 1.37, 95% CI 1.31-1.44, p < 0.001 in SEER). The optimal cut-off value of TD counts was 4, and the patients were classified into the TD0 (count = 0), TD1 (count = 1-3) and TD2 (count ≥ 4) groups accordingly. The estimated 5-year OS was significantly different among the three groups (69.4%, 95% CI 68.8%-70.0% in TD0; 60.5%, 95% CI 58.9%-62.2% in TD1 and 42.6%, 95% CI 39.2%-46.4% in TD2, respectively, p < 0.001). The coN system integrating LNM and TD was established, and patients with stage III colon cancer were reclassified into five subgroups (coN1a, coN1b, coN2a, coN2b and coN2c). Compared with conventional N staging, the coN staging Cox model had a smaller AIC (197097.581 vs. 197358.006) and a larger C-index (0.611 vs. 0.601). The AUCs of coN staging at 3, 5 and 7 years were also greater than those of conventional N staging (0.6305, 0.6326, 0.6314 vs. 0.6186, 0.6197, 0.6160). Concomitant with the SEER cohort results, the coN staging Cox model of the Xiangya cohort also had a smaller AIC (2883.856 vs. 2906.741) and a larger C-index (0.669 vs. 0.633). Greater AUCs at 3, 5 and 7 years for coN staging were also observed in the Xiangya cohort (0.6983, 0.6774, 0.6502 vs. 0.6512, 0.6368, 0.6199).ConclusionsNot only the presence but also the number of TDs is associated with poor prognosis in stage III colon cancer. A combined N staging system integrating LNM and TD provides more accurate prognostic prediction than the latest AJCC N staging in stage III colon cancer.

Dataset Information

Combining pathological risk factors and T, N staging to optimize the assessment for risk stratification and prognostication in low-risk stage III colon cancer.

Background

Patients and methods

Results

Conclusion

Publications

Combining pathological risk factors and T, N staging to optimize the assessment for risk stratification and prognostication in low-risk stage III colon cancer.

Similar Datasets

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