A large population-based and validated study on the follow-up management and supportive strategy of locally advanced rectal cancer patients.

Support Care Cancer

Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.

Published: September 2024

AI Article Synopsis

  • The study looked at how to predict if patients with locally advanced rectal cancer will get liver or lung cancer spreading (metastasis).
  • Researchers used different methods to find factors that could indicate this risk and analyzed patient data to check their findings.
  • They found that certain factors, like cancer stage and CEA levels, help predict who is more likely to have liver or lung metastasis, suggesting that patients at higher risk need closer monitoring for early detection.

Article Abstract

Objective: Our objective was to evaluate the predictive factors and metastatic time for liver and lung metastasis in locally advanced rectal cancer (RC) patients.

Methods: Univariate and multivariate analysis were performed to identify risk factors and prognostic factors for liver metastasis and lung metastasis in RC. Survival probabilities were calculated using the Kaplan-Meier model and compared using the log-rank test between groups. The probability of time-to-event occurrence was calculated using the random survival forest model. Finally, the SEER database was used to verify our findings.

Results: Our results indicated that pathological T stage and pathological N stage were independent predictive factors for liver metastasis. Furthermore, CEA level, pathological T stage, and tumor deposit were independent predictive factors for lung metastasis. Based on the results of a multivariate Cox analysis, we categorized patients with liver and lung metastasis into three groups based on their scores. The results revealed that patients with higher scores had a higher probability of experiencing metastasis. For liver metastasis, Groups 1, 2, and 3 all exhibited higher occurrence rates within the first 24 months. However, for lung metastasis, Group 4 showed the highest occurrence rate at the 12th month, while Groups 5 and 6 exhibited the highest occurrence rates at the 15th month.

Conclusions: In summary, we developed predictive models to determine the likelihood of liver and lung metastasis in RC patients. It is crucial to implement a more intensive surveillance program for patients with unfavorable risk profiles in order to facilitate early detection of metastasis.

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Source
http://dx.doi.org/10.1007/s00520-024-08860-1DOI Listing

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