BMC Cancer (Oct 2024)

Nomograms predicting benefit after immunotherapy in oral bifidobacteria supplementation ICC patients: a retrospective study

  • Sihui Zhu,
  • Yuncheng Jin,
  • Juan Zhang,
  • Minzheng Zhou,
  • Baorui Liu,
  • Xiufeng Liu,
  • Jie Shen,
  • Chao Chen

DOI
https://doi.org/10.1186/s12885-024-12982-4
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 14

Abstract

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Abstract Purpose The objective of this study was to develop nomograms for predicting outcomes following immunotherapy in patients diagnosed with intrahepatic cholangiocarcinoma (ICC). Patients and methods A retrospective analysis was conducted on data from 75 ICC patients who received immunotherapy at Jinling Hospital and Drum Hospital. The discriminative power, accuracy, and clinical applicability of the nomograms were assessed using the concordance index (C-index), calibration curve, and decision curve analysis (DCA). The predictive performance of the nomograms for overall survival (OS) and progression-free survival (PFS) was evaluated using the area under the receiver operating characteristic (ROC) curve. Kaplan-Meier curves were also generated for validation purposes. Results Multivariable analysis identified independent prognostic factors for OS, including CA19-9 levels, portal vein tumor thrombus (PVTT) grade, bifidobacteria administration, and surgery. The C-index of the nomogram for OS prediction was 0.722 (95% confidence interval [CI]: 0.661–0.783). Independent prognostic factors for PFS included CA19-9 levels, albumin, and bilirubin, with a C-index of 0.678 (95% CI: 0.612–0.743) for the nomogram predicting PFS. Calibration curves demonstrated strong concordance between predicted and observed outcomes, while DCA and Kaplan-Meier curves further supported the clinical utility of the nomogram. Conclusion The nomogram developed in this study demonstrated favorable performance in predicting the prognosis of ICC patients undergoing immunotherapy. Additionally, our findings, for the first time, identified probiotics as a potential prognostic marker for immunotherapy. This prognostic model has the potential to enhance patient selection for immunotherapy and improve clinical decision-making.

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