Annals of Surgical Oncology

, Volume 25, Issue 5, pp 1366–1373 | Cite as

Development and Validation of a Prediction Model for Postoperative Peritoneal Metastasis After Curative Resection of Colon Cancer

  • Hiroshi Nagata
  • Soichiro Ishihara
  • Koji Oba
  • Toshiaki Tanaka
  • Keisuke Hata
  • Kazushige Kawai
  • Hiroaki Nozawa
Colorectal Cancer



Detection of peritoneal metastasis remains challenging due to the limited sensitivity of current examination methods. This study aimed to establish a prediction model for estimating the individual risk of postoperative peritoneal metastasis from colon cancer to facilitate early interventions for high-risk patients.


This study investigated 1720 patients with stages 1–3 colon cancer who underwent curative resection at the University of Tokyo Hospital between 1997 and 2015. The data for the patients were retrospectively retrieved from their medical records. The risk score was developed using the elastic net techniques in a derivation cohort (973 patients treated in 1997–2009) and validated in a validation cohort (747 patients treated in 2010–2015).


The factors selected using the elastic net approaches included the T stage, N stage, number of examined lymph nodes, preoperative carcinoembryonic antigen level, large bowel obstruction, and anastomotic leakage. The model had good discrimination (c-index, 0.85) and was well-calibrated after application of the bootstrap resampling method. Discrimination and calibration were favorable in external validation (c-index, 0.83). The model presented a clear stratification of patients’ risk for postoperative peritoneal recurrence, and decision curve analysis showed its net benefit across a wide range of threshold probabilities.


This study established and validated a prediction model that can aid clinicians in optimizing postoperative surveillance and therapeutic strategies according to the individual patient risk of peritoneal recurrence.



This study was supported by the Japan Society for the Promotion of Science (Grant Nos. 16H02672, 16K07143, 16K07161, 17K10620, 17K10621, and 17K10623) and the Japan Agency for Medical Research and Development (Grant No. JP17cm0106502).


There are no conflicts of interest.

Supplementary material

10434_2018_6403_MOESM1_ESM.tif (593 kb)
Supplementary material 1 (TIFF 593 kb). Calibration plots of the prediction model in the derivation data sets. The black line shows the probability calculated by the prediction model, and the blue curve corresponds to 1000 bootstrap-corrected estimates.
10434_2018_6403_MOESM2_ESM.docx (31 kb)
Supplementary material 2 (DOCX 30 kb)


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Copyright information

© Society of Surgical Oncology 2018

Authors and Affiliations

  • Hiroshi Nagata
    • 1
  • Soichiro Ishihara
    • 2
  • Koji Oba
    • 3
    • 4
  • Toshiaki Tanaka
    • 1
  • Keisuke Hata
    • 1
  • Kazushige Kawai
    • 1
  • Hiroaki Nozawa
    • 1
  1. 1.Department of Surgical Oncology, Faculty of Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
  2. 2.Surgery DepartmentSanno Hospital, International University of Health and WelfareTokyoJapan
  3. 3.Department of Biostatistics, School of Public Health, Graduate School of MedicineThe University of TokyoTokyoJapan
  4. 4.Interfaculty Initiative in Information StudiesThe University of TokyoTokyoJapan

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