Prediction of distant metastasis and specific survival prediction of small intestine cancer patients with metastasis: A population-based study

Abstract

Background: Small intestine cancer (SIC) is difficult to diagnose early and presents a poor prognosis due to distant metastasis. This study aimed to develop nomograms for diagnosing and assessing the prognosis of SIC with distant metastasis.

Methods: Patients diagnosed with SIC between 2010 and 2015 were included from the Surveillance, Epidemiology and End Results database. Univariate and multifactor analysis determined independent risk factors for distant metastasis and prognostic factors for overall and cancer-specific survival. We then constructed the corresponding three nomograms and assessed the diagnostic accuracy of the nomograms by net reclassification improvement, receiver operating characteristic curves and calibration curves, assessed the clinical utility by decision curve analysis.

Results: The cohort consisted of 6697 patients, of whom 1299 had distant metastasis at diagnosis. Tstage, Nstage, age, tumor size, grade, and histological type were independent risk factors for distant metastasis. Age, histological type, T stage, N stage, grade, tumor size, whether receiving surgery, number of lymph nodes removed, and the presence of bone or lung metastases were predictors of both overall survival and cancer-specific survival. The nomograms showed excellent accuracy in predicting distant metastasis and prognosis.

Conclusion: Nomograms were developed and validated for SIC patients with distant metastasis, aiding physicians in making rational and personalized clinical decisions.

Keywords: cancer-specific survival; distant metastases; nomogram; overall survival; small intestine cancer.

FIGURE 1

Construction and validation of diagnostic nomogram. Nomogram for DM risk prediction in SIC patients (A). Training set ROC curves (B), calibration curves (D) and DCA (F). Validation set ROC curves (C), calibration curves (E) and DCA (G).

FIGURE 2

In the training set (A) and validation set (B), nomogram was compared with AUC for the various independent factors (N stage, CS extension, tumor size, and histological type).

FIGURE 3

Forest plot of OS in DM patients and p‐value of each factor.

FIGURE 4

Forest plot of CSS rate in DM patients and p‐value of each factor.

FIGURE 5

Prognostic nomogram predicting OS at 3, 5 and 7 years in SIC patients.

FIGURE 6

Prognostic nomogram predicting CSS at 3, 5 and 7 years in SIC patients.

FIGURE 7

Calibration curves for 3‐, 5‐, and 7‐year OS prediction nomogram in the training set (A, B, and C) and validation set (D, E, and F).

FIGURE 8

Calibration curves for 3‐, 5‐, and 7‐year CSS prediction nomogram in the training set (A, B, and C) and validation set (D, E, and F).

FIGURE 9

Decision Curve Analysis (DCA) curves for 3‐(A), 5‐(B), and 7‐year(C) OS prediction nomogram in the training and validation sets.

FIGURE 10

Decision Curve Analysis (DCA) curves for 3‐(A), 5‐(B), and 7‐year(C) CSS prediction nomogram in the training and validation sets.

FIGURE 11

Time‐dependent ROC curve analysis of the OS nomogram for the 3‐, 5‐, and 7‐year in the training set (A) and the validation set (B). The Kaplan–Meier survival curves of the patients in the training set (C) and in the validation set (D).

FIGURE 12

Time‐dependent ROC curve analysis of the CSS nomogram for the 3‐, 5‐, and 7‐year in the training set (A) and the validation set (B). The Kaplan–Meier survival curves of the patients in the training set (C) and in the validation set (D).