Radionuclide Theranostics in Neuroendocrine Neoplasms: An Update

Di Franco, Martina; Zanoni, Lucia; Fortunati, Emilia; Fanti, Stefano; Ambrosini, Valentina

doi:10.1007/s11912-024-01526-5

Cited by 1 publication

(1 citation statement)

References 111 publications

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“…For example, any information about somatostatin receptor analogs, PRRT, and targeted therapy, which are currently available therapies, is not recorded in the database. Thus, we cannot justify those factors during model development [55]. Other unavailable information includes patient performance, margin status, surgery indication, chemotherapy agent, radiotherapy detail, venous invasion, and volume of the healthcare organization.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Machine Learning Model to Predict the Survival of Patients with Gastrointestinal Neuroendocrine Neoplasms

Liu,

Chen,

Dai

et al. 2024

Neuroendocrinology

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Introduction: Well-calibrated models for personalized prognostication of patients with gastrointestinal neuroendocrine neoplasms (GINENs) are limited. This study aimed to develop and validate a machine-learning model to predict the survival of patients with GINENs. Methods: oblique random survival forest (ORSF) model, Cox proportional hazard risk model, Cox model with least absolute shrinkage and selection operator penalization, CoxBoost, Survival Gradient Boosting Machine, Extreme Gradient Boosting survival regression, DeepHit, DeepSurv, DNNSurv, Logistic-Hazard model, and PC-Hazard model were compared. We further tuned hyperparameters and selected variables for the best-performing ORSF. Then, the final ORSF model was validated. Results: 43444 patients with GINENs were included. The median (interquartile range) survival time was 53 (19-102) months. The ORSF model performed best, in which age, histology, M stage, tumor size, primary tumor site, sex, tumor number, surgery, lymph nodes removed, N stage, race, and grade were ranked as important variables. However, chemotherapy and radiotherapy were not necessary for the ORSF model. The ORSF model had an overall C-index of 0.86 (95% confidence interval, 0.85-0.87). The area under the receiver operation curves at 1-, 3-, 5-, and 10-year were 0.91, 0.89, 0.87, and 0.80, respectively. The decision curve analysis showed superior clinical usefulness of the ORSF model than the American Joint Committee on Cancer Stage. A nomogram and an online tool were given. Conclusion: The machine-learning ORSF model could precisely predict the survival of patients with GINENs, with the ability to identify patients at high risk for death and probably guide clinical practice.

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Section: Discussionmentioning

confidence: 99%