Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction

Davide Chicco; Riccardo Haupt; Alberto Garaventa; Paolo Uva; Roberto Luksch; Davide Cangelosi

doi:10.1016/j.ejca.2023.113291

Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction

Eur J Cancer. 2023 Nov:193:113291. doi: 10.1016/j.ejca.2023.113291. Epub 2023 Aug 19.

Authors

Davide Chicco¹, Riccardo Haupt², Alberto Garaventa³, Paolo Uva⁴, Roberto Luksch⁵, Davide Cangelosi⁶

Affiliations

¹ Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy.
² DOPO Clinic, Department of Hematology/Oncology, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
³ Unità di Oncologia, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
⁴ Unità di Bioinformatica Clinica, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
⁵ S.C. Pediatria oncologica, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
⁶ Unità di Bioinformatica Clinica, IRCCS Istituto Giannina Gaslini, Genoa, Italy. Electronic address: davidecangelosi@gaslini.org.

PMID: 37708628
DOI: 10.1016/j.ejca.2023.113291

Abstract

Objective: Seek new candidate prognostic markers for neuroblastoma outcome, relapse or progression.

Materials and methods: In this multicentre and retrospective study, Random Forests coupled with recursive feature elimination techniques were applied to electronic records (55 clinical features) of 3034 neuroblastoma patients. To assess model performance and feature importance, dataset was split into a training set (80%) and a test set (20%).

Results: In the test set, the mean Matthews correlation coefficient for the Random Forests models was greater than 0.46. Feature importance analysis revealed that, together with maximum response to first-line treatment (D_MAX_RESP), time to maximum response to first-line treatment (TIME_MAX_RESP.days) is a relevant predictor of both patients' outcome and relapse\progression. We showed the prognostic value of the max response to first-line treatment in clinically relevant subsets of high-, intermediate-, and low-risk patients for both overall and relapse-free survival (Log-rank p-value<0.0001). In high-risk patients older than 18 months and stage 4 tumour achieving a complete response or very good partial response, patients who exhibited a D_MAX_RESP greater than 9 months showed a better prognosis with respect to patients achieving D_MAX_RESP earlier than 9 months (overall survival): hazard ratio 3.3 95% confidence interval 1.8-5.9, Log-rank p-value p < 0.0001; relapse-free survival: 3.2 95%CI 1.8-5.6, Log-rank p-value p < 0.0001).

Conclusion: Our findings evidence the emerging role of the TIME_MAX_RESP.days in addition to the D_MAX_RESP as relevant predictors of outcome and relapse\progression in neuroblastoma with potential clinical impact on the management and treatment of patients.

Keywords: Artificial intelligence; Feature importance ranking; Maximum response to first-line treatment; Neuroblastoma; Random forests; Time to maximum response to first-line treatment.