Andrea Campagner1, Pedro Berjano2, Claudio Lamartina2, Francesco Langella2, Giovanni Lombardi3, Federico Cabitza4. 1. IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy. Electronic address: a.campagner@campus.unimib.it. 2. IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy. 3. Laboratory of Experimental Biochemistry & Molecular Biology, IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy; Department of Athletics, Strength and Conditioning, Poznań University of Physical Education, Królowej Jadwigi 27/39, 61-871, Poznań, Poland. 4. Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy. Electronic address: federico.cabitza@unimib.it.
Abstract
BACKGROUND: The interest in Minimally Invasive Surgery (MIS) techniques has greatly increased in the recent years due to their significant advantages, both in terms of outcome improvement and cost reduction. Also in spine surgery, MIS is now applicable to several conditions and, above all, in low back pain (LBP) treatment. However, reliable and objective measures of invasiveness, necessary to compare different procedures, are still lacking. METHODS: In this article we study the application of Machine Learning (ML) techniques to define an invasiveness score for LBP procedures based on biological markers and inflammatory profiles. In so doing, we can assess the invasiveness of surgical procedures. We also propose a predictive model for treatment planning based on the evaluation of invasiveness of surgical alternatives for specific patients, using their pre-surgery biomarkers. The data used in study was characterized by low sample size and high-dimensionality, thus we adopted a combination of feature selection, careful selection of ML models and conservative model selection choices in order to address these concerns. We also performed an external validation based on a statistically significantly different datasets in order to confirm the relevance of the findings. RESULTS: We report the results of an experimental study on real-world data, for which we obtained promising results for both considered applications: we report an AUC of 0.87 for the task of invasiveness score definition, and an AUC of 0.76 for the invasiveness prediction task. The results obtained on the external validation were in agreement with the obtained results. Further, in both cases the performances were considered as excellent by the involved clinicians and the selected predictive features were biologically relevant and associated with invasiveness and biological impact in the relevant literature. CONCLUSION: Our results show that ML techniques could be effectively employed not only for diagnosis or prognosis, but also for treatment planning, a task of fundamental importance toward personalized and value-based healthcare. These results also show that ML approaches could be effectively used even in scenarios (e.g. pilot studies) where only small samples are available.
BACKGROUND: The interest in Minimally Invasive Surgery (MIS) techniques has greatly increased in the recent years due to their significant advantages, both in terms of outcome improvement and cost reduction. Also in spine surgery, MIS is now applicable to several conditions and, above all, in low back pain (LBP) treatment. However, reliable and objective measures of invasiveness, necessary to compare different procedures, are still lacking. METHODS: In this article we study the application of Machine Learning (ML) techniques to define an invasiveness score for LBP procedures based on biological markers and inflammatory profiles. In so doing, we can assess the invasiveness of surgical procedures. We also propose a predictive model for treatment planning based on the evaluation of invasiveness of surgical alternatives for specific patients, using their pre-surgery biomarkers. The data used in study was characterized by low sample size and high-dimensionality, thus we adopted a combination of feature selection, careful selection of ML models and conservative model selection choices in order to address these concerns. We also performed an external validation based on a statistically significantly different datasets in order to confirm the relevance of the findings. RESULTS: We report the results of an experimental study on real-world data, for which we obtained promising results for both considered applications: we report an AUC of 0.87 for the task of invasiveness score definition, and an AUC of 0.76 for the invasiveness prediction task. The results obtained on the external validation were in agreement with the obtained results. Further, in both cases the performances were considered as excellent by the involved clinicians and the selected predictive features were biologically relevant and associated with invasiveness and biological impact in the relevant literature. CONCLUSION: Our results show that ML techniques could be effectively employed not only for diagnosis or prognosis, but also for treatment planning, a task of fundamental importance toward personalized and value-based healthcare. These results also show that ML approaches could be effectively used even in scenarios (e.g. pilot studies) where only small samples are available.
Authors: Francesco Langella; Paolo Barletta; Alice Baroncini; Matteo Agarossi; Laura Scaramuzzo; Andrea Luca; Roberto Bassani; Giuseppe M Peretti; Claudio Lamartina; Jorge H Villafañe; Pedro Berjano Journal: Eur Spine J Date: 2021-05-10 Impact factor: 3.134
Authors: Alice Baroncini; Filippo Migliorini; Francesco Langella; Paolo Barletta; Per Trobisch; Riccardo Cecchinato; Marco Damilano; Emanuele Quarto; Claudio Lamartina; Pedro Berjano Journal: J Clin Med Date: 2021-12-28 Impact factor: 4.241