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Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.

ABSTRACT: Inconsistent therapeutic efficacy of mesenchymal stem cells (MSCs) in regenerative medicine has been documented in many clinical trials. Precise prediction on the therapeutic outcome of a MSC therapy based on the patient's conditions would provide valuable references for clinicians to decide the treatment strategies. In this article, we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studies. The unique features of our neural network model in handling missing data and calculating prediction uncertainty enabled precise prediction of post-treatment cartilage repair scores with coefficient of determination of 0.637 ± 0.005. From this model, we identified defect area percentage, defect depth percentage, implantation cell number, body weight, tissue source, and the type of cartilage damage as critical properties that significant impact cartilage repair. A dosage of 17 - 25 million MSCs was found to achieve optimal cartilage repair. Further, critical thresholds at 6% and 64% of cartilage damage in area, and 22% and 56% in depth were predicted to significantly compromise on the efficacy of MSC therapy. This study, for the first time, demonstrated machine learning of patient-specific cartilage repair post MSC therapy. This approach can be applied to identify and investigate more critical properties involved in MSC-induced cartilage repair, and adapted for other clinical indications.

SUBMITTER: Liu YYF

PROVIDER: S-EPMC7571701 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.

Liu Yu Yang Fredrik YYF Lu Yin Y Oh Steve S Conduit Gareth J GJ

PLoS computational biology 20201007 10

Inconsistent therapeutic efficacy of mesenchymal stem cells (MSCs) in regenerative medicine has been documented in many clinical trials. Precise prediction on the therapeutic outcome of a MSC therapy based on the patient's conditions would provide valuable references for clinicians to decide the treatment strategies. In this article, we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studie ...[more]

PMID: 33027251

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Project description:BackgroundThe management of articular cartilage defects presents many clinical challenges due to its avascular, aneural and alymphatic nature. Bone marrow stimulation techniques, such as microfracture, are the most frequently used method in clinical practice however the resulting mixed fibrocartilage tissue which is inferior to native hyaline cartilage. Other methods have shown promise but are far from perfect. There is an unmet need and growing interest in regenerative medicine and tissue engineering to improve the outcome for patients requiring cartilage repair. Many published reviews on cartilage repair only list human clinical trials, underestimating the wealth of basic sciences and animal studies that are precursors to future research. We therefore set out to perform a systematic review of the literature to assess the translation of stem cell therapy to explore what research had been carried out at each of the stages of translation from bench-top (in vitro), animal (pre-clinical) and human studies (clinical) and assemble an evidence-based cascade for the responsible introduction of stem cell therapy for cartilage defects. This review was conducted in accordance to PRISMA guidelines using CINHAL, MEDLINE, EMBASE, Scopus and Web of Knowledge databases from 1st January 1900 to 30th June 2015. In total, there were 2880 studies identified of which 252 studies were included for analysis (100 articles for in vitro studies, 111 studies for animal studies; and 31 studies for human studies). There was a huge variance in cell source in pre-clinical studies both of terms of animal used, location of harvest (fat, marrow, blood or synovium) and allogeneicity. The use of scaffolds, growth factors, number of cell passages and number of cells used was hugely heterogeneous.Short conclusionsThis review offers a comprehensive assessment of the evidence behind the translation of basic science to the clinical practice of cartilage repair. It has revealed a lack of connectivity between the in vitro, pre-clinical and human data and a patchwork quilt of synergistic evidence. Drivers for progress in this space are largely driven by patient demand, surgeon inquisition and a regulatory framework that is learning at the same pace as new developments take place.

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Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.

Publications

Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.

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