Abstract
Original language | English |
---|---|
Number of pages | 21 |
Journal | Cancers |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- Artificial intelligence
- Decision trees
- Extent of resection
- Grade II glioma
- Molecular classification
- MRI data
- Prognosis
- Ki 67 antigen
- adult
- aged
- algorithm
- Article
- artificial intelligence
- cancer grading
- cancer prognosis
- cancer surgery
- cancer survival
- clinical feature
- clinical outcome
- cohort analysis
- controlled study
- decision tree
- female
- glioma
- histopathology
- human
- human tissue
- major clinical study
- male
- nuclear magnetic resonance imaging
- overall survival
- predictive value
- progression free survival
- retrospective study
- survival prediction
- tumor volume
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Application of an artificial intelligence algorithm to prognostically stratify grade II gliomas. / Cesselli, D.; Ius, T.; Isola, M.; Del Ben, F.; Da Col, G.; Bulfoni, M.; Turetta, M.; Pegolo, E.; Marzinotto, S.; Scott, C.A.; Mariuzzi, L.; Di Loreto, C.; Beltrami, A.P.; Skrap, M.
In: Cancers, Vol. 12, No. 1, 2020.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Application of an artificial intelligence algorithm to prognostically stratify grade II gliomas
AU - Cesselli, D.
AU - Ius, T.
AU - Isola, M.
AU - Del Ben, F.
AU - Da Col, G.
AU - Bulfoni, M.
AU - Turetta, M.
AU - Pegolo, E.
AU - Marzinotto, S.
AU - Scott, C.A.
AU - Mariuzzi, L.
AU - Di Loreto, C.
AU - Beltrami, A.P.
AU - Skrap, M.
N1 - Cited By :3 Export Date: 16 February 2021 Correspondence Address: Cesselli, D.; Department of Medicine, Italy; email: daniela.cesselli@uniud.it Funding details: Regione Autonoma Friuli Venezia Giulia Funding text 1: Funding: This work has been supported by: the Interreg V-A Italy-Slovenia programme 2014-2020 with the project: “TRANS-GLIOMA - Nuove terapie per il glioblastoma tramite una piattaforma di ricerca transfrontaliera traslazionale”; Regione Friuli Venezia Giulia, within the framework of “legge regionale 17/2004: Contributi per la ricerca clinica, traslazionale, di base, epidemiologica e organizzativa”, with the projects “BioMec – Applicazione delle tecnologie biomeccaniche a integrazione delle metodiche tradizionali nel contesto ospedaliero” and “Glioblastoma - Infiltrazione nei gliomi: nuovo target terapeutico“. References: Chammas, M., Saadeh, F., Maaliki, M., Assi, H., Therapeutic Interventions in Adult Low-Grade Gliomas (2019) J. Clin. 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PY - 2020
Y1 - 2020
N2 - (1) Background: Recently, it has been shown that the extent of resection (EOR) and molecular classification of low-grade gliomas (LGGs) are endowed with prognostic significance. However, a prognostic stratification of patients able to give specific weight to the single parameters able to predict prognosis is still missing. Here, we adopt classic statistics and an artificial intelligence algorithm to define a multiparametric prognostic stratification of grade II glioma patients. (2) Methods: 241 adults who underwent surgery for a supratentorial LGG were included. Clinical, neuroradiological, surgical, histopathological and molecular data were assessed for their ability to predict overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS). Finally, a decision-tree algorithm was employed to stratify patients. (3) Results: Classic statistics confirmed EOR, pre-operative-and post-operative tumor volumes, Ki67, and the molecular classification as independent predictors of OS, PFS, and MPFS. The decision tree approach provided an algorithm capable of identifying prognostic factors and defining both the cut-off levels and the hierarchy to be used in order to delineate specific prognostic classes with high positive predictive value. Key results were the superior role of EOR on that of molecular class, the importance of second surgery, and the role of different prognostic factors within the three molecular classes. (4) Conclusions: This study proposes a stratification of LGG patients based on the different combinations of clinical, molecular, and imaging data, adopting a supervised non-parametric learning method. If validated in independent case studies, the clinical utility of this innovative stratification approach might be proved. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
AB - (1) Background: Recently, it has been shown that the extent of resection (EOR) and molecular classification of low-grade gliomas (LGGs) are endowed with prognostic significance. However, a prognostic stratification of patients able to give specific weight to the single parameters able to predict prognosis is still missing. Here, we adopt classic statistics and an artificial intelligence algorithm to define a multiparametric prognostic stratification of grade II glioma patients. (2) Methods: 241 adults who underwent surgery for a supratentorial LGG were included. Clinical, neuroradiological, surgical, histopathological and molecular data were assessed for their ability to predict overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS). Finally, a decision-tree algorithm was employed to stratify patients. (3) Results: Classic statistics confirmed EOR, pre-operative-and post-operative tumor volumes, Ki67, and the molecular classification as independent predictors of OS, PFS, and MPFS. The decision tree approach provided an algorithm capable of identifying prognostic factors and defining both the cut-off levels and the hierarchy to be used in order to delineate specific prognostic classes with high positive predictive value. Key results were the superior role of EOR on that of molecular class, the importance of second surgery, and the role of different prognostic factors within the three molecular classes. (4) Conclusions: This study proposes a stratification of LGG patients based on the different combinations of clinical, molecular, and imaging data, adopting a supervised non-parametric learning method. If validated in independent case studies, the clinical utility of this innovative stratification approach might be proved. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
KW - Artificial intelligence
KW - Decision trees
KW - Extent of resection
KW - Grade II glioma
KW - Molecular classification
KW - MRI data
KW - Prognosis
KW - Ki 67 antigen
KW - adult
KW - aged
KW - algorithm
KW - Article
KW - artificial intelligence
KW - cancer grading
KW - cancer prognosis
KW - cancer surgery
KW - cancer survival
KW - clinical feature
KW - clinical outcome
KW - cohort analysis
KW - controlled study
KW - decision tree
KW - female
KW - glioma
KW - histopathology
KW - human
KW - human tissue
KW - major clinical study
KW - male
KW - nuclear magnetic resonance imaging
KW - overall survival
KW - predictive value
KW - progression free survival
KW - retrospective study
KW - survival prediction
KW - tumor volume
U2 - 10.3390/cancers12010050
DO - 10.3390/cancers12010050
M3 - Article
VL - 12
JO - Cancers
JF - Cancers
SN - 2072-6694
IS - 1
ER -