Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

Humans Machine Learning Magnetic Resonance Imaging Nerve Sheath Neoplasms / diagnostic imaging Neurofibrosarcoma Retrospective Studies
Machine learning Magnetic resonance imaging Malignant peripheral nerve sheath tumor Peripheral nerve sheath tumor Radiomics Sensitivity Specificity

Journal

Neurosurgery
ISSN: 1524-4040
Titre abrégé: Neurosurgery
Pays: United States
ID NLM: 7802914

Informations de publication

Date de publication:
16 08 2021
Historique:
received: 29 09 2020
accepted: 27 04 2021
pubmed: 17 6 2021
medline: 26 11 2021
entrez: 16 6 2021
Statut: ppublish

Résumé

Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001). Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Sections du résumé

BACKGROUND
Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications.
OBJECTIVE
To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs.
METHODS
We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers.
RESULTS
A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001).
CONCLUSION
Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Identifiants

DOI: 10.1093/neuros/nyab212 PMID: 34131749 PMC: PMC8364819
pubmed: 34131749
pii: 6299935
doi: 10.1093/neuros/nyab212
pmc: PMC8364819
doi:

Types de publication

Journal Article Multicenter Study Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

509-517

Subventions

Organisme : NCI NIH HHS
ID : T32 CA009695
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

© Congress of Neurological Surgeons 2021.

Auteurs

Michael Zhang (M)

Department of Neurosurgery, Stanford University, Stanford, California, USA.
Department of Radiology, Stanford University, Stanford, California, USA.

Elizabeth Tong (E)

Department of Radiology, Stanford University, Stanford, California, USA.

Forrest Hamrick (F)

Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA.

Edward H Lee (EH)

Department of Radiology, Stanford University, Stanford, California, USA.

Lydia T Tam (LT)

Stanford School of Medicine, Stanford University, Stanford, California, USA.

Courtney Pendleton (C)

Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA.

Brandon W Smith (BW)

Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA.

Nicholas F Hug (NF)

Stanford School of Medicine, Stanford University, Stanford, California, USA.

Sandip Biswal (S)

Department of Radiology, Stanford University, Stanford, California, USA.

Jayne Seekins (J)

Department of Radiology, Stanford University, Stanford, California, USA.

Sarah A Mattonen (SA)

Department of Medical Biophysics, Western University, London, Canada.

Sandy Napel (S)

Department of Radiology, Stanford University, Stanford, California, USA.

Cynthia J Campen (CJ)

Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA.

Robert J Spinner (RJ)

Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA.

Kristen W Yeom (KW)

Department of Radiology, Stanford University, Stanford, California, USA.

Thomas J Wilson (TJ)

Department of Neurosurgery, Stanford University, Stanford, California, USA.

Mark A Mahan (MA)

Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA.
ORCID: 0000-0002-9777-4701

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Machine-Learning Approach to Differentiation...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Machine-Learning Approach to Differentiation...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Machine-Learning Approach to Differentiation...
questionsmedicales.fr › Maladies du système nerveux › Maladies neuromusculaires › Neuropathies périphériques › Tumeurs du système nerveux périphérique › Tumeurs des gaines nerveuses › Machine-Learning Approach to Differentiation...
questionsmedicales.fr › Maladies du système nerveux › Maladies neuromusculaires › Neuropathies périphériques › Tumeurs du système nerveux périphérique › Tumeurs des gaines nerveuses › Neurofibrome › Neurofibrosarcome › Machine-Learning Approach to Differentiation...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études rétrospectives › Machine-Learning Approach to Differentiation...