A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images.

head and neck cancer machine learning precision medicine radiomics survival study

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
29 Jun 2021
Historique:
received: 13 05 2021
revised: 15 06 2021
accepted: 23 06 2021
entrez: 2 7 2021
pubmed: 3 7 2021
medline: 3 7 2021
Statut: epublish

Résumé

Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan-Meier (KM) curves. A log-rank test was performed for significance ( A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups ( A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.

Sections du résumé

BACKGROUND BACKGROUND
Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard.
PATIENT AND METHODS METHODS
Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan-Meier (KM) curves. A log-rank test was performed for significance (
RESULTS RESULTS
A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (
CONCLUSION CONCLUSIONS
A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.

Identifiants

DOI: 10.3390/cancers13133271 PMID: 34210048 PMC: PMC8269129
pubmed: 34210048
pii: cancers13133271
doi: 10.3390/cancers13133271
pmc: PMC8269129
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Horizon 2020
ID : 689715

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Auteurs

Simon A Keek (SA)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.

Frederik W R Wesseling (FWR)

Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Postbus 3035, 6202 NA Maastricht, The Netherlands.

Henry C Woodruff (HC)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.
Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.
ORCID: 0000-0001-7911-5123

Janita E van Timmeren (JE)

Department of Radiation Oncology, University Hospital Zürich, University of Zürich, Rämistrasse 100, 8091 Zürich, Switzerland.
ORCID: 0000-0002-8166-6853

Irene H Nauta (IH)

Amsterdam UMC, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands.

Thomas K Hoffmann (TK)

Department of Otorhinolaryngology, Head Neck Surgery, i2SOUL Consortium, University of Ulm, Frauensteige 14a (Haus 18), 89075 Ulm, Germany.

Stefano Cavalieri (S)

Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, via Giacomo Venezian, University of Milan, 1 20133 Milano, Italy.
ORCID: 0000-0003-1294-6859

Giuseppina Calareso (G)

Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori via Giacomo Venezian, 1 20133 Milano, Italy.

Sergey Primakov (S)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.

Ralph T H Leijenaar (RTH)

Oncoradiomics SA, Liège, Clos Chanmurly 13, 4000 Liège, Belgium.
ORCID: 0000-0001-8642-9657

Lisa Licitra (L)

Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, via Giacomo Venezian, University of Milan, 1 20133 Milano, Italy.
Department of Oncology and Hemato-Oncology, University of Milan, via S. Sofia 9/1, 20122 Milano, Italy.

Marco Ravanelli (M)

Department of Medicine and Surgery, University of Brescia, Viale Europa, 11-25123 Brescia, Italy.
ORCID: 0000-0002-4841-2771

Kathrin Scheckenbach (K)

Department. of Otorhinolaryngology-Head and Neck Surgery, University Hospital Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany.

Tito Poli (T)

Maxillofacial Surgery Unit, Department of Medicine and Surgery, University of Parma-University Hospital of Parma, via Università, 12-I, 43121 Parma, Italy.

Davide Lanfranco (D)

Maxillofacial Surgery Unit, Department of Medicine and Surgery, University of Parma-University Hospital of Parma, via Università, 12-I, 43121 Parma, Italy.

Marije R Vergeer (MR)

Amsterdam UMC, Cancer Center Amsterdam, Department of Radiation Oncology, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands.

C René Leemans (CR)

Amsterdam UMC, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands.
ORCID: 0000-0001-7887-083X

Ruud H Brakenhoff (RH)

Amsterdam UMC, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands.

Frank J P Hoebers (FJP)

Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Postbus 3035, 6202 NA Maastricht, The Netherlands.

Philippe Lambin (P)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.
Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.
ORCID: 0000-0001-7961-0191

Classifications MeSH