IDH-wild-type glioblastoma cell density and infiltration distribution influence on supramarginal resection and its impact on overall survival: a mathematical model.

IDH–wild type glioblastoma mathematical model oncology supramarginal resection supratotal resection

Journal

Journal of neurosurgery
ISSN: 1933-0693
Titre abrégé: J Neurosurg
Pays: United States
ID NLM: 0253357

Informations de publication

Date de publication:
01 Jun 2022
Historique:
received: 08 04 2021
accepted: 18 06 2021
medline: 30 10 2021
pubmed: 30 10 2021
entrez: 29 10 2021
Statut: epublish

Résumé

Recent studies have proposed resection of the T2 FLAIR hyperintensity beyond the T1 contrast enhancement (supramarginal resection [SMR]) for IDH-wild-type glioblastoma (GBM) to further improve patients' overall survival (OS). GBMs have significant variability in tumor cell density, distribution, and infiltration. Advanced mathematical models based on patient-specific radiographic features have provided new insights into GBM growth kinetics on two important parameters of tumor aggressiveness: proliferation rate (ρ) and diffusion rate (D). The aim of this study was to investigate OS of patients with IDH-wild-type GBM who underwent SMR based on a mathematical model of cell distribution and infiltration profile (tumor invasiveness profile). Volumetric measurements were obtained from the selected regions of interest from pre- and postoperative MRI studies of included patients. The tumor invasiveness profile (proliferation/diffusion [ρ/D] ratio) was calculated using the following formula: ρ/D ratio = (4π/3)2/3 × (6.106/[VT21/1 - VT11/1])2, where VT2 and VT1 are the preoperative FLAIR and contrast-enhancing volumes, respectively. Patients were split into subgroups based on their tumor invasiveness profiles. In this analysis, tumors were classified as nodular, moderately diffuse, or highly diffuse. A total of 101 patients were included. Tumors were classified as nodular (n = 34), moderately diffuse (n = 34), and highly diffuse (n = 33). On multivariate analysis, increasing SMR had a significant positive correlation with OS for moderately and highly diffuse tumors (HR 0.99, 95% CI 0.98-0.99; p = 0.02; and HR 0.98, 95% CI 0.96-0.99; p = 0.04, respectively). On threshold analysis, OS benefit was seen with SMR from 10% to 29%, 10% to 59%, and 30% to 90%, for nodular, moderately diffuse, and highly diffuse, respectively. The impact of SMR on OS for patients with IDH-wild-type GBM is influenced by the degree of tumor invasiveness. The authors' results show that increasing SMR is associated with increased OS in patients with moderate and highly diffuse IDH-wild-type GBMs. When grouping SMR into 10% intervals, this benefit was seen for all tumor subgroups, although for nodular tumors, the maximum beneficial SMR percentage was considerably lower than in moderate and highly diffuse tumors.

Identifiants

pubmed: 34715662
doi: 10.3171/2021.6.JNS21925
pii: 2021.6.JNS21925
pmc: PMC9248269
mid: NIHMS1816814
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1567-1575

Subventions

Organisme : NCI NIH HHS
ID : R01 CA216855
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA195503
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA217836
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA183827
Pays : United States
Organisme : NCI NIH HHS
ID : R33 CA240181
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA015083
Pays : United States
Organisme : NCI NIH HHS
ID : R43 CA221490
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA200399
Pays : United States

Auteurs

Shashwat Tripathi (S)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.
10Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and.

Tito Vivas-Buitrago (T)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.
11Department of Health Sciences, School of Medicine, Universidad de Santander UDES, Bucaramanga, Colombia.

Ricardo A Domingo (RA)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Gaetano De Biase (G)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Desmond Brown (D)

2Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.

Oluwaseun O Akinduro (OO)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Andres Ramos-Fresnedo (A)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Wendy Sherman (W)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.
7Department of Neurology, Division of Neuro-Oncology, Mayo Clinic, Jacksonville.

Vivek Gupta (V)

8Department of Radiology, Mayo Clinic, Jacksonville.

Erik H Middlebrooks (EH)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.
8Department of Radiology, Mayo Clinic, Jacksonville.

David S Sabsevitz (DS)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.
9Department of Psychology, Mayo Clinic, Jacksonville, Florida.

Alyx B Porter (AB)

5Department of Neurology, Division of Neuro-Oncology, Mayo Clinic, Phoenix, Arizona.

Joon H Uhm (JH)

6Department of Neurology, Division of Neuro-Oncology, Mayo Clinic, Rochester, Minnesota.

Bernard R Bendok (BR)

3Department of Neurosurgery, Mayo Clinic, Phoenix.

Ian Parney (I)

2Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.

Fredric B Meyer (FB)

2Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.

Kaisorn L Chaichana (KL)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Kristin R Swanson (KR)

3Department of Neurosurgery, Mayo Clinic, Phoenix.
4Mathematical Neuro-Oncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix.

Alfredo Quiñones-Hinojosa (A)

1Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida.

Classifications MeSH