Clinical significance of pretreatment tumor growth rate for locally advanced non-small cell lung cancer.

Chemoradiation therapy (CRT) metastases non-small cell lung cancer (NSCLC) specific growth rate (SGR) tumor volume

Journal

Annals of translational medicine
ISSN: 2305-5839
Titre abrégé: Ann Transl Med
Pays: China
ID NLM: 101617978

Informations de publication

Date de publication:
Mar 2019
Historique:
entrez: 26 4 2019
pubmed: 26 4 2019
medline: 26 4 2019
Statut: ppublish

Résumé

Locally advanced non-small cell lung cancer (NSCLC) may exhibit significant tumor growth before the initiation of definitive chemoradiation therapy (CRT). We thus investigated the prognostic value of pretreatment tumor growth rate as measured by specific growth rate (SGR). We conducted a retrospective review of 42 patients with locally advanced NSCLC treated with definitive concurrent CRT. For each patient, we contoured the primary gross tumor volume (GTV) on the pretreatment diagnostic chest computed tomography (CT) scan and the radiation therapy (RT) planning CT scan. We then calculated SGR based on the primary GTV from each scan and the time interval between scans. We used log-rank tests and univariate Cox regression models to quantify differences in progression-free survival (PFS), overall survival (OS) and recurrence based on SGR. We divided patients into two groups for analysis: those with an SGR greater than or equal to the upper tercile value of 0.94%/day (high SGR) and those with SGR less than 0.94%/day (low SGR). Patients with high SGRs versus low SGRs experienced inferior PFS (median, 5.6 Pretreatment SGR was associated with inferior PFS and distant control among patients with locally advanced NSCLC treated with concurrent CRT. Further studies in larger populations may aid in elucidating optimal SGR cut-off points for risk stratification.

Sections du résumé

BACKGROUND BACKGROUND
Locally advanced non-small cell lung cancer (NSCLC) may exhibit significant tumor growth before the initiation of definitive chemoradiation therapy (CRT). We thus investigated the prognostic value of pretreatment tumor growth rate as measured by specific growth rate (SGR).
METHODS METHODS
We conducted a retrospective review of 42 patients with locally advanced NSCLC treated with definitive concurrent CRT. For each patient, we contoured the primary gross tumor volume (GTV) on the pretreatment diagnostic chest computed tomography (CT) scan and the radiation therapy (RT) planning CT scan. We then calculated SGR based on the primary GTV from each scan and the time interval between scans. We used log-rank tests and univariate Cox regression models to quantify differences in progression-free survival (PFS), overall survival (OS) and recurrence based on SGR.
RESULTS RESULTS
We divided patients into two groups for analysis: those with an SGR greater than or equal to the upper tercile value of 0.94%/day (high SGR) and those with SGR less than 0.94%/day (low SGR). Patients with high SGRs versus low SGRs experienced inferior PFS (median, 5.6
CONCLUSIONS CONCLUSIONS
Pretreatment SGR was associated with inferior PFS and distant control among patients with locally advanced NSCLC treated with concurrent CRT. Further studies in larger populations may aid in elucidating optimal SGR cut-off points for risk stratification.

Identifiants

pubmed: 31019945
doi: 10.21037/atm.2019.02.14
pii: atm-07-05-95
pmc: PMC6462644
doi:

Types de publication

Journal Article

Langues

eng

Pagination

95

Déclaration de conflit d'intérêts

Conflicts of Interest: Dr. Jabbour has received research funding from Merck and Nestle. Dr. Malhotra is on the advisory board of Astra-Zeneca and Pfizer and has received research funding from Oncoceutics. The other authors have no conflicts of interest to declare.

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Auteurs

Benedict Osorio (B)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Nikhil Yegya-Raman (N)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Sinae Kim (S)

Department of Biostatistics, School of Public Health, Rutgers University, New Brunswick, NJ, USA.
Biometrics Division, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Charles B Simone (CB)

Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, Maryland, USA.

Christina Theodorou Ross (C)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Matthew P Deek (MP)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.
Department of Radiation Oncology & Molecular Radiation Sciences, the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Dakim Gaines (D)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Wei Zou (W)

Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Liyong Lin (L)

Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

Jyoti Malhotra (J)

Division of Medical Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Ke Nie (K)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Joseph Aisner (J)

Division of Medical Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Salma K Jabbour (SK)

Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.

Classifications MeSH