Volumetric Measurements in Lung Cancer Screening Reduces Unnecessary Low-Dose Computed Tomography Scans : Results from a Single-Center Prospective Trial on 4119 Subjects

This study aims to compare the low-dose computed tomography (LDCT) outcome and volume-doubling time (VDT) derived from the measured volume (MV) and estimated volume (EV) of pulmonary nodules (PNs) detected in a single-center lung cancer screening trial. MV, EV and VDT were obtained for prevalent pulmonary nodules detected at the baseline round of the bioMILD trial. The LDCT outcome (based on bioMILD thresholds) and VDT categories were simulated on PN- and screenee-based analyses. A weighted Cohen's kappa test was used to assess the agreement between diagnostic categories as per MV and EV, and 1583 screenees displayed 2715 pulmonary nodules. In the PN-based analysis, 40.1% PNs were included in different LDCT categories when measured by MV or EV. The agreements between MV and EV were moderate (κ = 0.49) and fair (κ = 0.37) for the LDCT outcome and VDT categories, respectively. In the screenee-based analysis, 46% pulmonary nodules were included in different LDCT categories when measured by MV or EV. The agreements between MV and EV were moderate (κ = 0.52) and fair (κ = 0.34) for the LDCT outcome and VDT categories, respectively. Within a simulated lung cancer screening based on a recommendation by estimated volumetry, the number of LDCTs performed for the evaluation of pulmonary nodules was higher compared with in prospective volumetric management.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:12

Enthalten in:

Diagnostics (Basel, Switzerland) - 12(2022), 2 vom: 18. Jan.

Sprache:

Englisch

Beteiligte Personen:

Milanese, Gianluca [VerfasserIn]
Sabia, Federica [VerfasserIn]
Ledda, Roberta Eufrasia [VerfasserIn]
Sestini, Stefano [VerfasserIn]
Marchianò, Alfonso Vittorio [VerfasserIn]
Sverzellati, Nicola [VerfasserIn]
Pastorino, Ugo [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Lung cancer screening
Pulmonary nodules
Semi-automated volumetry

Anmerkungen:

Date Revised 28.02.2022

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.3390/diagnostics12020229

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM337358710