Deep inspiration breath hold in locally advanced lung cancer radiotherapy: validation of intrafractional geometric uncertainties in the INHALE trial

Authors Josipovic M, Aznar MC, Thomsen JB, Scherman J, Damkjaer SM, Nygård L, Specht L, Pøhl M, Persson GF
Source Br J Radiol. 2019 Dec;92(1104):20190569 Publicationdate 26 Sep 2019
Abstract

OBJECTIVES

Patients with locally advanced non-small cell lung cancer (NSCLC) were included in a prospective trial for radiotherapy in deep inspiration breath-hold (DIBH). We evaluated DIBH compliance and target position reproducibility.

METHODS

Voluntary, visually guided DIBHs were performed with optical tracking. Patients underwent three consecutive DIBH CT scans for radiotherapy planning. We evaluated the intrafractional uncertainties in the position of the peripheral tumour, lymph nodes and differential motion between them, enabling PTV margins calculation. Patients who underwent all DIBH imaging and had tumor position reproducibility <8 mm were up-front DIBH compliant. Patients who performed DIBHs throughout the treatment course were overall DIBH compliant. Clinical parameters and DIBH-related uncertainties were validated against our earlier pilot study.

RESULTS

69 of 88 included patients received definitive radiotherapy. 60/69 patients (87%) were up-front DIBH compliant. DIBH plan was not superior in seven patients and three lost DIBH ability during the treatment, leaving 50/69 patients (72%) overall DIBH compliant. The systematic and random errors between consecutive DIBHs were small but differed from the pilot study findings. This led to slightly different PTV margins between the two studies.

CONCLUSIONS

DIBH compliance and reproducibility was high. Still, this validation study highlighted the necessity of designing PTV margins in larger, representative patient cohorts. ADVANCES IN KNOWLEDGE: We demonstrated high DIBH compliance in locally advanced NSCLC patients. DIBH does not eliminate but mitigates the target position
uncertainty, which needs to be accounted for in treatment margins. Margin design should be based on data from larger representative patient groups.