Large scale evaluation of sarcopenia as prognostic factor in lung cancer radiotherapy patients
Green, Andrew Van Herk, Marcel Vasquez Osorio, Eliana Weaver, Jamie M McWilliam, Alan
Green, Andrew
Van Herk, Marcel
Vasquez Osorio, Eliana
Weaver, Jamie M
McWilliam, Alan
Citations
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Abstract
Purpose or Objective
Sarcopenia is a degenerative condition in which muscle
wastes, that has been widely shown to be prognostic for
patients treated with chemotherapy. Sarcopenia is also
emerging as prognostic factor in radiotherapy. However,
to date, cohorts analysed have been small due to the need
for manual segmentations. In this work we analysed a very
large cohort of non-small cell lung cancer (NSCLC)
patients, using an Artifical Intelligence (AI) based
automated segmentation to identify skeletal muscle at the
third lumbar vertebral level (L3) and demonstrate the
prognostic value of skeletal muscle density as a measure
of sarcopenia.
Material and Methods
Whole body PET/CT images from a cohort of 549 NSCLC
patients treated with standard fractionation (55 Gy in 20
fractions) were collected. The slices at the center of the
L3 vertebral body, manually identified, were segmented
using a previously developed AI tool. After visual
inspection, the segmentations were used to compute the
mean skeletal muscle density (SMD). SMD indirectly
measures fat infiltration in the muscle capsule, a common
feature of sarcopenia, with lower SMD indicating more fat.
Known gender differences in skeletal muscle properties
were accounted for by adding +7 HU to SMD for females to
equate their median SMD with that of males. An optimal
threshold in corrected SMD for survival difference was
identified. Kaplan-Meier survival curves were produced for
high- and low-SMD groups. A multivariate Cox regression
model for overall survival accounting for log tumour size,
gender, N stage, Performance Status (PS) and corrected
SMD (as a continuous variable) was produced.
Results
Of the available 549 images, 473 were segmented
successfully (figure 1). The majority of failures occurred in poor quality low-dose CT images; likely as the AI tool was
trained on higher dose CT imaging.
Figure 2a shows the Kaplan-Meier curves produced by
splitting the cohort on corrected SMD of 17 HU, the
identified optimal threshold for survival difference. A
difference in median survival of 3 months is observed
where patients with higher SMD do better. In the
multivariate Cox analysis SMD remained significant (fig.
2b), with a hazard ratio of 0.99 per HU (p=0.02), indicating
that denser muscle is advantageous. In our final model
performance status (PS) was not significant. However,
without SMD, PS was significant (data not shown). Conclusion
We performed semi-automated segmentation for
sarcopenia assessment in a very large cohort of lung cancer
patients, and it was successful in 86% of the images. We
are in the process of improving the AI tool and developing
methods to utilize planning thoracic images as alternative.
A statistically significant difference in survival was
identified for NSCLC patients, where patients with an SMD
>17 HU have an additional 3 months median survival. SMD
remains significant in multivariate analysis with a hazard
ratio of 0.99 per HU. Further work exploring the use of SMD as a quantitative alternative to the qualitative PS is in
order.
Description
Date
2020
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Green A, Van Herk M, Osorio EV, Weaver J, McWilliam A. PD-0428: Large scale evaluation of sarcopenia as prognostic factor in lung cancer radiotherapy patients. Radiotherapy and Oncology . 2020 Nov;152:S234–5.