CT-derived body composition associated with lung cancer recurrence after surgery

Abstract

Objectives: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence.

Methods: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence.

Results: Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years.

Conclusions: Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.

Keywords: Body composition; Image biomarker; Lung cancer; Recurrence; Surgical resection.

Figure. 1.

An example of segmenting five body tissues depicted on CT images.

Figure. 2.

Lung tumor segmentation example: (a) original CT image with a slice thickness of 2.5 mm, (b) segmented tumor, and (c)-(d) 3-D tumor visualization of the tumor and its surrounding vessels.

Figure 3.

The overall Cumulative Incidence Function for the lung cancer recurrence.

Figure 4.

Cumulative Incidence Function of lung cancer recurrence by surgical approach (left: 292 patients with lobectomy, 62 with segmentectomy or wedge, and 9 with pneumonectomy) and by age groups (right: 188 older and 175 younger patients)

Figure 5.

Cross-validated (5-fold) ROC curves at 3-years post-surgery for the overall model and category specific models (based on standard pathology parameters, subject characteristics, CT-computed tumor parameters, and CT-computed body-composition parameters). Dots on the ROC curve for the composite model correspond to 50^th and 25^th percentiles of the predicted risk.

Figure 6.

Cumulative Incidence of lung cancer recurrence (and 95% Confidence limits) for the risk strata representing the highest 50% and lowest 25% of the cross-validated composite model score (with 181 high risk, 92 medium risk, and 90 low risk patients respectively)