Computed Tomography Morphometrics and Pulmonary Intolerance in Endometrial Cancer Robotic Surgery

Using simple radiologic measurements to anticipate surgical challenge in endometrial cancer: a prospective study

Objectives

To determine if linear measurements of adiposity from pre-operative imaging can improve anticipation of surgical difficulty among endometrial cancer patients.

Methods

Eighty patients with newly diagnosed endometrial cancer were enrolled. Routine pre-operative imaging (MRI or CT) was performed. Radiologic linear measurements of the following were obtained: anterior-to-posterior skin distance; anterior skin to anterior edge of L5 distance (total anterior); anterior peritoneum to anterior edge of L5 distance (visceral obesity); and posterior edge of L5 to posterior skin distance (total posterior). Surgeons completed questionnaires quantifying preoperative anticipated operative difficulty and postoperative reported operative difficulty. The primary objective was to assess for a correlation between linear measurements of visceral fat and reported operative difficulty.

Results

Seventy-nine patients had questionnaires completed, preoperative imaging obtained, and surgery performed. Univariate analysis showed all four linear measurements, body mass index, weight, and anticipated operative difficulty were associated with increased reported operative difficulty (P< 0.05). Multivariate analysis demonstrated that body mass index and linear measurements visceral obesity and total posterior were independently associated with increased reported operative difficulty (P< 0.05). Compared with body mass index, the visceral obesity measurement was more sensitive and specific for predicting increased reported operative difficulty (visceral obesity; sensitivity 54%, specificity 91 %; body mass index; sensitivity 38%, specificity 89%). A difficulty risk model combining body mass index, visceral obesity, and total posterior demonstrated better predictive performance than any individual preoperative variable.

Conclusions

Simple linear measurements of visceral fat obtained from preoperative imaging are more predictive than body mass index alone in anticipating surgeon-reported operative difficulty. These easily obtained measurements may assist in preoperative decision making in this challenging patient population.

Validation of a DIXON-based fat quantification technique for the measurement of visceral fat using a CT-based reference standard

PURPOSE

The purpose of the study is to determine whether a novel semi-automated DIXON-based fat quantification algorithm can reliably quantify visceral fat using a CT-based reference standard.

METHODS

This was an IRB-approved retrospective cohort study of 27 subjects who underwent abdominopelvic CT within 7 days of proton density fat fraction (PDFF) mapping on a 1.5T MRI. Cross-sectional visceral fat area per slice (cm²) was measured in blinded fashion in each modality at intervertebral disc levels from T12 to L4. CT estimates were obtained using a previously published semi-automated computational image processing system that sums pixels with attenuation - 205 to - 51 HU. MR estimates were obtained using two novel semi-automated DIXON-based fat quantification algorithms that measure visceral fat area by spatially regularizing non-uniform fat-only signal intensity or de-speckling PDFF 2D images and summing pixels with PDFF ≥ 50%. Pearson's correlations and Bland-Altman analyses were performed.

RESULTS

Visceral fat area per slice ranged from 9.2 to 429.8 cm² for MR and from 1.6 to 405.5 cm² for CT. There was a strong correlation between CT and MR methods in measured visceral fat area across all studied vertebral body levels (r = 0.97; n = 101 observations); the least (r = 0.93) correlation was at T12. Bland-Altman analysis revealed a bias of 31.7 cm² (95% CI [- 27.1]-90.4 cm²), indicating modestly higher visceral fat assessed by MR.

CONCLUSION

MR- and CT-based visceral fat quantification are highly correlated and have good cross-modality reliability, indicating that visceral fat quantification by either method can yield a stable and reliable biomarker.