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Prediction of neoplastic progression in Barrett’s esophagus using nanoscale nuclear architecture mapping: a pilot study

Published:January 20, 2022DOI:https://doi.org/10.1016/j.gie.2022.01.007

      Background and Aims

      Nanoscale nuclear architecture mapping (nanoNAM), an optical coherence tomography–derived approach, is capable of detecting with nanoscale sensitivity structural alterations in the chromatin of epithelial cell nuclei at risk for malignant transformation. Because these alterations predate the development of dysplasia, we aimed to use nanoNAM to identify patients with Barrett’s esophagus (BE) who might progress to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC).

      Methods

      This is a nested case-control study of 46 BE patients, of which 21 progressed to HGD/EAC over 3.7 ± 2.37 years (cases/progressors) and 25 patients who did not progress over 6.3 ± 3.1 years (control subjects/nonprogressors). The archived formalin-fixed paraffin-embedded tissue blocks collected as part of standard clinical care at the index endoscopy were used. nanoNAM imaging was performed on a 5-μm formalin-fixed paraffin-embedded section, and each nucleus was mapped to a 3-dimensional (3D) depth-resolved optical path difference (drOPD) nuclear representation, quantifying nanoscale-sensitive alterations in the 3D nuclear architecture of the cell. Using 3D-drOPD representation of each nucleus, we computed 12 patient-level nanoNAM features summarizing the alterations in intrinsic nuclear architecture. A risk prediction model was built incorporating nanoNAM features and clinical features.

      Results

      A statistically significant differential shift was observed in the drOPD cumulative distributions between progressors and nonprogressors. Of the 12 nanoNAM features, 6 (mean-maximum, mean-mean, mean-median, entropy-median, entropy-entropy, entropy-skewness) showed a statistically significant difference between cases and control subjects. NanoNAM features based prediction model identified progression in independent validation sets, with an area under the receiver operating characteristic curve of 80.8% ± .35% (mean ± standard error), with an increase to 82.54% ± .46% when combined with length of the BE segment.

      Conclusions

      NanoNAM can serve as an adjunct to histopathologic evaluation of BE patients and aid in risk stratification.

      Abbreviations:

      3D (3-dimensional), AUROC (area under the receiver operating characteristic), BE (Barrett’s esophagus), drOPD (depth-resolved optical path difference), EAC (esophageal adenocarcinoma), HGD (high-grade dysplasia), LGD (low-grade dysplasia), nanoNAM (nanoscale nuclear architecture mapping), NDBE (nondysplastic Barrett’s esophagus), SVM (support vector machine)
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