Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging. (November 2021)

• Record Type: Journal Article
• Authors: Beuque, Manon; Martin-Lorenzo, Marta; Balluff, Benjamin; Woodruff, Henry C.; Lucas, Marit; de Bruin, Daniel M.; van Timmeren, Janita E.; Boer, Onno J.de; Heeren, Ron MA.; Meijer, Sybren L.; Lambin, Philippe
• Is Part Of: Computers in biology and medicine. Volume 138(2021)
• Publication Date: 2021-11
• Subjects: Deep learning -- Machine learning -- Mass spectrometry imaging -- H&E staining -- Barrett's esophagus; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104918
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE. Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n = 15), LGD non-progressive (n = 14), LGD progressive (n = 14), and HGD (n = 14). MSI experiments were conducted at 50 × 50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144, 823 tiles for the whole dataset. Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)). Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches. Data are available via ProteomeXchange with identifier PXD028949. Highlights: Investigate machine learning solutions to MSI and H&E data in Barrett's esophagus. H&E-based classifier was best at distinguishing tissue types. MSI-based classifier was better at distinguishing dysplastic grades. MSI-based classifier was better at predicting progression of disease. Study shows complementarity of imaging modalities for different diagnostic tasks.