Machine learning method for the cellular phenotyping of nasal polyps from multicentre tissue scans

ABSTRACT

Background

This study aimed to establish a convenient and accurate chronic rhinosinusitis evaluation platform CRSAI 1.0 according to four phenotypes of nasal polyps.

Research design and methods

Tissue sections of a training (n = 54) and test cohort (n = 13) were sourced from the Tongren Hospital, and those for a validation cohort (n = 55) from external hospitals. Redundant tissues were automatically removed by the semantic segmentation algorithm of Unet++ with Efficientnet-B4 as backbone. After independent analysis by two pathologists, four types of inflammatory cells were detected and used to train the CRSAI 1.0. Dataset from Tongren Hospital were used for training and testing, and validation tests used the multicentre dataset.

Results

The mean average precision (mAP) in the training and test cohorts for tissue eosinophil%, neutrophil%, lymphocyte%, and plasma cell% was 0.924, 0.743, 0.854, 0.911 and 0.94, 0.74, 0.839, and 0.881, respectively. The mAP in the validation dataset was consistent with that of the test cohort. The four phenotypes of nasal polyps varied significantly according to the occurrence of asthma or recurrence.

Conclusions

CRSAI 1.0 can accurately identify various types of inflammatory cells in CRSwNP from multicentre data, which could enable rapid diagnosis and personalized treatment.

KEYWORDS:

• Accurate diagnosis
• Inflammatory cell types
• machine learning
• multicentre
• nasal polyps

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

W Liu, Y Piao and C Wang were involved in the conception and design of the paper; L Zhang and B Chen were involved in analysis and interpretation of the data; J Ding, C Yue and S Shen were involved in the drafting of the paper and revising it critically for intellectual content; all authors approved the final approval of the version to be published; and that all authors agree to be accountable for all aspects of the work.

Ethical approval

This study was approved by the institutional research ethics committee of Beijing Tongren Hospital, Capital Medical University (approval number: TREC2020–65.A2).

Additional information

Funding

This work was supported by grants from national key R&D program of China (2022YFC2504100), the program for the Changjiang scholars and innovative research team (IRT13082), CAMS innovation fund for medical sciences (2019-I2M-5-022) and the Capital’s Funds for Health Improvement and Research (2022-2-2054).