A Fully Automated and Explainable Algorithm for the Prediction of
  Malignant Transformation in Oral Epithelial Dysplasia
Adam J Shephard Raja Muhammad Saad Bashir Hanya Mahmood Mostafa Jahanifar Fayyaz Minhas Shan E Ahmed Raza Kris D McCombe Stephanie G Craig Jacqueline James Jill Brooks Paul Nankivell Hisham Mehanna Syed Ali Khurram Nasir M Rajpoot
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis
given to lesions of the oral cavity. Its grading suffers from significant
inter-/intra- observer variability, and does not reliably predict malignancy
progression, potentially leading to suboptimal treatment decisions. To address
this, we developed a novel artificial intelligence algorithm that can assign an
Oral Malignant Transformation (OMT) risk score, based on histological patterns
in the in Haematoxylin and Eosin stained whole slide images, to quantify the
risk of OED progression. The algorithm is based on the detection and
segmentation of nuclei within (and around) the epithelium using an in-house
segmentation model. We then employed a shallow neural network fed with
interpretable morphological/spatial features, emulating histological markers.
We conducted internal cross-validation on our development cohort (Sheffield; n
= 193 cases) followed by independent validation on two external cohorts
(Birmingham and Belfast; n = 92 cases). The proposed OMTscore yields an AUROC =
0.74 in predicting whether an OED progresses to malignancy or not. Survival
analyses showed the prognostic value of our OMTscore for predicting malignancy
transformation, when compared to the manually-assigned WHO and binary grades.
Analysis of the correctly predicted cases elucidated the presence of
peri-epithelial and epithelium-infiltrating lymphocytes in the most predictive
patches of cases that transformed (p < 0.0001). This is the first study to
propose a completely automated algorithm for predicting OED transformation
based on interpretable nuclear features, whilst being validated on external
datasets. The algorithm shows better-than-human-level performance for
prediction of OED malignant transformation and offers a promising solution to
the challenges of grading OED in routine clinical practice.