19 research outputs found
Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis
Acknowledgements The colorectal cancer microarray was provided by the NHS Grampian Biorepository and the majority of the immunostaining was performed in the Grampian Biorepository laboratory (www.biorepository.nhsgrampian.org/). The antibodies were developed in collaboration with Vertebrate Antibodies Ltd (https://vertebrateantibodies.com/)Peer reviewedPublisher PD
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
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
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia
Background: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. Methods: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months. Results: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model. Conclusions: This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.</p
Transformer-based Model for Oral Epithelial Dysplasia Segmentation
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, resulting in the under/over-treatment of patients. We developed a new Transformer-based pipeline to improve detection and segmentation of OED in haematoxylin and eosin (H&E) stained whole slide images (WSIs). Our model was trained on OED cases (n = 260) and controls (n = 105) collected using three different scanners, and validated on test data from three external centres in the United Kingdom and Brazil (n = 78). Our internal experiments yield a mean F1-score of 0.81 for OED segmentation, which reduced slightly to 0.71 on external testing, showing good generalisability, and gaining state-of-the-art results. This is the first externally validated study to use Transformers for segmentation in precancerous histology images. Our publicly available model shows great promise to be the first step of a fully-integrated pipeline, allowing earlier and more efficient OED diagnosis, ultimately benefiting patient outcomes