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Application of pattern recognition software on preclinical and safety studies Alison Bigley CSci CSci,, FIBMS “There are only patterns, patterns on top of patterns, patterns that affect other patterns. Patterns hidden by patterns. Patterns within patterns. What we call chaos are patterns we haven't recognized. What we call random are patterns we can't decipher. what we can't understand we call nonsense” Charles Michael “Chuck” Palahniuk (Author – 2013) The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities classifying the data into different categories (Prof. Chris Bishop) Background noise Calculus Uncertainty Linear algebra Finite data sets Probability theory Pattern recognition software with a view to: Hierarchical/tiered workflows and utilisation on preclinical and safety tissues prior to or in conjunction with ‘routine analysis’ Comparison of 4 different pattern recognition software systems in the evaluation of progesterone receptor in breast TMA Current applications and future integrations within the drug discovery process and potential translation to companion algorithms Pattern recognition software – approach to analysis optimisation TRAINING DATA feature selection /extraction Input image features label pattern recognition algorithm classifier (model) STUDY/TEST DATA feature extraction features classifier (model) Input image label > Classifiers > Time to run > Image Resolution > Time to run > Complexity > Class variation < Accuracy Validation against ‘ground truth’ Generally classification systems utilise a single tier approach results data Pattern recognition single tiered / hierarchical workflows ROI / CELL SLIDE SCANNING ANALYSIS / MEASURES (TISSUE/ROI DETECTION) Tier 1 Low Res x1.0 – 2.5 CELL DETECTION PATTERN RECOGNITION TISSUE DETECTION Tier 3 Tier 2 High Res x10-40 Med Res x5.0 – 10 TUMOUR DETECTION H&E – normal tissues / tumour detection / classification x2.5 magnification x5 magnification IHC labelled – Kidney cell component classification x10 magnification Pattern recognition application • Value of pattern recognition in digital pathology • Enables a more practical solution for ROI identification/selection on large studies by multiple users • Reduces degree of pre-conceived ideas, obviates bias • Potential identification/elimination of inherent experimental variation in tissue sections • Potential to use misclassification rate as a marker of response • Current PR software user-friendly • Validation of pattern recognition classifiers in digital pathology • Need to confirm robustness of classifiers • Range of slides, several runs, multiple studies • Increase size of data sets - re-check classifier performance Pattern recognition - considerations • Establishing classifications based on identification of a single tissue type or tumour type: • • • • • Potential lack of robustness due to insufficient training data Single application may not translate to other ‘like’ studies Tumour/tissue heterogeneity may not be accounted for Impact on experimental design Incompatibility with range of staining applications • Specific object orientation • May require associated image registration to eliminate effect of orientation e.g. polarised cells • Stereological approach may be more applicable for anisotropic (orientation dependent) Pattern recognition - considerations • Artefacts/facts • Elimination of background artefacts that may hinder recognition • Ensuring suitability of quantifiable tissue • Influence of staining on PR spectral and texture feature detection • Classifiers • • • • • Appropriate segmentation of training data Wide range of representative image features per class Insufficient optimisation Too many classes – increase computational requirements Too few classes – noisy data Misclassification RESEARCH Project specific research areas Disease models Complementary technology Imaging groups integration/collaboration WHAT ARE THE QUESTIONS THAT PATTERN RECOGNITION IN DIGITAL PATHOLOGY IS TRYING TO ADDRESS? Target expression & functionality Disease models Efficacy Species variation Toxicity, Safety Primary diagnosis Patient stratification Prognostic value Companion algorithms Clinical translation CLINICAL PRE-CLINICAL OBSERVE Clinical biomarkers DETECT MEASURE DEMONSTRATE TRANSLATE Pattern recognition software comparison – Breast tumour TMA, progesterone receptor, IHC stained Feature selection Interactive ROI tools Object based, Paintbrush Interactive ROI tools Paint brush Interactive object tools Image features RGB spectral, texture, density RGB spectral, object & pixel based RGB spectral, density, morphology RGB spectral , texture & edges Multispectral Classifier model Mahalanobis k-NN, SVM, Bayes, CART Bayes Random forest decision tree Number of classes/labels 2- 2- Membership functions & conditional reclassification for fine tuning Additions Cell level analysis 2-8 Requires separate algorithms Integral for continuous workflow 2- 2- Minimum class area refinement Requires separate algorithms Integral for continuous workflow Integral for continuous workflow Time taken to develop classifiers ranging from 10 - 30 mins Comparative image analysis: Breast TMA evaluation of Progesterone Receptor labelling index using tumour pattern recognition and nuclear detection Future application of Pattern recognition technology and workflows TISSUE ARCHITECTURE SPATIAL MAPPING MORPHOLOGICAL PATTERNS TISSUE / TUMOUR REGISTRATION TUMOUR MAPPING PHENOTYPIC FINGERPRINTING TIER 2 INTRA-TUMOUR SCAFFOLDING ‘NORMAL TISSUE ENVIRONMENT’ DISTRIBUTION Tissue/Cells ‘Exception classification’ CONNECTIVITY ‘Misclassification levels’ (SIMPLE/COMPOUND, TUBULAR/ALVEOLAR) NEIGHBOURHOOD RELATIONSHIPS TIER 3 CELL-CELL HETEROGENEITY ENVIRONMENT DISTANCE PROFILING PATIENT SELECTION NUCLEAR FEATURES CLINICAL OUTCOME Future perspective of Digital Pathology Imaging IHC, ISH, FISH PLA, multiplexing Right patient Right molecular change Right treatment Brightfield, fluorescence & multispectral Improved understanding of Analysis of large data sets of WSI tumour heterogeneity and associated tissue reducing need for manual microenvironment changes, interactive processes. both morphological & Serial section combination molecular level, in relation to 12% growth in the digital analysis (SSCA) tumour progression, prognosis pathology scanning platform and personalised medicines. Increased utilisation of pattern technology. Supporting pharma, recognition, neural network Companion algorithms. research and clinical. algorithms (machine learning) Tumour/tissue pattern library. Utilised on analysis of large with improved data analysis, modelling & mining applied to image datasets. Image & analysis standardisation. protein and molecular pathology. Virtual microscopy to replace lab Alignment with other technologies microscope. e.g. mass spec imaging