MS17.03 Quantitative Image Analysis, Image-Based Profiling Including 3D Digital Printing and AI

Digital pathology and AI Digital pathology refers to the use of computer stations to analyze images of whole slides scanned at high resolution for teaching or research purposes; these images are analyzed by algorithms that allow standardization and reproducibility of the analyses. Recently, artificial intelligence has been used to build even more powerful algorithms. The idea is not to reproduce the visual analysis of pathologists but to improve it and to identify new predictive or prognostic morphological characteristics or to couple them with genomic analysis to better stratify tumours (1,2). Technical considerations The digital workflow requires specific equipement (slide scanner, image storage and digital pathology workstation). Image analysis needs well-standardized pre-analytical conditions, especially if IHC stainings are analyzed, where colour variations and absence of background noise must be controlled during different validation steps. Some methods exist to correct artifacts such as section fold, fuzzy area. SOPs (standardised operating procedures) and regulations must also be respected concerning the scanning platforms, which must be CE IVD or FDA approved, the construction of databases, anonymisation, sending and image analysis. Algorithms used for analyses perform different operations on digital images: quality improvement, filtering, recording and segmentation and they must also be validated by correlation measuring the reproducibility between pathologists and algorithms. In addition, The large amount of data requires significant computer processing with accelerated calculations by CNNs (Convolutional Neural Networks) and large image storage capacity. Applications: Quantitative analyses and Image-Based Profiling: most image analyses proposed by commercial softwares (VisionPharm, Definiens Tissue Studio, Indica Labs HALO), or open source softwares such as QuPath Open Source Software for Quantitative pathology or ImageJ, are area or cells-based measurements. Area- based measurements include quantification of stained zones (using IHC stain for example) ; cell_based measurements require segmentation steps to delineate tissue compartments, to distinguish for example tumor from begnin regions or to identify subcellular structures (such as nuclei). Some modules or algorithms are CE-IVD or FDA approved such as those used to quantify the expression of ER, PR, Her2 and KI67 in breast cancers. Other algorithms enable the estimation of the percentage of tumour cells before molecular analysis for the detection of genes mutations (Tissue Mark, Philipps pathology). They can also enable to localise and quantify the immune infiltrate ( CD3 and CD8+ T Cells) within the tumor stroma (Immunoscore , Laboratory of Integrative cancer Immunology INSERM Paris). Some machine Learning methods can also automatically differentiate tumor cells from stroma cells and inflammatory cells or identify lymph node metastases. In thoracic pathology, automated whole slide scoring of PDL1 has been proposed in NSCLC with an excellent agreement for tumor cells scoring and a good concordance for immune cells (3). Image based analysis has been used for quantification of immune checkpoints molecules co expression in NSCLC (4). Several studies have showed the interest of automatic image-based analysis for the optimization of histological or cytological classification of lung cancer and prediction of prognosis (5,6). Deep learning technology has been used to classify lung tumour subtypes from the virtual slides available in the TCGA (7), or to classifiy adenocarcinoma according to the predominant pattern with a kappa score of 0.525 and an agreement of 66.6% with three pathologists (8). Deep learning was also proposed for genomic profiling of tumours, with a prediction of STK11, EGFR, FAT1, SETBP1, KRAS and TP53 mutations from pathology images showing an AUCs ranging from 0.733 to 0.856 (9,10). Applications: 3D dimensions modeling and digital Printing Three-dimensional (3D) photogrammetry is a method of image-based modeling in which data points in digital images, taken from offset viewpoints, are analyzed to generate a 3D model. This technique can be used to generate 3D representation sof surgical specimens, for routine gross examination, in multidisciplinary meetings to improve clinicopathologic correlation between surgeon and pathologists, and for education purposes via 3D printing specimen models (11). 1. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. mai 2019;20(5):e253-61. 2. Aeffner F, Zarella M, Buchbinder N, Bui M, Goodman M, Hartman D, et al. Introduction to digital image analysis in whole-slide imaging: A white paper from the digital pathology association. J Pathol Inform. 2019;10(1):9. 3. Taylor CR, Jadhav AP, Gholap A, Kamble G, Huang J, Gown A, et al. A Multi-Institutional Study to Evaluate Automated Whole Slide Scoring of Immunohistochemistry for Assessment of Programmed Death-Ligand 1 (PD-L1) Expression in Non–Small Cell Lung Cancer: Appl Immunohistochem Mol Morphol. avr 2019;27(4):263-9. 4. Parra ER, Villalobos P, Zhang J, Behrens C, Mino B, Swisher S, et al. Immunohistochemical and Image Analysis-Based Study Shows That Several Immune Checkpoints are Co-expressed in Non–Small Cell Lung Carcinoma Tumors. J Thorac Oncol. juin 2018;13(6):779-91. 5. Yu K-H, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun [Internet]. nov 2016 [cité 10 juin 2019];7(1). Disponible sur: http://www.nature.com/articles/ncomms12474 6. Luo X, Zang X, Yang L, Huang J, Liang F, Rodriguez-Canales J, et al. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. J Thorac Oncol. mars 2017;12(3):501-9. 7. Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. EBioMedicine. janv 2018;27:317-28. 8. Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep [Internet]. déc 2019 [cité 10 juin 2019];9(1). Disponible sur: http://www.nature.com/articles/s41598-019-40041-7 9. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med. oct 2018;24(10):1559-67. 10. Patil PD, Hobbs B, Pennell NA. The promise and challenges of deep learning models for automated histopathologic classification and mutation prediction in lung cancer. J Thorac Dis. févr 2019;11(2):369-72. 11. Turchini J, Buckland ME, Gill AJ, Battye S. Three-Dimensional Pathology Specimen Modeling Using “Structure-From-Motion” Photogrammetry: A Powerful New Tool for Surgical Pathology. Arch Pathol Lab Med. nov 2018;142(11):1415-20. digital pathology, deep learning, image analysis