Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study.

Background: Clinical benefits of atezolizumab plus bevacizumab (atezolizumab-bevacizumab) are observed only in a subset of patients with hepatocellular carcinoma and the development of biomarkers is needed to improve therapeutic strategies. The atezolizumab-bevacizumab response signature (ABRS), assessed by molecular biology profiling techniques, has been shown to be associated with progression-free survival after treatment initiation. The primary objective of our study was to develop an artificial intelligence (AI) model able to estimate ABRS expression directly from histological slides, and to evaluate if model predictions were associated with progression-free survival.

Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world.

Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.

Background & Aims: Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however, the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological images, to develop models able to predict the activation of 6 immune gene signatures.

Methods: AI models were trained and validated in 2 different series of patients with HCC treated by surgical resection.

[The first data challenge of the french society of pathology: An international competition in 2020, a research tool in A.I. for the future?].

The french society of pathology (SFP) organized in 2020 its first data challenge with the help of Health Data Hub (HDH). The organisation of this event first consisted in recruiting almost 5000 slides of uterus cervical biopsies obtained in 20 pathology centers. After having made sure that patients did not refuse to include their slides in the project, the slides were anonymised, digitized and annotated by expert pathologists, and were finally uploaded on a data challenge platform for competitors all around the world.

Artificial intelligence for solid tumour diagnosis in digital pathology.

Tumour diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information due to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms.

Pembrolizumab with Capox Bevacizumab in patients with microsatellite stable metastatic colorectal cancer and a high immune infiltrate: The FFCD 1703-POCHI trial.

Pembrolizumab, a PD1 immune checkpoint inhibitor (ICI), was recently reported to be very effective in patients with microsatellite instable/deficient mismatch repair metastatic colorectal cancer (MSI/dMMR mCRC), unlike patients with microsatellite stable/proficient MMR (MSS/pMMR) mCRC, in whom ICIs are generally ineffective. However, about 15% of MSS/pMMR CRCs are highly infiltrated by tumour infiltrating lymphocytes. In addition, both oxaliplatin and bevacizumab have been shown to have immunomodulatory properties that may increase the efficacy of an ICI.

Mitosis detection in breast cancer histological images An ICPR 2012 contest.

Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study.

Time-efficient sparse analysis of histopathological whole slide images.

Histopathological examination is a powerful standard for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of whole slide images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI.

A cognitive virtual microscopic framework for knowledge-based exploration of large microscopic images in breast cancer histopathology.

Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Analysis of medical images largely remains the work of human experts. Current virtual microscope systems are mainly an emulation of real microscopes with annotation and some image analysis capabilities.

An automatic system for the analysis of intercellular communication and early carcinogenesis.

Some recent works on intercellular communication pointed out an impaired trafficking of Cx43 proteins in early carcinogenesis. In collaboration with biologists, we propose an automatic system for the analysis of spatial protein configurations within cells at early tumor stages. This system is an essential step towards the future development of a computer-aided diagnosis tool and the statistical validation of biological hypotheses about Cx43 expressions and configurations during tumorogenesis.