Publications by authors named "Charlie Saillard"
Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted.
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- Researchers identified two main subtypes of Pancreatic adenocarcinoma (PDAC) based on tumor and stroma characteristics, which can influence patient prognosis and treatment strategies.
- They developed a deep learning model called PACpAInt to quickly classify PDAC using more accessible methods, trained on a diverse set of biopsy data from 202 patients and validated on additional cohorts.
- The model effectively predicts tumor subtypes and survival outcomes while revealing complex tumor-stroma interactions, including new categories like Hybrid and Intermediate tumors that suggest varied evolution in PDAC.
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation.
View Article and Find Full Text PDFBackground And Aims: Standardized and robust risk-stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resection/ablation.
Approach And Results: In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (whole-slide imaging; WSI) to build models for predicting survival of patients with HCC treated by surgical resection. Two independent series were investigated: a discovery set (Henri Mondor Hospital, n = 194) used to develop our algorithms and an independent validation set (The Cancer Genome Atlas [TCGA], n = 328).