Une approche d'apprentissage few-shot transductive pour la classification de lames histopathologiques numériques du cancer du foie

This paper presents a new approach for classifying 2D histopathology patches using few-shot learning. The method is designed to tackle a significant challenge in histopathology, which is the limited availability of labeled data. By applying a sliding window technique to histopathology slides, we illustrate the practical benefits of transductive learning (i.e., making joint predictions on patches) to achieve consistent and accurate classification. Our approach involves an optimization-based strategy that actively penalizes the prediction of a large number of distinct classes within each window. We conducted experiments on histopathological data to classify tissue classes in digital slides of liver cancer, specifically hepatocellular carcinoma. The initial results show the effectiveness of our method and its potential to enhance the process of automated cancer diagnosis and treatment, all while reducing the time and effort required for expert annotation

[18F]FDG PET/CT predicts progression-free survival in patients with idiopathic pulmonary fibrosis

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a devastating disease characterized by an unpredictable course. Prognostic markers and disease activity markers are needed. The purpose of this single-center retrospective study was to evaluate the prognostic value of lung fluorodeoxyglucose ([18F]-FDG) uptake assessed by standardized uptake value (SUV), metabolic lung volume (MLV) and total lesion glycolysis (TLG) in patients with IPF. Methods We included 27 IPF patients (IPF group) and 15 patients with a gastrointestinal neuroendocrine tumor without thoracic involvement (control group). We quantified lung SUV mean and SUV max, MLV and TLG and assessed clinical data, high-resolution CT (HRCT) fibrosis and ground-glass score; lung function; gender, age, physiology (GAP) stage at inclusion and during follow-up; and survival. Results Lung SUV mean and SUV max were higher in IPF patients than controls (p <0.00001). For patients with IPF, SUV mean, SUV max, MLV and TLG were correlated with severity of lung involvement as measured by a decline in forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) and increased GAP score. In a univariate and in a multivariate Cox proportional-hazards model, risk of death was increased although not significantly with high SUV mean. On univariate analysis, risk of death was significantly associated with high TLG and MLV, which disappeared after adjustment functional variables or GAP index. Increased MLV and TLG were independent predictors of death or disease progression during the 12 months after PET scan completion (for every 100-point increase in TLG, hazard ratio [HR]: 1.11 (95% CI 1.06; 1.36), p = 0.003; for every 100-point increase in MLV, HR: 1.20 (1.04; 1.19), p = 0.002). On multivariable analysis including TLG or MLV with age, FVC, and DLCO or GAP index, TLG and MLV remained associated with progression-free survival (HR: 1.1 [1.03; 1.22], p = 0.01; and 1.13 [1.0; 1.2], p = 0.005). Conclusion FDG lung uptake may be a marker of IPF severity and predict progression-free survival for patients with IPF

Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning

Background &amp; Aims: The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Method: We selected 166 PLC biopsies divided into training, internal and external validation sets: 90, 29 and 47 samples, respectively. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, tiles of 256x256 pixels were extracted from the WSIs and used to train a ResNet18 neural network. The tumour/non-tumour annotations served as labels during training, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Results: Pathological review classified the training and validation sets into hepatocellular carcinoma (HCC, 33/90, 11/29 and 26/47), intrahepatic cholangiocarcinoma (iCCA, 28/90, 9/29 and 15/47), and cHCC-CCA (29/90, 9/29 and 6/47). In the two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the two-cluster model predictions (major contingent) in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (cluster 0: 5-97%; cluster 1: 2-94%). Conclusion: Our method applied to PLC HES biopsy could identify specific morphological features of HCC and iCCA. Although no specific features of cHCC-CCA were recognized, assessing the proportion of HCC and iCCA tiles within a slide could facilitate the identification of cHCC-CCA. Impact and implications: The diagnosis of primary liver cancers can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified primary liver cancers on routine-stained biopsies using a weakly supervised learning method. Our model identified specific features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Despite no specific features of cHCC-CCA being recognized, the identification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma tiles within a slide could facilitate the diagnosis of primary liver cancers, and particularly cHCC-CCA

Advanced epithelioid hemangioendothelioma of the liver: could lenvatinib offer a bridge treatment to liver transplantation?

In this article, we describe the case of a 34-year-old woman presenting a multifocal and metastatic epithelioid hemangioendothelioma (HEHE) of the liver. Under classical chemotherapy using cyclophosphamide, there was a fast tumor progression in liver and extra-hepatic metastatic sites (lungs and mediastinal lymph node). Taking into account the patient's age and the natural history of the HEHE, our goal was to try to bring her to liver transplantation (LT) and lenvatinib was an acceptable candidate for this reason. Shortly after the initiation of lenvatinib before LT and surgery, we observed the enlargement of large devascularized necrotic areas in most of the liver HEHE masses, suggesting a good response. The patient was finally transplanted 6 months after initiation of lenvatinib treatment. Eight months after LT, progression occurred (ascites, peritoneal recurrence, and mediastinal lymph node). After restarting lenvatinib, ascites disappeared and the lymph node decreased in size, suggesting a good response, more than 1 year after her transplantation. This is the first case report to our knowledge that illustrates the benefit of lenvatinib as a neoadjuvant bridge until LT for a multifocal and metastatic HEHE. In addition, this drug has also shown a benefit in term of disease control after a late recurrence of the tumor. We suggest that lenvatinib should be proposed as a bridge to the LT for nonresectable HEHE. Moreover, this drug was also beneficial in the treatment of late recurrence after LT. The absence of pharmacologic interactions between classical immunosuppressive drugs and lenvatinib may allow its use as an early adjuvant approach when the risk of recurrence is high. The strength of our case consists in the long follow-up and the innovative message allowing changing palliative strategies into curative ones in case of advanced HEHE