Publications associées à Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabo ...

Nature Portfolio2024

The role of LRRK2 in lung adenocarcinoma

Aspasia Gkasti

Lung cancer is the leading cause of cancer-related deaths worldwide and the most common lung cancer subtype is lung adenocarcinoma (LUAD). Frequently mutated genes involve activating mutations in KRAS and loss of function mutations in TP53. LUADs primarily ...

EPFL2024

Multimodal fusion of liquid biopsy and CT enhances differential diagnosis of early-stage lung adenocarcinoma

Jiancheng Yang, Li Zhang, Jun Lu, Hua Zhong, Yunhua Zhang

This research explores the potential of multimodal fusion for the differential diagnosis of early-stage lung adenocarcinoma (LUAD) (tumor sizes < 2 cm). It combines liquid biopsy biomarkers, specifically extracellular vesicle long RNA (evlRNA) and the comp ...

Nature Portfolio2024

Bcl-xL targeting eliminates ageing tumor-promoting neutrophils and inhibits lung tumor growth

Freddy Radtke, Nadine Fournier, Amber Dawn Bowler, Etienne Meylan, Anita Bodac, Justine Pascual, Vincent Roh

Elevated peripheral blood and tumor-infiltrating neutrophils are often associated with a poor patient prognosis. However, therapeutic strategies to target these cells are difficult to implement due to the life-threatening risk of neutropenia. In a genetica ...

Springernature2024

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma

Jiancheng Yang, Li Zhang, Lin Chen, Qiong Li, Ying Zhu

Objectives To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. ...

SPRINGER2023

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma

Jiancheng Yang, Li Zhang, Xinchao Wang, Hua Sun

Background: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.Methods: In this retrospective study, an AI algorithm w ...

Elsevier Ireland Ltd2023

Cancer hallmarks intersect with neuroscience in the tumor microenvironment

Douglas Hanahan

The mechanisms underlying the multistep process of tumorigenesis can be distilled into a logical framework involving the acquisition of functional capabilities, the so-called hallmarks of cancer, which are collectively envisaged to be necessary for maligna ...

CELL PRESS2023

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Jiancheng Yang, Li Zhang, Zhigang Li, Bo Du

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Springer International Publishing Ag2023

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Michele De Palma, Nahal Mansouri

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

2023

Development of a novel Chimeric Antigen Receptor (CAR) for its use in Natural Killer (NK) cells for cancer treatment and beyond

Angela Madurga Alonso

The use of CAR technologies has revolutionized cancer treatment. Their unprecedented efficacy against B cell malignancies has opened the doors for a lot of excitement and research in the field. These synthetic receptors are composed of an antigen recogniti ...

EPFL2023