AIMC Topic: Prognosis

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Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis.

Japanese journal of clinical oncology
BACKGROUND: The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide i...

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Journal of cellular and molecular medicine
Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to current therapies. The tumour microenvironment, especially the role of M2 macrophages, is pivotal in th...

Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

JCO clinical cancer informatics
PURPOSE: Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentat...

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Clinical and translational science
Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect imp...

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

The journal of gene medicine
OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for ...

External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.

Radiology. Artificial intelligence
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radio...

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.

The British journal of radiology
The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable...

Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs.

Studies in health technology and informatics
INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15-18 months. Therefore, it is helpful to be able to assess a patient's OS to tailor treatment more s...

Visualizing Future Breast Cancer Prognosis by Generative AI.

Studies in health technology and informatics
In Sweden, 30 percent of breast cancer cases are detected between screenings, leading to later staged cancer diagnoses. Aileen Health is preventing later staged cancers by making a breast cancer prognosis with generative AI. This study investigates h...