AIMC Topic: Prognosis

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Multi-modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease.

Diabetes, obesity & metabolism
BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study aims to develop classification and prognostic models based on multi-modal data.

Impact of Artificial Intelligence on the Timing of Recurrent Laryngeal Nerve Recognition during Robot-Assisted Minimally Invasive Esophagectomy.

Annals of surgical oncology
BACKGROUND: As a first step to prevent recurrent laryngeal nerve (RLN) palsy, we have developed an artificial intelligence (AI)-based anatomical recognition system for critical anatomical structures in robot-assisted minimally invasive esophagectomy ...

Prognosis and prognostic factors for chronic fibrosing idiopathic interstitial pneumonias.

Respiratory investigation
Progressive lung fibrosis is frequently observed in patients with idiopathic interstitial pneumonias (IIPs), especially in those with idiopathic pulmonary fibrosis (IPF) being a representative form of IIPs characterized by a poor prognosis, even in t...

Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.

Journal of neuro-oncology
INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learning (ML) algorithms to predict the probability of local failure (LF).

Current concepts in the pathogenesis and clinical management of lymphangioleiomyomatosis.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: Lymphangioleiomyomatosis (LAM) is a systemic, low-grade, metastasizing neoplasm that predominantly affects women. This review demonstrates recent progression in this rare disease, from improved understanding of pathogenesis, to nov...

Cancer in a drop: Advances in liquid biopsy in 2024.

Critical reviews in oncology/hematology
Over the past decade, liquid biopsy (LB) has emerged as a key tool in oncology. Its utility in non-invasive sampling and real-time monitoring has made it a cornerstone in precision medicine. Since 2020, publications on LB in solid tumors have doubled...

Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker Signatures.

Clinical pharmacology and therapeutics
In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We propose a virtual trial framework for chronic Hepatitis B, accurately simulating clinical protocols, pat...

ASO Author Reflections: Clinical-Radiomic Machine Learning Model Predicts Pheochromocytomas and Paragangliomas Surgical Difficulty: A Retrospective Study.

Annals of surgical oncology
This study developed a machine learning (ML) model combining clinical and radiomic features to predict surgical difficulty in pheochromocytomas and paragangliomas (PPGLs), aiming to optimize preoperative planning and reduce perioperative complication...

Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application.

Critical reviews in oncology/hematology
INTRODUCTION: Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors und...

Multimodal MRI radiomics enhances epilepsy prediction in pediatric low-grade glioma patients.

Journal of neuro-oncology
BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based ...