AIMC Topic: Lung Neoplasms
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Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.
Frontiers in immunology
Sep 13, 2024
INTRODUCTION: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies...
Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.
Biomedical physics & engineering express
Sep 11, 2024
. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosi...
Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.
PloS one
Sep 10, 2024
BACKGROUND: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolution...
Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 9, 2024
PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.
Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma.
Expert review of anticancer therapy
Sep 9, 2024
BACKGROUND: The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a precise prognostic model is vital to assist clinicians in patient counseling and creating effective t...
Development of a prognostic model for NSCLC based on differential genes in tumour stem cells.
Scientific reports
Sep 9, 2024
Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effec...
Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and fir...
Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.
Scientific reports
Sep 4, 2024
Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carb...
PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.
Physical and engineering sciences in medicine
Sep 3, 2024
The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for p...
Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sep 3, 2024
BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring.
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