AIMC Topic: Aged
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Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer.
JAMA network open
May 1, 2025
IMPORTANCE: Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging characteristics of bot...
Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: Nonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mortality. To date, no prognostic model has been developed to predict the metastatic probability for nm...
Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2025
Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared with conventional multiple-time-point image-based dosimetry. Despite numerous advancements, current ST...
A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting respo...
The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2025
This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep learning (DL) in [F]FDG and [Ga]Ga-prostate-specific membrane antigen (PSMA) PET/CT scans. We used 489 ...
Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology.
The neurologist
May 1, 2025
OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety ...
Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-fr...
Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.
Journal of thoracic imaging
May 1, 2025
PURPOSE: Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.
Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.
Journal of thoracic imaging
May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.
Journal of thoracic imaging
May 1, 2025
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).
Male
Middle Aged
Lung
Deep Learning
Aged, 80 and over
Adult
Sensitivity and Specificity
Cross-Sectional Studies
Radiation Dosage
Radiography, Thoracic
Lung Neoplasms
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Radiographic Image Interpretation, Computer-Assisted
Humans
Tomography, X-Ray Computed
Female
Aged
Algorithms
Prospective Studies
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