AIMC Topic: Neoplasm Staging
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Extracting lung cancer staging descriptors from pathology reports: A generative language model approach.
Journal of biomedical informatics
Sep 2, 2024
BACKGROUND: In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilizati...
Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.
Journal of vascular and interventional radiology : JVIR
Aug 28, 2024
PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical ...
Lung Neoplasms
Treatment Outcome
Predictive Value of Tests
Neoplasm Staging
Humans
Radiographic Image Interpretation, Computer-Assisted
Radiomics
Neoplasm Recurrence, Local
Machine Learning
Male
Microwaves
Decision Support Techniques
Middle Aged
Margins of Excision
Ablation Techniques
Retrospective Studies
Risk Assessment
Adenocarcinoma of Lung
Tomography, X-Ray Computed
Time Factors
Clinical Decision-Making
Female
Risk Factors
Aged
Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.
Journal of magnetic resonance imaging : JMRI
Aug 22, 2024
BACKGROUND: Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized select...
Thy-DAMP: deep artificial neural network model for prediction of thyroid cancer mortality.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Aug 22, 2024
PURPOSE: Despite the rising incidence of differentiated thyroid cancer (DTC), mortality rates have remained relatively low yet crucial for effective patient management. This study aims to develop a deep neural network capable of predicting mortality ...
Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study.
Urology
Aug 15, 2024
OBJECTIVE: To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.
Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.
BMC gastroenterology
Aug 5, 2024
BACKGROUND: This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer.
Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 1, 2024
OBJECTIVES: The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and cl...
MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.
International journal of surgery (London, England)
Aug 1, 2024
INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to...
Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.
Annals of surgery
Jul 30, 2024
OBJECTIVE: To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC).
Development and validation of a machine learning-based F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jul 30, 2024
BACKGROUND: Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-...
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