AIMC Topic: Neoplasm Recurrence, Local
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Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.
Scientific reports
Jul 1, 2025
This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curative surgery in patients with hepatocellular carcinoma (HCC).A retrospective analysis was conducted on ...
Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.
Nature communications
Jul 1, 2025
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients w...
Harnessing the machine learning and nomogram models: elevating prognostication in nonmetastatic gastric cancer with "double invasion" for personalized patient care.
European journal of medical research
Jun 23, 2025
OBJECTIVE: To develop and validate a machine learning framework combined with a nomogram for predicting recurrence after radical gastrectomy in patients with vascular and neural invasion.
Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.
World journal of surgical oncology
Jun 18, 2025
BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compared to other head and neck cancers. The prognosis of NPC patients after intensity-modulated radiation therapy (IMRT) has not been fully studied, and the...
Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.
BMC medical informatics and decision making
Jun 5, 2025
BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.
Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.
Scientific reports
Jun 5, 2025
Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurrence remains a significant challenge. Traditional methods of assessing patient prognosis, such as AJCC...
Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.
Scientific reports
Jun 4, 2025
Urethral recurrence (UR) following radical cystectomy for bladder cancer represents an aggressive disease failure with typically poor survival outcomes. Our study aimed to assess the predictive risk factors for UR, to develop and validate an easy-to-...
Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.
EBioMedicine
May 28, 2025
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most ...
Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.
Scientific reports
May 6, 2025
Acute myeloid leukemia (AML) is a severe hematological malignancy characterized by high recurrence rates, especially in pediatric patients, highlighting the need for reliable prognostic markers. This study proposes methylation signatures associated w...
Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence.
JCO precision oncology
May 5, 2025
PURPOSE: Endometrial cancer (EC) is the most common gynecologic cancer in the United States with rising incidence and mortality. Despite optimal treatment, 15%-20% of all patients will recur. To better select patients for adjuvant therapy, it is impo...
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