AIMC Topic: SEER Program
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Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.
The American journal of pathology
Apr 8, 2020
Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural n...
Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival.
World neurosurgery
Aug 8, 2018
BACKGROUND: Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learni...
Breast cancer data analysis for survivability studies and prediction.
Computer methods and programs in biomedicine
Dec 12, 2017
BACKGROUND: Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to asses...
Characterizing Autoimmune Disease-associated Diffuse Large B-cell Lymphoma in a SEER-Medicare Cohort.
Clinical lymphoma, myeloma & leukemia
Dec 2, 2017
BACKGROUND: Severe immune dysregulation such as seen in autoimmune (AI) disease is known to act as a significant risk factor for diffuse large B-cell lymphoma (DLBCL). However, little is known about the demographics or clinical outcomes of DLBCL that...
Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.
Scientific reports
Jul 2, 2025
Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific su...
Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study.
World journal of gastroenterology
Apr 7, 2025
BACKGROUND: The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic status before treatment and determine appropriate treatment strategies.
Epidemiology characteristics and clinical outcomes of composite Hodgkin lymphoma and diffuse large B-cell lymphoma using machine learning.
The oncologist
Apr 4, 2025
Composite lymphoma (CL) is rare. We conducted an analysis of 53 329 cases of diffuse large B-cell lymphoma (DLBCL), 17,916 cases of Hodgkin lymphoma (HL), and 869 cases of composite HL and DLBCL from the SEER database diagnosed between 2000 and 2019....
Determining health care cost drivers in older Hodgkin lymphoma survivors using interpretable machine learning methods.
Journal of managed care & specialty pharmacy
Apr 1, 2025
BACKGROUND: The cost of health care for patients with Hodgkin lymphoma (HL) is projected to rise, making it essential to understand expenditure drivers across different demographics, including the older adult population. Although older HL patients co...
Model development and validation for predicting small-cell lung cancer bone metastasis utilizing diverse machine learning algorithms based on the SEER database.
Medicine
Mar 21, 2025
The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cancer (SCLC) and create a straightforward web-based predictor based on the developed algorithm. Data com...
Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER) database.
Journal of orthopaedic surgery (Hong Kong)
Jan 1, 2025
The chemotherapy benefit for high-grade chondrosarcoma remains controversial. Ensemble learning has better overall performance than single computational approaches for clinical decision. The primary objective was to select prognostic variables and d...
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