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Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.
In Frontiers in oncology
BACKGROUND :
METHODS :
RESULTS :
CONCLUSIONS :
Weissmann Thomas, Huang Yixing, Fischer Stefan, Roesch Johannes, Mansoorian Sina, Ayala Gaona Horacio, Gostian Antoniu-Oreste, Hecht Markus, Lettmaier Sebastian, Deloch Lisa, Frey Benjamin, Gaipl Udo S, Distel Luitpold Valentin, Maier Andreas, Iro Heinrich, Semrau Sabine, Bert Christoph, Fietkau Rainer, Putz Florian
2023
artificial intelligence, autosegmentation, deep learning, head and neck, lymph node level, neural network, radiotherapy, target volume
Applications of different machine learning approaches in prediction of breast cancer diagnosis delay.
In Frontiers in oncology
BACKGROUND :
OBJECTIVES :
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RESULTS :
CONCLUSION :
Dehdar Samira, Salimifard Khodakaram, Mohammadi Reza, Marzban Maryam, Saadatmand Sara, Fararouei Mohammad, Dianati-Nasab Mostafa
2023
breast cancer (BC), delay, extreme gradient boosting, logistic regression, machine learning, neural networks (NN), random forest (RF)

Molecular features and predictive models identify the most lethal subtype and a therapeutic target for osteosarcoma.
In Frontiers in oncology
BACKGROUND :
METHODS :
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CONCLUSION :
Zheng Kun, Hou Yushan, Zhang Yiming, Wang Fei, Sun Aihua, Yang Dong
2023
SQLE, cholesterol metabolism, drug target, molecular classification, osteosarcoma, predictive model
Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis.
In Frontiers in oncology
OBJECTIVE :
METHODS :
RESULTS :
CONCLUSION :
Xiong Ming, Xu Yaona, Zhao Yang, He Si, Zhu Qihan, Wu Yi, Hu Xiaofei, Liu Li
2023
Citespace, VOSviewer, artificial intelligence, bibliometrics, liver cancer
Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.
In Frontiers in oncology
BACKGROUND AND OBJECTIVES :
PATIENTS AND METHODS :
RESULTS :
CONCLUSIONS :
Barkan Ella, Porta Camillo, Rabinovici-Cohen Simona, Tibollo Valentina, Quaglini Silvana, Rizzo Mimma
2023
artificial intelligence, first-line treatment, machine learning, metastatic renal cell carcinoma, overall survival, predictive model

Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome.
In Patterns (New York, N.Y.)
Xu Haodong, Jia Johnathan, Jeong Hyun-Hwan, Zhao Zhongming
2023-Feb-10
HTLV-1, T cell leukemia/lymphoma, deep learning, motif, viral integration sites
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