Lise Wei
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Author Swipe
View article: Towards quantitative ionizing radiation acoustic imaging (iRAI) for radiation dose measurement: Validation from simulations to experiments
Towards quantitative ionizing radiation acoustic imaging (iRAI) for radiation dose measurement: Validation from simulations to experiments Open
Background In clinical radiation therapy (RT), accurately quantifying the delivered radiation dose to the targeted tumors and surrounding tissues is essential for evaluating treatment outcomes. Ionizing radiation acoustic imaging (iRAI), a…
View article: PET/CT-Based Absorbed Dose Maps in<sup>90</sup>Y Selective Internal Radiation Therapy Correlate with Spatial Changes in Liver Function Derived from Dynamic MRI
PET/CT-Based Absorbed Dose Maps in<sup>90</sup>Y Selective Internal Radiation Therapy Correlate with Spatial Changes in Liver Function Derived from Dynamic MRI Open
Functional liver parenchyma can be damaged from treatment of liver malignancies with 90Y selective internal radiation therapy (SIRT). Evaluating functional parenchymal changes and developing an absorbed dose (AD)-toxicity model can assist …
View article: Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration
Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration Open
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine lea…
View article: Interpretable artificial intelligence in radiology and radiation oncology
Interpretable artificial intelligence in radiology and radiation oncology Open
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial inte…
View article: Deep learning prediction of post‐SBRT liver function changes and NTCP modeling in hepatocellular carcinoma based on DGAE‐MRI
Deep learning prediction of post‐SBRT liver function changes and NTCP modeling in hepatocellular carcinoma based on DGAE‐MRI Open
Background Stereotactic body radiation therapy (SBRT) produces excellent local control for patients with hepatocellular carcinoma (HCC). However, the risk of toxicity for normal liver tissue is still a limiting factor. Normal tissue compli…
View article: Current status and future developments in predicting outcomes in radiation oncology
Current status and future developments in predicting outcomes in radiation oncology Open
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges…
View article: Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics
Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics Open
Purpose To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90 Y PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies Methods Given the nois…
View article: Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics
Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics Open
Purpose To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90Y PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies. Methods Given the nois…
View article: Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics
Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics Open
Purpose Evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90 Y PET can be integrated to better predict outcome in microsphere radioembolization of liver malignancies. Methods Given the noisy n…
View article: Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics
Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics Open
Purpose Evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90 Y PET can be integrated to better predict outcome in microsphere radioembolization of liver malignancies. Methods Given the noisy n…
View article: Machine and deep learning methods for radiomics
Machine and deep learning methods for radiomics Open
Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning ha…
View article: Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics
Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics Open
Purpose Evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90 Y PET can be integrated to better predict outcome in microsphere radioembolization of liver malignancies. Methods Given the noisy n…
View article: Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology
Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology Open
Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. …
View article: Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling
Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling Open
Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on a…
View article: Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling
Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling Open
We developed a new method for automated and efficient detection of streak artifacts. We also showed that such streak artifacts in HNC CT images can worsen the performance of radiomics modeling.
View article: Machine Learning and Imaging Informatics in Oncology
Machine Learning and Imaging Informatics in Oncology Open
In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expand…
View article: Radiomics in precision medicine for lung cancer
Radiomics in precision medicine for lung cancer Open
With the improvement of external radiotherapy delivery accuracy, such as intensity-modulated and stereotactic body radiation therapy, radiation oncology has recently entered in the era of precision medicine. Despite these precise irradiati…