Cargando…

Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models

PURPOSE: The purpose of this study was to develop computational algorithms to best determine tumor responses early after the start of neoadjuvant chemotherapy, based on quantitative ultrasound (QUS) and textural analysis in patients with locally advanced breast cancer (LABC). METHODS: A total of 100...

Descripción completa

Detalles Bibliográficos
Autores principales:	Sannachi, Lakshmanan, Gangeh, Mehrdad, Tadayyon, Hadi, Gandhi, Sonal, Wright, Frances C., Slodkowska, Elzbieta, Curpen, Belinda, Sadeghi-Naini, Ali, Tran, William, Czarnota, Gregory J.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Neoplasia Press 2019
Materias:	Original article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6639683/ https://www.ncbi.nlm.nih.gov/pubmed/31325763 http://dx.doi.org/10.1016/j.tranon.2019.06.004

Ejemplares similares

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features
por: Sannachi, Lakshmanan, et al.
Publicado: (2018)

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis
por: Tran, William T, et al.
Publicado: (2017)

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound
por: Tadayyon, Hadi, et al.
Publicado: (2017)

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities
por: Sadeghi-Naini, Ali, et al.
Publicado: (2017)

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach
por: Tadayyon, Hadi, et al.
Publicado: (2016)

Correction: Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach
por: Tadayyon, Hadi, et al.
Publicado: (2017)

A priori prediction of breast tumour response to chemotherapy using quantitative ultrasound imaging and artificial neural networks
por: Tadayyon, Hadi, et al.
Publicado: (2019)

Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy
por: Tran, William T., et al.
Publicado: (2016)

Noninvasive Characterization of Locally Advanced Breast Cancer Using Textural Analysis of Quantitative Ultrasound Parametric Images
por: Tadayyon, Hadi, et al.
Publicado: (2014)

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography
por: Fernandes, Jason, et al.
Publicado: (2019)

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer
por: Dasgupta, Archya, et al.
Publicado: (2020)

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning
por: Moghadas-Dastjerdi, Hadi, et al.
Publicado: (2021)

Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture
por: Sadeghi-Naini, Ali, et al.
Publicado: (2014)

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
por: Kolios, Christopher, et al.
Publicado: (2021)

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results
por: Quiaoit, Karina, et al.
Publicado: (2020)

Quantification of Ultrasonic Scattering Properties of In Vivo Tumor Cell Death in Mouse Models of Breast Cancer()
por: Tadayyon, Hadi, et al.
Publicado: (2015)

Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study
por: DiCenzo, Daniel, et al.
Publicado: (2020)

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps
por: Sadeghi-Naini, Ali, et al.
Publicado: (2017)

Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound
por: Dasgupta, Archya, et al.
Publicado: (2021)

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer
por: Tran, William T, et al.
Publicado: (2019)

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer
por: Bhardwaj, Divya, et al.
Publicado: (2022)

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies
por: Tran, William T, et al.
Publicado: (2020)

A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods
por: Osapoetra, Laurentius O., et al.
Publicado: (2021)

Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features
por: Safakish, Aryan, et al.
Publicado: (2023)

Quantitative ultrasound imaging of therapy response in bladder cancer in vivo
por: Tran, William T., et al.
Publicado: (2016)

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning
por: Moghadas-Dastjerdi, Hadi, et al.
Publicado: (2020)

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods
por: Osapoetra, Laurentius O., et al.
Publicado: (2020)

Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound
por: Sadeghi-Naini, Ali, et al.
Publicado: (2015)

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
por: Lagree, Andrew, et al.
Publicado: (2021)

Quantitative Ultrasound for Evaluation of Tumour Response to Ultrasound-Microbubbles and Hyperthermia
por: Sharma, Deepa, et al.
Publicado: (2023)

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment
por: Taleghamar, Hamidreza, et al.
Publicado: (2021)

MR-guided ultrasound-stimulated microbubble therapy enhances radiation-induced tumor response
por: McNabb, Evan, et al.
Publicado: (2023)

Tumour Vascular Shutdown and Cell Death Following Ultrasound-Microbubble Enhanced Radiation Therapy
por: El Kaffas, Ahmed, et al.
Publicado: (2018)

Novel MRI-guided focussed ultrasound stimulated microbubble radiation enhancement treatment for breast cancer
por: Dasgupta, Archya, et al.
Publicado: (2023)

Focused Ultrasound Stimulation of Microbubbles in Combination With Radiotherapy for Acute Damage of Breast Cancer Xenograft Model
por: Sharma, Deepa, et al.
Publicado: (2022)

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade
por: Lagree, Andrew, et al.
Publicado: (2021)

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions
por: Osapoetra, Laurentius O., et al.
Publicado: (2020)

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy
por: Taleghamar, Hamidreza, et al.
Publicado: (2022)

Quantitative ultrasound radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma treated with radical radiotherapy
por: Dasgupta, Archya, et al.
Publicado: (2020)

Ultrasound Elastography of the Prostate Using an Unconstrained Modulus Reconstruction Technique: A Pilot Clinical Study
por: Mousavi, Seyed Reza, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_4q1g4q9g22mvu1hkfmcs5273ld