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Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Breast cancer research : BCR, 2017-05, Vol.19 (1), p.57-57, Article 57

2017

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Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas

Radiology, 2019-03, Vol.290 (3), p.783

2019

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Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis

Frontiers in oncology, 2021-09, Vol.11, p.744250-744250

2021

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Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery: First MICCAI Workshop, AIIIMA 2022, and First MICCAI Workshop, MIABID 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

1st Edition 2022, 2022

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Erratum to: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Breast cancer research : BCR, 2017-07, Vol.19 (1), p.80-80, Article 80

2017

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Predicting cancer outcomes with radiomics and artificial intelligence in radiology

Nature reviews. Clinical oncology, 2022-02, Vol.19 (2), p.132-146

2022

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Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer

NPJ breast cancer, 2023-08, Vol.9 (1), p.67-67, Article 67

2023

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Radiomics For Surgical Planning and Prognostication

JAMA network open, 2020-12, Vol.3 (12), p.e2028608-e2028608

Braman, Nathaniel

2020

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NIMG-27. RADIOGENOMIC ANALYSIS OF GLIOBLASTOMA REVEALS TEXTURAL FEATURES FROM MRI THAT CORRELATE WITH GENOMIC IMMUNE SCORE AND ARE ALSO PREDICTIVE OF CHEMO-RADIATION TREATMENT RESPONSE

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi181-vi181

2018

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Association of single-click radiomic classifier with response and prognosis in non-small cell lung cancers (NSCLC) treated with immune checkpoint inhibitors

Journal of clinical oncology, 2023-06, Vol.41 (16_suppl), p.8574-8574

2023

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Abstract P5-13-27: Post-treatment vascularity and vessel shape are associated with survival and response to CDK4/6 inhibitors in hormone receptor-positive metastatic breast cancer (MBC)

Cancer research (Chicago, Ill.), 2022-02, Vol.82 (4_Supplement), p.P5-P5-13-27

2022

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Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma

Clinical cancer research, 2020-04, Vol.26 (8), p.1866-1876

2020

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Abstract PS5-42: Change in intra-lesion heterogeneity on CT predicts long-term survival following treatment with CDK4/6 inhibitors in hormone receptor-positive metastatic breast cancer (MBC)

Cancer research (Chicago, Ill.), 2021-02, Vol.81 (4_Supplement), p.PS5-42-PS5-42

2021

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Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status

Journal of clinical oncology, 2018-05, Vol.36 (15_suppl), p.582-582

2018

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Radiomics risk score (RRS) on CT to predict survival and response to CDK 4/6 inhibitors in hormone receptor (HR) positive metastatic breast cancer (MBC)

Journal of clinical oncology, 2020-05, Vol.38 (15_suppl), p.e13041-e13041

2020

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Abstract PD9-03: A combination of intra- and peri-lesional deep learning classifiers from multiple views enables accurate diagnosis of architectural distortion malignancy with digital breast tomosynthesis

Cancer research (Chicago, Ill.), 2020-02, Vol.80 (4_Supplement), p.PD9-03-PD9-03

2020

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Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

Clinical cancer research, 2022-10, Vol.28 (20), p.4410-4424

2022

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Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer

JAMA network open, 2019-04, Vol.2 (4), p.e192561-e192561

2019

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Abstract P1-10-06: Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Cancer research (Chicago, Ill.), 2020-02, Vol.80 (4_Supplement), p.P1-P1-10-06

2020

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Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy

Braman, Nathaniel

2020

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ProQuest

Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study

Cancer research (Chicago, Ill.), 2020-02, Vol.80 (4_Supplement), p.P4-P4-10-13

2020

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Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI

Journal of clinical oncology, 2019-05, Vol.37 (15_suppl), p.593-593

2019

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Computerized textural analysis of DCE-MRI to enable identification of HER2-enriched breast cancers

Journal of clinical oncology, 2016-05, Vol.34 (15_suppl), p.598-598

2016

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Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, p.803-811

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Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas

Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis

Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery: First MICCAI Workshop, AIIIMA 2022, and First MICCAI Workshop, MIABID 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings

Erratum to: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of Action

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer

Radiomics For Surgical Planning and Prognostication

NIMG-27. RADIOGENOMIC ANALYSIS OF GLIOBLASTOMA REVEALS TEXTURAL FEATURES FROM MRI THAT CORRELATE WITH GENOMIC IMMUNE SCORE AND ARE ALSO PREDICTIVE OF CHEMO-RADIATION TREATMENT RESPONSE

Association of single-click radiomic classifier with response and prognosis in non-small cell lung cancers (NSCLC) treated with immune checkpoint inhibitors

Abstract P5-13-27: Post-treatment vascularity and vessel shape are associated with survival and response to CDK4/6 inhibitors in hormone receptor-positive metastatic breast cancer (MBC)

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma

Abstract PS5-42: Change in intra-lesion heterogeneity on CT predicts long-term survival following treatment with CDK4/6 inhibitors in hormone receptor-positive metastatic breast cancer (MBC)

Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status

Radiomics risk score (RRS) on CT to predict survival and response to CDK 4/6 inhibitors in hormone receptor (HR) positive metastatic breast cancer (MBC)

Abstract PD9-03: A combination of intra- and peri-lesional deep learning classifiers from multiple views enables accurate diagnosis of architectural distortion malignancy with digital breast tomosynthesis

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer

Abstract P1-10-06: Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy

Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study

Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI

Computerized textural analysis of DCE-MRI to enable identification of HER2-enriched breast cancers

Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response

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