Mohammadhadi Khorrami

发表

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

Prateek Prasanna, Anant Madabhushi, Pallavi Tiwari, 2019, Radiology.

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer

A. Madabhushi, M. Feldman, V. Velcheti, 2019, Cancer Immunology Research.

Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.

A. Madabhushi, V. Velcheti, S. Murthy, 2019, Lung cancer.

Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study.

A. Madabhushi, M. Feldman, P. Rajiah, 2020, Lung cancer.

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

A. Madabhushi, V. Velcheti, P. Prasanna, 2022, Clinical cancer research : an official journal of the American Association for Cancer Research.

Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by Zheng et al

A. Madabhushi, P. Fu, N. Pennell, 2022, Journal for ImmunoTherapy of Cancer.

Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade

A. Madabhushi, V. Velcheti, M. Alilou, 2020, Journal for ImmunoTherapy of Cancer.

Novel Non-Invasive Radiomic Signature on CT Scans Predicts Response to Platinum-Based Chemotherapy and Is Prognostic of Overall Survival in Small Cell Lung Cancer

A. Madabhushi, P. Rajiah, P. Fu, 2021, Frontiers in Oncology.

A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study

Prateek Prasanna, Anant Madabhushi, Kaustav Bera, 2019, Medical Imaging.

A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors

A. Madabhushi, V. Velcheti, P. Prasanna, 2022, Science advances.

Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab

Anas M. Saad, A. Madabhushi, P. Rajiah, 2022, Journal for ImmunoTherapy of Cancer.

Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.

A. Madabhushi, P. Rajiah, V. Velcheti, 2021, European journal of cancer.

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

A. Madabhushi, P. Rajiah, V. Velcheti, 2019, Radiology. Artificial intelligence.

Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival

Anant Madabhushi, Kaustav Bera, Vamsidhar Velcheti, 2019, Medical Imaging.

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

A. Madabhushi, J. Abraham, Mohammadhadi Khorrami, 2023, NPJ breast cancer.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Mahdi Orooji, Anant Madabhushi, Vamsidhar Velcheti, 2018, Journal of medical imaging.