Vamsidhar Velcheti | Case School of Engineering

Publications

Wang, X., Bera, K., Barrera, C., Zhou, Y., Lu, C., Vaidya, P., Yang, M., Schmid, R., Berezowska, S., Choi, H., Velcheti, V., & Madabhushi, A. (2021). A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer. EBioMedicine.

Alilou, M., Prasanna, P., Bera, K., Gupta, A., Rajiah, P., Yang, M., Jacono, F., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2021). A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers, 13 (11), 2781.

Lu, C., Koyuncu, C., Corredor-Prada, G., Prasanna, P., Leo, P., WAng, X., Janowczyk, A., Bera, K., Lewis, J., Velcheti, V., & Madabhushi, A. (2021). Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Medical Image Analysis, 68

WAng, X., Bera, K., Barrera, C., Zhou, Y., Lu, C., Vaidya, P., Fu, P., Yang, M., Schmid, R., Berezowska, S., Choi, H., Velcheti, V., & Madabhushi, A. (2021). A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer. EBIOMEDICINE, 69

Lu, C., Bera, K., WAng, X., Prasanna, P., Xue, Z., Janowczyk, A., Beig, N., Yang, M., Fu, P., Lewis, J., Choi, H., Schmid, R., Berezowska, S., Schalper, K., Rimm, D., Velcheti, V., & Madabhushi, A. (2020). A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet Digital Health, 2 (11), e594-e606.

Khorrami, M., Bera, K., Leo, P., Vaidya, P., Patil, P., Thawani, R., Velu, P., Rajiah, P., Alilou, M., Choi, H., Feldman, M., Gilkeson, R., Linden, P., Fu, P., Pass, H., Velcheti, V., & Madabhushi, A. (2020). Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer, 142 , 90-97.

Vaidya, P., Bera, K., Gupta, A., WAng, X., Corredor-Prada, G., Fu, P., Beig, N., Prasanna, P., Patil, P., Velu, P., Rajiah, P., Gilkeson, R., Feldman, M., Choi, H., Velcheti, V., & Madabhushi, A. (2020). CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. The Lancet Digital Health, 2 (3), e116-e128.

Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P., Thawani, R., Corredor-Prada, G., Alilou, M., Bera, K., Fu, P., Feldman, M., Velcheti, V., & Madabhushi, A. (2020). Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in NonSmall Cell Lung Cancer. Cancer Immunology Research, 8 (1), 108-119.

Vaidya, P., Bera, K., Patil, P., Gupta, A., Jain, P., Alilou, M., Khorrami, M., Velcheti, V., & Madabhushi, A. (2020). Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. Journal for immunotherapy of cancer, 8 (2).

Bera, K., Schalper, K., Rimm, D., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology � new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16 (11), 703-715.

Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P., Ahmad, U., Murthy, S., Stephans, K., Fu, P., Velcheti, V., & Madabhushi, A. (2019). Corrigendum to �Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features� [Lung Cancer 135 (September) (2019) 1�9]. Lung Cancer, 136

Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P., Ahmad, U., Murthy, S., Stephans, K., Fu, P., Velcheti, V., & Madabhushi, A. (2019). Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer, 135 , 1-9.

Prasanna, P., Khorrami, M., Gupta, A., Patil, P., Khunger, M., Velu, P., Bera, K., Alilou, M., Velcheti, V., & Madabhushi, A. (2019). Intra and perinodular CT delta radiomic features associated with early response to predict overall survival (OS) in immunotherapy-treated non-small cell lung cancer (NSCLC): A multisite multi-agent study.. Journal of Clinical Oncology, 37 (15_suppl), 2588-2588.

Corredor, G., WAng, X., Zhou, Y., Lu, C., Fu, P., Syrigos, K., Rimm, D., Yang, M., Romero, E., Schalper, K., Velcheti, V., & Madabhushi, A. (2019). Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non�Small Cell Lung Cancer. Clinical Cancer Research, 25 (5), 1526-1534.

Khorrami, M., Khunger, M., Zagouras, A., Patil, P., Thawani, R., Bera, K., Rajiah, P., Fu, P., Velcheti, V., & Madabhushi, A. (2019). Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma. Radiology: Artificial Intelligence, 1 (2).

Beig, N., Khorrami, M., Alilou, M., Prasanna, P., Braman, N., Orooji, M., Rakshit, S., Bera, K., Rajiah, P., Ginsberg, J., Donatelli, C., Thawani, R., Yang, M., Jacono, F., Tiwari, P., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2019). Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology, 290 (3), 783-792.

Alilou, M., Orooji, M., Beig, N., Prasanna, P., Rajiah, P., Donatelli, C., Velcheti, V., Rakshit, S., Yang, M., Jacono, F., Gilkeson, R., Linden, P., & Madabhushi, A. (2018). Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas. Scientific Reports, 8 (1).

Corredor, G., WAng, X., Zhou, Y., Lu, C., Fu, P., Syrigos, K., Rimm, D., Yang, M., Romero, E., Schalper, K., Velcheti, V., & Madabhushi, A. (2018). Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clinical Cancer Research.

Bera, K., Velcheti, V., & Madabhushi, A. (2018). Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications. American Society of Clinical Oncology Educational Book.

WAng, X., Barrera, C., Velu, P., Bera, K., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A. (2018). Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer.. Journal of Clinical Oncology, 36 (15_suppl), 12061-12061.

Barrera, C., Velu, P., Bera, K., WAng, X., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., Romero, E., & Madabhushi, A. (2018). Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology, 36 (15_suppl), 12115-12115.

Khorrami, M., Jain, P., Khunger, M., Ahmad, U., Stephans, K., Murthy, S., Velcheti, V., & Madabhushi, A. (2018). Combination of CT derived radiomic features and lymphovascular invasion status to predict disease recurrence following trimodality therapy in non-small cell lung cancer.. Journal of Clinical Oncology, 36 (15_suppl), e24314-e24314.

Patil, P., Bera, K., Vaidya, P., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A. (2018). Correlation of radiomic features with PD-L1 expression in early stage non-small cell lung cancer (ES-NSCLC) to predict recurrence and overall survival (OS).. Journal of Clinical Oncology, 36 (15_suppl), e24247-e24247.

Orooji, M., Alilou, M., Rakshit, S., Beig, N., Khorrami, M., Rajiah, P., Thawani, R., Ginsberg, J., Donatelli, C., Yang, M., Jacono, F., Gilkeson, R., Velcheti, V., Linden, P., & Madabhushi, A. (2018). Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. Journal of Medical Imaging, 5 (2).

Thawani, R., McLane, M., Beig, N., Ghose, S., Prasanna, P., Velcheti, V., & Madabhushi, A. (2018). Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer, 115 , 34-41.

WAng, X., Janowczyk, A., Zhou, Y., Thawani, R., Fu, P., Schalper, K., Velcheti, V., & Madabhushi, A. (2017). Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Scientific Reports, 7 (1).