Pingfu Fu

Publications

Corredor-Prada, G., Toro, P., Bera, K., Rasmussen, D., Sankar Viswanathan, V., Buzzy, C., Fu, P., Barton, L., Stroberg, E., Duval, E., Gilmore, H., Mukhopadyay, S., & Madabhushi, A. (2021). Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings. Journal of Medical Imaging, 8 (Suppl 1), 017501.
Hiremath, A., Shiradkar, R., Fu, P., Mahran, A., Rastinehad, A., Tewari, A., Tirumani, S., Purysko, A., Ponsky, L., & Madabhushi, A. (2021). An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. The Lancet Digital Health, 3 (7), E445 - E454.
Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera, K., Shiradkar, R., Farre, X., Fu, P., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T., Khani, F., Robinson, B., Eklund, L., Jambor, I., Merisaari, H., , O., Taimen, P., Aronen, H., Boström, P., Tewari, A., Magi-Galluzzi, C., Klein, E., Purysko, A., Shin, N., Feldman, M., Gupta, S., Lai, P., & Madabhushi, A. (2021). Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precision Oncology, 5 (1), 35.
Khorrami, M., Bera, K., Thawani, R., Rajiah, P., Gupta, A., Fu, P., Linden, P., Pennell, N., Jacono, F., Gilkeson, R., Velchetii, V., & Madabhushi, A. (2021). Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans. European Journal of Cancer, 148 , 146 - 158.
Leo, P., Chandramouli, S., Farre, X., Elliott, R., Janowczyk, A., Bera, K., Fu, P., Janaki, N., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T., Khani, F., Robinson, B., Shih, N., Feldman, M., Gupta, S., McKenney, J., Lai, P., & Madabhushi, A. (2021). Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. European Urology Focus.
Koyuncu, C., Lu, C., Bera, K., Zhang, Z., Xue, Z., Toro, P., Corredor-Prada, G., Chute, D., Fu, P., Thorstad, W., Faraji, F., Bishop, J., Mehrad, M., Castro, P., Sikora, A., Thompson, L., Chernock, R., Lang Kuhs, K., Luo, J., Sandulache, V., Adelstein, D., Koyfman, S., Lewis, Jr, J., & Madabhushi, A. (2021). Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma. The Journal of Clinical Investigation, 131 (8), e145488.
Koyuncu, C., Lu, C., Bera, K., Zhang, Z., Xue, Z., Toro, P., Corredor-Prada, G., Chute, D., Fu, P., Thorstad, W., & Others, W. (2021). Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma. The Journal of Clinical Investigation, 131 (8).
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
Li, H., Bera, K., Toro, P., Fu, P., Zhang, Z., Lu, C., Feldman, M., Ganesan, S., Goldstein, L., Davidson, N., & Others, N. (2021). Collagen fiber orientation disorder from H\&E images is prognostic for early stage breast cancer: clinical trial validation. npj Breast Cancer, 7 (1), 1--10.
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.
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.
Chandramouli, S., Leo, P., Lee, G., Elliott, R., Davis, C., Zhu, G., Fu, P., Epstein, J., Veltri, R., & Madabhushi, A. (2020). Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance. Cancers, 12 (9).
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.
Azarianpour Esfahani, S., Corredor-Prada, G., Bera, K., Fu, P., Joehlin-Price, A., Mahdi, H., & Madabhushi, A. (2020). Computerized features of spatial arrangement of tumor-infiltrating lymphocytes from H&E images predicts survival and response to checkpoint inhibitors in gynecologic cancers.. Journal of Clinical Oncology, 38 (15_suppl), 6074-6074.
Corredor-Prada, G., Lu, C., Koyuncu, C., Bera, K., Toro, P., Fu, P., Koyfman, S., Chute, D., Adelstein, D., Thorstad, W., Bishop, J., Faraji, F., Lewis, J., & Madabhushi, A. (2020). Computerized features of spatial interplay of tumor-infiltrating lymphocytes predict disease recurrence in p16+ oropharyngeal squamous cell carcinoma: A multisite validation study.. Journal of Clinical Oncology, 38 (15_suppl), 6559-6559.
Koyuncu, C., Corredor-Prada, G., Lu, C., Toro, P., Bera, K., Fu, P., Koyfman, S., Chute, D., Adelstein, D., Thorstad, W., Bishop, J., Faraji, F., Lewis, J., & Madabhushi, A. (2020). Combination of tumor multinucleation and spatial arrangement of tumor-infiltrating lymphocytes to predict overall survival in oropharyngeal squamous cell carcinoma: A multisite study.. Journal of Clinical Oncology, 38 (15_suppl), 6566-6566.
Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Fu, P., Yamoah, K., Khani, F., Robinson, B., Rebbeck, T., Feldman, M., Lal, P., & Madabhushi, A. (2020). Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clinical Cancer Research, 26 (8), 1915-1923.
Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Fu, P., Yamoah, K., Khani, F., Robinson, B., Rebbeck, T., Feldman, M., Lal, P., & Madabhushi, A. (2020). Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients. Clinical Cancer Research, 26 (8), 1915-1923.
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 Non–Small Cell Lung Cancer. Cancer Immunology Research, 8 (1), 108-119.
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A. (2020). T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.
Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P., Thawani, R., Corredor-Prada, G., Alilou, M., Bera, K., Fu, P., & Others, P. (2020). Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer. Cancer immunology research, 8 (1), 108.
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.
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).
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.
Corredor, Germ\'an, WAng, X., Zhou, Y., Lu, C., Fu, P., Syrigos, K., Rimm, D., Yang, M., Romero, E., Schalper, K., & Others, K. (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.
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.
Penzias, G., Singanamalli, A., Elliott, R., Gollamudi, J., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., B�hm, M., Haynes, A., Ponsky, L., Fu, P., Tiwari, P., Viswanath, S. E., & Madabhushi, A. E. (2018). Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS ONE, 13 (8).
Verma, N., Harding, D., Mohammadi, A., Goldstein, L., Gilmore, H., Feldman, M., Tomaszewski, J., Basavanhally, A., Lloyd, M., Fu, P., Ganesan, S., Davidson, N., Madabhushi, A., & Monaco, J. (2018). Image-based risk score to predict recurrence of ER+ breast cancer in ECOG-ACRIN Cancer Research Group E2197.. Journal of Clinical Oncology, 36 (15_suppl), 540-540.
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).
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).
Shankar, E., Kanwal, R., Goel, A., Yang, X., Shukla, S., MacLennan, G., Fu, P., Madabhushi, A., Ramakrishnan, P., & Gupta, S. (2017). Abstract 1080: Targeting the PI3K-Akt and NF-?B pathways as a combination therapy in blocking prostate cancer progression. Cancer Research, 77 (13 Supplement), 1080-1080.
Shankar, E., Kanwal, R., Goel, A., Yang, X., Shukla, S., MacLennan, G., Fu, P., Liu, H., Madabhushi, A., & Gupta, S. (2017). PD33-02 PROSTATE CANCER AGGRESSIVENESS IS MEDIATED BY AKT AND NF-?B SIGNALING PATHWAYS: A SYSTEMS BIOLOGY APPROACH. The Journal of Urology, 197 (4).