Center for Computational Imaging and Personalized Diagnostics Publications

Wednesday, May 23, 2018

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

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, (38), 1008-1018.

Sunday, May 20, 2018

Combination of CT derived radiomic features and lymphovascular invasion status to predict disease recurrence following trimodality therapy in non-small cell lung cancer.

Khorrami, M., Jain, P., Khunger, M., Ahmad, U., Stephans, K., Murthy, S., ... 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.

Sunday, May 20, 2018

Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer.

WAng, X., Barrera, C., Velu, P., Bera, K., Prasanna, P., Khunger, M., ... 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.

Sunday, May 20, 2018

Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC).

Barrera, C., Velu, P., Bera, K., WAng, X., Prasanna, P., Khunger, M., ... 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.

Sunday, May 20, 2018

Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence.

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., ... Madabhushi, A. (2018). Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence.. Journal of Clinical Oncology, 36 (15_suppl), 12075-12075.

Sunday, May 20, 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).

Patil, P., Bera, K., Vaidya, P., Prasanna, P., Khunger, M., Khunger, A., ... 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.

Sunday, May 20, 2018

Image-based risk score to predict recurrence of ER+ breast cancer in ECOG-ACRIN Cancer Research Group E2197.

Verma, N., Harding, D., Mohammadi, A., Goldstein, L., Gilmore, H., Feldman, M., ... 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.

Sunday, May 20, 2018

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

Braman, N., Ravichandran, K., Janowczyk, A., Abraham, J., & Madabhushi, A. (2018). Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status.. Journal of Clinical Oncology, 36 (15_suppl), 582-582.

Monday, May 7, 2018

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings: Prostate Cancer Recurrence Prediction

Shiradkar, R., Ghose, S., Jambor, I., Taimen, P., Ettala, O., Purysko, A., ... Madabhushi, A. (2018). Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings: Prostate Cancer Recurrence Prediction. Journal of Magnetic Resonance Imaging, ().

Friday, May 4, 2018

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images

Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2018). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 6 (3), 270-276.

Tuesday, May 1, 2018

857 - Machine Learning Analysis of the Whole Rectal Wall on Post-Neoadjuvant Chemoradiation MRI may offer Accurate Identifiction of Rectal Cancer Patients Needing more Aggressive Follow-Up or Surgery

Antunes, J., Selvam, A., Bera, K., Brady, J., Willis, J., Paspulati, R., ... Viswanath, S. E. (2018). 857 - Machine Learning Analysis of the Whole Rectal Wall on Post-Neoadjuvant Chemoradiation MRI may offer Accurate Identifiction of Rectal Cancer Patients Needing more Aggressive Follow-Up or Surgery. Gastroenterology, 154 (6).

Wednesday, April 18, 2018

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

Orooji, M., Alilou, M., Rakshit, S., Beig, N., Khorrami, M., Rajiah, 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).

Tuesday, April 3, 2018

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

Nirschl, J., Janowczyk, A., Peyster, E., Frank, R., Margulies, K., Feldman, M., ... Madabhushi, A. (2018). A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS ONE, 13 (4).

Sunday, April 1, 2018

MP08-16 COMBINATION OF NUCLEAR ORIENTATION AND SHAPE FEATURES IN H&E STAINED IMAGES DISTINGUISH CONSENSUS LOW AND HIGH GRADE BLADDER CANCER

Li, H., Leo, P., Nezami, B., Akgul, M., Elliott, R., Harper, H., ... Madabhushi, A. (2018). MP08-16 COMBINATION OF NUCLEAR ORIENTATION AND SHAPE FEATURES IN H&E STAINED IMAGES DISTINGUISH CONSENSUS LOW AND HIGH GRADE BLADDER CANCER. The Journal of Urology, 199 (4).

Sunday, April 1, 2018

MP12-17 COMPUTER EXTRACTED FEATURES OF NUCLEI SHAPE, ARCHITECTURE AND ORIENTATION FROM INITIAL H&E TISSUE BIOPSIES PREDICT DISEASE PROGRESSION FOR PROSTATE CANCER PATIENTS ON ACTIVE SURVEILLANCE

Chandramouli, S., Leo, P., Lee, G., Elliott, R., Zhu, G., Veltri, R., ... Madabhushi, A. (2018). MP12-17 COMPUTER EXTRACTED FEATURES OF NUCLEI SHAPE, ARCHITECTURE AND ORIENTATION FROM INITIAL H&E TISSUE BIOPSIES PREDICT DISEASE PROGRESSION FOR PROSTATE CANCER PATIENTS ON ACTIVE SURVEILLANCE. The Journal of Urology, 199 (4), e142-e143.

Sunday, April 1, 2018

MP35-01 PROSTATE TUMOR TEXTURAL HETEROGENEITY OF 11 C-ACETATE POSITRON EMISSION TOMOGRAPHY AND T2-WEIGHTED MAGNETIC RESONANCE IMAGING CORRELATE WITH BIOCHEMICAL RECURRENCE: PRELIMINARY FINDINGS

Li, L., Jambor, I., Taimen, P., Merisaari, H., Minn, H., Boström, P., ... Madabhushi, A. (2018). MP35-01 PROSTATE TUMOR TEXTURAL HETEROGENEITY OF 11 C-ACETATE POSITRON EMISSION TOMOGRAPHY AND T2-WEIGHTED MAGNETIC RESONANCE IMAGING CORRELATE WITH BIOCHEMICAL RECURRENCE: PRELIMINARY FINDINGS. The Journal of Urology, 199 (4).

Sunday, April 1, 2018

MP35-02 COMPUTER-EXTRACTED FEATURES OF NUCLEAR AND GLANDULAR MORPHOLOGY FROM DIGITAL H&E TISSUE IMAGES PREDICT PROSTATE CANCER BIOCHEMICAL RECURRENCE AND METASTASIS FOLLOWING RADICAL PROSTATECTOMY

Leo, P., Gawlik, A., Zhu, G., Feldman, M., Gupta, S., Veltri, R., ... Madabhushi, A. (2018). MP35-02 COMPUTER-EXTRACTED FEATURES OF NUCLEAR AND GLANDULAR MORPHOLOGY FROM DIGITAL H&E TISSUE IMAGES PREDICT PROSTATE CANCER BIOCHEMICAL RECURRENCE AND METASTASIS FOLLOWING RADICAL PROSTATECTOMY. The Journal of Urology, 199 (4), e446-e447.

Sunday, April 1, 2018

MP35-09 COMBINATION OF NF-?B/P65 NUCLEAR LOCALIZATION AND GLAND MORPHOLOGIC FEATURES IS PREDICTIVE OF BIOCHEMICAL RECURRENCE

Leo, P., Shankar, E., Elliott, R., Janowczyk, A., Janaki, N., MacLennan, G., ... Gupta, S. (2018). MP35-09 COMBINATION OF NF-?B/P65 NUCLEAR LOCALIZATION AND GLAND MORPHOLOGIC FEATURES IS PREDICTIVE OF BIOCHEMICAL RECURRENCE. The Journal of Urology, 199 (4).

Sunday, April 1, 2018

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H and e tissue

Tuesday, March 6, 2018

A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images

Romero Castro, E., Corredor, G., Lu, C., Madabhushi, A., WAng, X., & Velcheti, V. (2018). A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images. Medical Imaging: Digital Pathology, ().

Tuesday, March 6, 2018

Combination of nuclear NF-kB/p65 localization and gland morphological features from surgical specimens appears to be predictive of early biochemical recurrence in prostate cancer patients

Janowczyk, A., Shankar, E., Leo, P., Madabhushi, A., Elliott, R., & Gupta, S. (2018). Combination of nuclear NF-kB/p65 localization and gland morphological features from surgical specimens appears to be predictive of early biochemical recurrence in prostate cancer patients. Medical Imaging: Digital Pathology, ().

Tuesday, March 6, 2018

RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer

WAng, X., Velcheti, V., Vaidya, P., Bera, K., Madabhushi, A., Khunger, A., ... Choi, H. (2018). RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer. Medical Imaging: Digital Pathology, ().

Thursday, March 1, 2018

Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection

Peyster, E., Madabhushi, A., & Margulies, K. (2018). Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Transplantation, ().

Tuesday, February 27, 2018

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

Ravichandran, K., Braman, N., Janowczyk, A., & Madabhushi, A. (2018). A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI. Medical Imaging: Computer-Aided Diagnosis, ().

Tuesday, February 27, 2018

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

Chirra, P., Bloch, N., Rastinehead, A., Purysko, A., Madabhushi, A., Viswanath, S., ... Rosen, M. (2018). Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI. Medical Imaging: Computer-Aided Diagnosis, ().

Case School of Engineering

Center for Computational Imaging and Personalized Diagnostics

Center for Computational Imaging and Personalized Diagnostics Publications

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

Combination of CT derived radiomic features and lymphovascular invasion status to predict disease recurrence following trimodality therapy in non-small cell lung cancer.

Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer.

Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC).

Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence.

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).

Image-based risk score to predict recurrence of ER+ breast cancer in ECOG-ACRIN Cancer Research Group E2197.

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

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings: Prostate Cancer Recurrence Prediction

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images

857 - Machine Learning Analysis of the Whole Rectal Wall on Post-Neoadjuvant Chemoradiation MRI may offer Accurate Identifiction of Rectal Cancer Patients Needing more Aggressive Follow-Up or Surgery

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

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

MP08-16 COMBINATION OF NUCLEAR ORIENTATION AND SHAPE FEATURES IN H&E STAINED IMAGES DISTINGUISH CONSENSUS LOW AND HIGH GRADE BLADDER CANCER

MP12-17 COMPUTER EXTRACTED FEATURES OF NUCLEI SHAPE, ARCHITECTURE AND ORIENTATION FROM INITIAL H&E TISSUE BIOPSIES PREDICT DISEASE PROGRESSION FOR PROSTATE CANCER PATIENTS ON ACTIVE SURVEILLANCE

MP35-01 PROSTATE TUMOR TEXTURAL HETEROGENEITY OF 11 C-ACETATE POSITRON EMISSION TOMOGRAPHY AND T2-WEIGHTED MAGNETIC RESONANCE IMAGING CORRELATE WITH BIOCHEMICAL RECURRENCE: PRELIMINARY FINDINGS

MP35-02 COMPUTER-EXTRACTED FEATURES OF NUCLEAR AND GLANDULAR MORPHOLOGY FROM DIGITAL H&E TISSUE IMAGES PREDICT PROSTATE CANCER BIOCHEMICAL RECURRENCE AND METASTASIS FOLLOWING RADICAL PROSTATECTOMY

MP35-09 COMBINATION OF NF-?B/P65 NUCLEAR LOCALIZATION AND GLAND MORPHOLOGIC FEATURES IS PREDICTIVE OF BIOCHEMICAL RECURRENCE

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H and e tissue

A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images

Combination of nuclear NF-kB/p65 localization and gland morphological features from surgical specimens appears to be predictive of early biochemical recurrence in prostate cancer patients

RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer

Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

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