Center for Computational Imaging and Personalized Diagnostics Publications

Monday, June 1, 2020

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

Merisaari, H., Taimen, P., Shiradkar, R., Ettala, O., Pesola, M., Saunavaara, J., ... Jambor, I. (2020). Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magnetic Resonance in Medicine, 83 (6), 2293-2309.

Wednesday, May 20, 2020

Combination of tumor multinucleation and spatial arrangement of tumor-infiltrating lymphocytes to predict overall survival in oropharyngeal squamous cell carcinoma: A multisite study.

Koyuncu, C., Corredor-Prada, G., Lu, C., Toro, P., Bera, K., Fu, P., ... 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.

Wednesday, May 20, 2020

Computerized features of spatial arrangement of tumor-infiltrating lymphocytes from H&E images predicts survival and response to checkpoint inhibitors in gynecologic cancers.

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.

Wednesday, May 20, 2020

Computerized features of spatial interplay of tumor-infiltrating lymphocytes predict disease recurrence in p16+ oropharyngeal squamous cell carcinoma: A multisite validation study.

Corredor-Prada, G., Lu, C., Koyuncu, C., Bera, K., Toro, P., Fu, P., ... 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.

Wednesday, May 20, 2020

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).

Kunte, S., Braman, N., Bera, K., Leo, P., Abraham, J., Montero, A., ... Madabhushi, A. (2020). 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, 38 (15_suppl), e13041-e13041.

Wednesday, April 15, 2020

Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., ... 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.

Wednesday, April 15, 2020

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

Beig, N., Bera, K., Prasanna, P., Antunes, J., Correa, R., Singh, S., ... Tiwari, P. (2020). Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clinical Cancer Research, 26 (8), 1866-1876.

Wednesday, April 15, 2020

Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., ... 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.

Wednesday, April 1, 2020

MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS

Shiradkar, R., Mahran, A., Sharma, S., Conroy, B., Tirumani, S., Ponsky, L., ... Madabhushi, A. (2020). MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS. The Journal of Urology, 203 ().

Wednesday, April 1, 2020

PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY

Leo, P., Elliott, R., Janowczyk, A., Janaki, N., Bera, K., Shiradkar, R., ... Madabhushi, A. (2020). PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY. The Journal of Urology, 203 (), e1089-e1090.

Wednesday, April 1, 2020

PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY

Hiremath, A., Shiradkar, R., Merisaari, H., Li, L., Prasanna, P., Ettala, O., ... Madabhushi, A. (2020). PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY. The Journal of Urology, 203 ().

Wednesday, April 1, 2020

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

Khorrami, M., Bera, K., Leo, P., Vaidya, P., Patil, P., Thawani, R., ... 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.

Monday, March 16, 2020

A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer

Hiremath, A., Shiradkar, R., Braman, N., Prasanna, P., Rastinehad, A., Purysko, A., ... Madabhushi, A. (2020). A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer. Medical Imaging: Computer-Aided Diagnosis, ().

Monday, March 16, 2020

Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores

Ding, R., Prasanna, P., Corredor-Prada, G., Lu, C., Velu, P., Le, K., ... Madabhushi, A. (2020). Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores. Medical Imaging: Digital Pathology, ().

Monday, March 16, 2020

Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Arabyarmohammadi, S., Zhang, Z., Leo, P., Firouznia, M., Janowczyk, A., Li, H., ... Madabhushi, A. (2020). Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation. Medical Imaging: Digital Pathology, ().

Monday, March 16, 2020

Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer

Azarianpour Esfahani, S., Corredor-Prada, G., Bera, K., Leo, P., Braman, N., Fu, P., ... Madabhushi, A. (2020). Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer. Medical Imaging: Digital Pathology, ().

Monday, March 16, 2020

Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers

Selvam, A., Antunes, J., Bera, K., Ofshteyn, A., Brady, J., Bingmer, K., ... Viswanath, S. (2020). Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers. Medical Imaging: Computer-Aided Diagnosis, ().

Monday, March 16, 2020

Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results

Shiradkar, R., Zuo, R., Mahran, A., Ponsky, L., Tirumani, S., & Madabhushi, A. (2020). Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results. Medical Imaging: Computer-Aided Diagnosis, ().

Sunday, March 1, 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

Vaidya, P., Bera, K., Gupta, A., WAng, X., Corredor-Prada, G., Fu, P., ... 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.

Sunday, March 1, 2020

Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting

Sandulache, V., Lei, Y., Heasley, L., Chang, M., Amos, C., Sturgis, E., ... Sikora, A. (2020). Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting. Oral Oncology, 102 ().

Saturday, February 15, 2020

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

Braman, N., Adoui, M., Vulchi, M., Turk, P., Etesami, M., Fu, P., ... Madabhushi, A. (2020). 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. San Antonio Breast Cancer Symposium; San Antonio, Texas, (), P4-10-13-P4-10-13.

Saturday, February 15, 2020

Abstract P5-06-15: Computer extracted features of nuclear shape, orientation disorder and texture from H&E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS)

Li, H., Bera, K., Gilmore, H., Zhang, Z., Cuzick, J., Thorat, M., ... Madabhushi, A. (2020). Abstract P5-06-15: Computer extracted features of nuclear shape, orientation disorder and texture from H&E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS). San Antonio Breast Cancer Symposium; San Antonio, Texas, (), P5-06-15-P5-06-15.

Saturday, February 15, 2020

Abstract P5-06-16: Histomorphometric measure of disorder of collagen fiber orientation is associated with risk of recurrence in ER+ breast cancers in ECOG-ACRIN E2197 and TCGA-BRCA

Li, H., Bera, K., Gilmore, H., Davidson, N., Goldstein, L., & Madabhushi, A. (2020). Abstract P5-06-16: Histomorphometric measure of disorder of collagen fiber orientation is associated with risk of recurrence in ER+ breast cancers in ECOG-ACRIN E2197 and TCGA-BRCA. San Antonio Breast Cancer Symposium; San Antonio, Texas, (), P5-06-16-P5-06-16.

Saturday, February 1, 2020

Deep-learning approaches for Gleason grading of prostate biopsies

Madabhushi, A., Feldman, M., & Leo, P. (2020). Deep-learning approaches for Gleason grading of prostate biopsies. Lancet Oncology, 21 (2), 187-189.

Wednesday, January 1, 2020

Computerized Nuclear Morphology Features Aid in Diagnosis of Cell Clusters on Digitized Bile Duct Brushing Slides

Monabbati, S., Lu, C., Leo, P., Bera, K., Madabhushi, A., Harbhajanka, A., ... Nezami, B. (2020). Computerized Nuclear Morphology Features Aid in Diagnosis of Cell Clusters on Digitized Bile Duct Brushing Slides. , 100 (SUPPL 1), 1662--1663.

Case School of Engineering

Center for Computational Imaging and Personalized Diagnostics

Center for Computational Imaging and Personalized Diagnostics Publications

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

Combination of tumor multinucleation and spatial arrangement of tumor-infiltrating lymphocytes to predict overall survival in oropharyngeal squamous cell carcinoma: A multisite study.

Computerized features of spatial arrangement of tumor-infiltrating lymphocytes from H&E images predicts survival and response to checkpoint inhibitors in gynecologic cancers.

Computerized features of spatial interplay of tumor-infiltrating lymphocytes predict disease recurrence in p16+ oropharyngeal squamous cell carcinoma: A multisite validation study.

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).

Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients

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

Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients

MP81-06 RADIOMIC FEATURES OF PROSTATE CANCER PATIENTS (GLEASON GRADE GROUP = 2) SHOW DIFFERENCES BETWEEN AFRICAN AMERICAN AND CAUCASIAN POPULATIONS ON BI-PARAMETRIC MRI: PRELIMINARY FINDINGS

PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY

PD57-05 A DEEP LEARNING NETWORK ALONG WITH PIRADS CAN DISTINGUISH CLINICALLY SIGNIFICANT AND INSIGNIFICANT PROSTATE CANCER ON BI-PARAMETRIC MRI: A MULTI-CENTER STUDY

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

A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer

Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores

Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer

Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers

Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results

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

Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting

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

Abstract P5-06-15: Computer extracted features of nuclear shape, orientation disorder and texture from H&E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS)

Abstract P5-06-16: Histomorphometric measure of disorder of collagen fiber orientation is associated with risk of recurrence in ER+ breast cancers in ECOG-ACRIN E2197 and TCGA-BRCA

Deep-learning approaches for Gleason grading of prostate biopsies

Computerized Nuclear Morphology Features Aid in Diagnosis of Cell Clusters on Digitized Bile Duct Brushing Slides

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