Andrew Janowczyk | Case School of Engineering

Patents Pending

2012, "Local Morphologic Scale with applications to Multi-protocol Registration and Tissue classification" PCT/US12/21133, Anant Madabhushi, Andrew Janowczyk, & Sharat Chandran.

2010, "High-throughput biomarker segmentation utilizing hierarchical normalized cuts" US20120219206, Anant Madabhushi, Andrew Janowczyk, & Sharat Chandran.

Publications

Chen, Y., Zee, J., Smith, A., Jayapandian, C., Hodgin, J., Howell, D., Palmer, M., Thomas, D., Cassol, C., Farris, A., Perkinson, K., Madabhushi, A., Barisoni, L., & Janowczyk, A. (2021). Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies. Journal of Pathology, 253 (3), 268-278.

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

Jayapandian, C., Chen, Y., Janowczyk, A., Palmer, M., Cassol, C., Sekulic, M., Hodgin, J., Zee, J., Hewitt, S., O'Toole, J., Toro, P., Sedor, J., Barisoni, L., Madabhushi, A., Sedor, J., Dell, K., Schachere, M., Negrey, J., Lemley, K., Lim, E., Srivastava, T., Garrett, A., Sethna, C., Laurent, K., Appel, G., Toledo, M., Barisoni, L., Greenbaum, L., Wang, C., Kang, C., Adler, S., Nast, C., LaPage, J., Stroger, J., Athavale, A., Itteera, M., Neu, A., Boynton, S., Fervenza, F., Hogan, M., Lieske, J., Chernitskiy, V., Kaskel, F., Kumar, N., Flynn, P., Kopp, J., Blake, J., Trachtman, H., Zhdanova, O., Modersitzki, F., Vento, S., Lafayette, R., Mehta, K., Gadegbeku, C., Johnstone, D., Quinn-Boyle, S., Cattran, D., Hladunewich, M., Reich, H., Ling, P., Romano, M., Fornoni, A., Bidot, C., Kretzler, M., Gipson, D., Williams, A., LaVigne, J., Derebail, V., Gibson, K., Froment, A., Grubbs, S., Holzman, L., Meyers, K., Kallem, K., Lalli, J., Sambandam, K., Wang, Z., Rogers, M., Jefferson, A., Hingorani, S., Tuttle, K., Bray, M., Kelton, M., Cooper, A., Freedman, B., & Howlin, B. (2021). Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney International, 99 (1), 86-101.

Sadri, A., Janowczyk, A., Zhou, R., Verma, R., Beig, N., Antunes, J., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2020). Technical Note: MRQy — An open-source tool for quality control of MR imaging data. Medical Physics, 47 (12), 6029-6038.

Marion, S., Desharnais, L., Studer, N., Dong, Y., Notter, M., Poudel, S., Menin, L., Janowczyk, A., Hettich, R., Hapfelmeier, S., & Bernier-Latmani, R. (2020). Biogeography of microbial bile acid transformations along the murine gut. Journal of Lipid Research, 61 (11), 1450-1463.

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.

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 learningderived 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 learningderived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology.

Chen, Y., Janowczyk, A., & Madabhushi, A. (2020). Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis. JCO Clinical Cancer Informatics.

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.

Leo, P., Elliott, R., Janowczyk, A., Janaki, N., Bera, K., Shiradkar, R., El-Fahmawi, A., Kim, J., Shahait, M., Shah, A., Thulasidass, H., Tewari, A., Gupta, S., Shih, N., Feldman, M., Lal, P., Lee, D., & 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.

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.

Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Shiradkar, R., Farr�, X., Yamoah, K., Rebbeck, T., Shih, N., Khani, F., Robinson, B., Eklund, L., Ettala, O., Taimen, P., Boström, P., Feldman, M., Gupta, S., Lal, P., & Madabhushi, A. (2019). Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: A multisite study.. Journal of Clinical Oncology, 37 (15_suppl), 5060-5060.

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. JCO Clinical Cancer Informatics.

Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M., & Madabhushi, A. (2019). HistoQC: An open-source quality control tool for digital pathology slides. JCO Clinical Cancer Informatics, 3

Chen, Y., Janowczyk, A., & Madabhushi, A. (2019). Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis. JCO Clinical Cancer Informatics, 3 , 221-233.

Unternaehrer, J., Grobholz, R., Janowczyk, A., & Zlobec, I. (2019). Current opinion, status and future development of digital pathology in Switzerland. Journal of Clinical Pathology.

Whitney, J., Corredor-Prada, G., Janowczyk, A., Ganesan, S., Doyle, S., Tomaszewski, J., Feldman, M., Gilmore, H., & Madabhushi, A. (2018). Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer, 18 (1).

Lu, C., Romo-Bucheli, D., WAng, X., Janowczyk, A., Ganesan, S., Gilmore, H., Rimm, D., & Madabhushi, A. (2018). Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Laboratory Investigation, 98 (11), 1438-1448.

Leo, P., Shankar, E., Elliott, R., Janowczyk, A., Janaki, N., MacLennan, G., Madabhushi, A., & Gupta, S. (2018). Abstract LB-021: Combination of quantitative histomorphometry with NF?B/p65 nuclear localization is better predictor of biochemical recurrence in prostate cancer patients. Cancer Research, 78 (13 Supplement), LB-021-LB-021.

Bhargava, H., Leo, P., Elliott, R., Janowczyk, A., Whitney, J., Gupta, S., Yamoah, K., Rebbeck, T., Feldman, M., Lal, P., & 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.

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.

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.

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).

Li, H., Leo, P., Nezami, B., Akgul, M., Elliott, R., Harper, H., Janowczyk, A., MacLennan, G., & 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).

Leo, P., Shankar, E., Elliott, R., Janowczyk, A., Janaki, N., MacLennan, G., Madabhushi, A., & 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).

Whitney, J., Romeo-Bucheli, D., Janowczyk, A., Ganesan, S., Feldman, M., Gilmore, H., & Madabhushi, A. (2018). Abstract P4-09-11: Computer extracted features of tumor grade from H&E images predict oncotype DX risk categories for early stage ER+ breast cancer. Cancer Research, 78 (4 Supplement), P4-09-11-P4-09-11.

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).

Lu, C., Lewis, J., Dupont, W., Plummer, W., Janowczyk, A., & Madabhushi, A. (2017). An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Modern Pathology, 30 (12), 1655-1665.

Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A. (2017). A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry Part A, 91 (6), 566-573.

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2017). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics, 57 , 50-61.

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2017). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 57 , 50 - 61.

Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A. (2016). Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images. Scientific Reports, 6

Penzias, G., Janowczyk, A., Singanamalli, A., Rusu, M., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., Böhm, M., Haynes, A., Ponsky, L., Viswanath, S. E., & Madabhushi, A. E. (2016). AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments.. Scientific reports, 6 , 29906.

Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.. Journal of pathology informatics, 7 , 29.

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2016). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics, 7 (1).

Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A. (2016). A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization [21681163].

Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics [21533539], 7 (1).

Janowczyk, A., Basavanhally, A., & Madabhushi, A. (2016). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics [08956111].

Madabhushi, A., & Janowczyk, A. (2013). Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions. Journal of Pathology Informatics.

Janowczyk, A., Chandran, S., Singh, U., Sasaroli, D., Coukos, G., Feldman, M., & Madabhushi, A. (2012). High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts.. IEEE transactions on bio-medical engineering, 59 (5), 1240-52.

Xu, J., Janowczyk, A., Chandran, S., & Madabhushi, A. (2011). A high-throughput active contour scheme for segmentation of histopathological imagery.. Medical image analysis, 15 (6), 851-62.

Janowczyk, A., Chandran, S., Singh, U., Sasaroli, D., Coukos, G., Feldman, M., & Madabhushi, A. (2009). Hierarchical normalized cuts: unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12 (Pt 1), 230-8.