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2. CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models

Author

Fan, Fan, Martinez, Georgia, Desilvio, Thomas, Shin, John, Chen, Yijiang, Wang, Bangchen, Ozeki, Takaya, Lafarge, Maxime W., Koelzer, Viktor H., Barisoni, Laura, Madabhushi, Anant, Viswanath, Satish E., and Janowczyk, Andrew

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder, an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com., Comment: 26 pages, 9 figures, 4 tables. Abstract was accepted by European Society of Digital and Integrative Pathology (ESDIP), Germany, 2022

Published
2023

3. PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling

Author

Walker, Cedric, Talawalla, Tasneem, Toth, Robert, Ambekar, Akhil, Rea, Kien, Chamian, Oswin, Fan, Fan, Berezowska, Sabina, Rottenberg, Sven, Madabhushi, Anant, Maillard, Marie, Barisoni, Laura, Horlings, Hugo Mark, and Janowczyk, Andrew

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets., Comment: The submission includes 15 pages, 8 figures, 1 table, and 30 references. It is a new submission

Published
2023

4. Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

Author

Zhu, Chuang, Liu, Shengjie, Yu, Zekuan, Xu, Feng, Aggarwal, Arpit, Corredor, Germán, Madabhushi, Anant, Qu, Qixun, Fan, Hongwei, Li, Fangda, Li, Yueheng, Guan, Xianchao, Zhang, Yongbing, Singh, Vivek Kumar, Akram, Farhan, Sarker, Md. Mostafa Kamal, Shi, Zhongyue, and Jin, Mulan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation., Comment: 12 pages, 12 figures, 2tables

Published
2023

11. Contributors

Author

Aja-Fernández, Santiago, primary, Alberola-López, Carlos, additional, Altmann, Andre, additional, Bano, Sophia, additional, Christlein, Vincent, additional, Cong, Shan, additional, Conjeti, Sailesh, additional, Cootes, Tim, additional, Curiale, Ariel H., additional, de Bruijne, Marleen, additional, Demirci, Stefanie, additional, de Vos, Bob D., additional, Duncan, James S., additional, Frangi, Alejandro F., additional, Graham, Simon, additional, Heinrich, Mattias, additional, Išgum, Ivana, additional, Lorenzi, Marco, additional, Lu, Cheng, additional, Madabhushi, Anant, additional, Maier, Andreas, additional, Martin, Melissa, additional, Moreau, Thomas, additional, Mullan, Sean, additional, Oguz, Ipek, additional, Paragios, Nikos, additional, Petersen, Jens, additional, Prince, Jerry L., additional, Rajpoot, Nasir, additional, Ramos-Llordén, Gabriel, additional, Ravikumar, Nishant, additional, Schnabel, Julia, additional, Shen, Li, additional, Shinohara, Russell T., additional, Sokooti, Hessam, additional, Sonka, Milan, additional, Sotiras, Aristeidis, additional, Sporring, Jon, additional, Staib, Lawrence H., additional, Staring, Marius, additional, Stoyanov, Danail, additional, Unberath, Mathias, additional, Vegas Sánchez-Ferrero, Gonzalo, additional, Wassermann, Demian, additional, Xia, Yan, additional, Yushkevich, Paul A., additional, Zakeri, Arezoo, additional, Zhang, Honghai, additional, Zhang, Lichun, additional, and Zhang, Miaomiao, additional

Published
2024
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12. Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

Author

Braman, Nathaniel, Prasanna, Prateek, Bera, Kaustav, Alilou, Mehdi, Khorrami, Mohammadhadi, Leo, Patrick, Etesami, Maryam, Vulchi, Manasa, Turk, Paulette, Gupta, Amit, Jain, Prantesh, Fu, Pingfu, Pennell, Nathan, Velcheti, Vamsidhar, Abraham, Jame, Plecha, Donna, and Madabhushi, Anant

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Tissues and Organs
Abstract

Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in these attributes may help stratify patients by likely benefit of systemic therapy (e.g. chemotherapy). In this work, we present a new category of radiomic biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancers, imaging modalities, and treatment regimens. Experimental Design: We segmented tumor vessels and computed mathematical measurements of twistedness and organization on routine pre-treatment radiology (CT or contrast-enhanced MRI) from 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n=371) or non-small cell lung cancer (NSCLC, n=187). Results: Across 4 chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (p<.05) predicted response in held out testing cohorts alone (AUC=0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. QuanTAV risk scores were prognostic of recurrence free survival in treatment cohorts chemotherapy for breast cancer (p=0.002, HR=1.25, 95% CI 1.08-1.44, C-index=.66) and chemoradiation for NSCLC (p=0.039, HR=1.28, 95% CI 1.01-1.62, C-index=0.66). Categorical QuanTAV risk groups were independently prognostic among all treatment groups, including NSCLC patients receiving chemotherapy (p=0.034, HR=2.29, 95% CI 1.07-4.94, C-index=0.62). Conclusions: Across these domains, we observed an association of vascular morphology on radiology with treatment outcome. Our findings suggest the potential of tumor-associated vasculature shape and structure as a prognostic and predictive biomarker for multiple cancers and treatments., Comment: This manuscript has been accepted for publication in Clinical Cancer Research, which is published by the American Association for Cancer Research

Published
2022
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14. Triangular Analysis of Geographical Interplay of Lymphocytes (TriAnGIL): Predicting Immunotherapy Response in Lung Cancer

Author

Arabyarmohammadi, Sara, Corredor, German, Zhou, Yufei, López de Rodas, Miguel, Schalper, Kurt, Madabhushi, Anant, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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15. STAR-Echo: A Novel Biomarker for Prognosis of MACE in Chronic Kidney Disease Patients Using Spatiotemporal Analysis and Transformer-Based Radiomics Models

Author

Dhamdhere, Rohan, Modanwal, Gourav, Makhlouf, Mohamed H. E., Shafiabadi Hassani, Neda, Bharadwaj, Satvika, Fu, Pingfu, Milioglou, Ioannis, Rahman, Mahboob, Al-Kindi, Sadeer, Madabhushi, Anant, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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20. Laterality of Radiation Therapy in Breast Cancer is Not Associated With Increased Risk of Coronary Artery Disease in the Contemporary Era

Author

Seth, Lakshya, Makram, Omar, Essa, Amr, Patel, Vraj, Jiang, Stephanie, Bhave, Aditya, Yerraguntla, Sandeep, Gopu, Gaurav, Malik, Sarah, Swaby, Justin, Rast, Johnathon, Padgett, Caleb A., Shetewi, Ahmed, Nain, Priyanshu, Weintraub, Neal, Miller, Eric D., Dent, Susan, Barac, Ana, Shiradkar, Rakesh, Madabhushi, Anant, Ferguson, Catherine, and Guha, Avirup

Published
2024
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24. Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer

Author

Chen, Yuli, Li, Haojia, Janowczyk, Andrew, Toro, Paula, Corredor, Germán, Whitney, Jon, Lu, Cheng, Koyuncu, Can F., Mokhtari, Mojgan, Buzzy, Christina, Ganesan, Shridar, Feldman, Michael D., Fu, Pingfu, Corbin, Haley, Harbhajanka, Aparna, Gilmore, Hannah, Goldstein, Lori J., Davidson, Nancy E., Desai, Sangeeta, Parmar, Vani, and Madabhushi, Anant

Published
2023
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25. Beta-defensin index: A functional biomarker for oral cancer detection

Author

Ghosh, Santosh K., Man, Yuncheng, Fraiwan, Arwa, Waters, Christopher, McKenzie, Crist, Lu, Cheng, Pfau, David, Kawsar, Hameem, Bhaskaran, Natarajan, Pandiyan, Pushpa, Jin, Ge, Briggs, Farren, Zender, Chad C., Rezaee, Rod, Panagakos, Fotinos, Thuener, Jason E., Wasman, Jay, Tang, Alice, Qari, Hiba, Wise-Draper, Trisha, McCormick, Thomas S., Madabhushi, Anant, Gurkan, Umut A., and Weinberg, Aaron

Published
2024
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26. Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

Author

Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Campbell, Taneisha, Fox, Monica, Knight, Richard, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell; Ron Gaba, Eunice, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown; Jerica Berge, Dorisann, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Chaudhury; Evan Zeitler, Prabir Roy, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, Kermani, Asra, Lee, Simon C., Ma, Shihong, Miller, Richard Tyler, Moe, Orson W., Park, Harold, Patel, Jiten, Pillai, Anil, Sambandam, Kamalanathan, Torrealba, Jose, Toto, Robert D., Vazquez, Miguel, Wang, Nancy, Wen, Natasha, Zhang, Dianbo, Alpers, Charles, Berglund, Ashley, Berry, Brooke, Blank, Kristina, Brown, Keith, Carson, Jonas, Daniel, Stephen, de Boer, Ian H., Dighe, Ashveena L., Dowd, Frederick, Grewenow, Stephanie M., Himmelfarb, Jonathan, Hoofnagle, Andrew, Jefferson, Nichole, Larson, Brandon, Limonte, Christine, McClelland, Robyn, Mooney, Sean, Nam, Yunbi, Park, Christopher, Phuong, Jimmy, Rezaei, Kasra, Roberts, Glenda, Sarkisova, Natalya, Shankland, Stuart, Snyder, Jaime, Stutzke, Christy, Tuttle, Katherine, Wangperawong, Artit, Wilcox, Adam, Williams, Kayleen, Young, Bessie, Allen, Jamie, Caprioli, Richard M., de Caestecker, Mark, Djambazova, Katerina, Dufresne, Martin, Farrow, Melissa, Fogo, Agnes, Sharman, Kavya, Spraggins, Jeffrey, Basta, Jeannine, Conlon, Kristine, Diettman, Sabine M., Gaut, Joseph, Kaushal, Madhurima, Jain, Sanjay, Knoten, Amanda, Minor, Brittany, Nwanne, Gerald, Vijayan, Anitha, Zhang, Bo, Arora, Tanima, Cantley, Lloyd, Victoria Castro, Angela M., Kakade, Vijayakumar, Moeckel, Gilbert, Moledina, Dennis, Shaw, Melissa, Wilson, Francis P., El-Achkar, Tarek M., and Eadon, Michael T.

Published
2024
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29. Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma

Author

Ismail, Marwa, Prasanna, Prateek, Bera, Kaustav, Statsevych, Volodymyr, Hill, Virginia, Singh, Gagandeep, Partovi, Sasan, Beig, Niha, McGarry, Sean, Laviolette, Peter, Ahluwalia, Manmeet, Madabhushi, Anant, and Tiwari, Pallavi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity within the tumor confines, on MRI scans. R-DepTH, on N = 207 GBM cases (training set (St) = 128, testing set (Sv) = 79), demonstrated improved prognosis of overall survival by categorizing patients into low- (prolonged survival) and high-risk (poor survival) groups (on St, p-value = 0.0000035, and on Sv, p-value = 0.0024). R-DepTH descriptor may serve as a comprehensive MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.

Published
2021

30. Quick Annotator: an open-source digital pathology based rapid image annotation tool

Author

Miao, Runtian, Toth, Robert, Zhou, Yu, Madabhushi, Anant, and Janowczyk, Andrew

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Image based biomarker discovery typically requires an accurate segmentation of histologic structures (e.g., cell nuclei, tubules, epithelial regions) in digital pathology Whole Slide Images (WSI). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROI) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (a) accept accurately annotated regions, or (b) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (a) 337,386 nuclei in 5 pancreatic WSIs, (b) 5,692 tubules in 10 colorectal WSIs, and (c) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102x, 9x, and 39x were respectively witnessed while retaining f-scores >.95, suggesting QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies., Comment: The submission includes 14 pages, 7 figures, 2 tables,and 21 references. It is a new submission

Published
2021

32. Molecular Signatures of Glomerular Neovascularization in a Patient with Diabetic Kidney Disease

Author

Ferkowicz, Michael J., Verma, Ashish, Barwinska, Daria, Melo Ferreira, Ricardo, Henderson, Joel M., Kirkpatrick, Mary, Silva, Paolo S., Steenkamp, Devin W., Phillips, Carrie L., Waikar, Sushrut S., Sutton, Timothy A., Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Stillman, Isaac, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Weins, Astrid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, Varela, German, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Barisoni, Laura, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Fox, Monica, Hayashi, Lynda, Jefferson, Nichole, Richard Knight, Jennifer Jones, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Stutzke, Christy, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Wong, Aaron, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Lutnick, Brendon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell, Eunice, Gaba, Ron, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown, Dorisann, Berge, Jerica, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Roy-Chaudhury, Prabir, Zeitler, Evan, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, and Asra

Published
2024
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33. Participant Experience with Protocol Research Kidney Biopsies in the Kidney Precision Medicine Project

Author

Victoria-Castro, Angela M., Corona-Villalobos, Celia P., Xu, Alan Y., Onul, Ingrid, Huynh, Courtney, Chen, Sarah W., Ugwuowo, Ugochukwu, Sarkisova, Natalya, Dighe, Ashveena L., Blank, Kristina N., Blanc, Victoria M., Rose, Michael P., Himmelfarb, Jonathan, de Boer, Ian H., Tuttle, Katherine R., Roberts, Glenda V., Alexandrov, Theodore, Alloway, Rita R., Alpers, Charles E., Amodu, Afolarin A., Anderton, Christopher R., Anjani, Kavya, Appelbaum, Paul, Ardayfio, Joseph, Arora, Tanima, Ascani, Heather, El-Achkar, Tarek M., Aulisio, Mark, Azeloglu, Evren U., Balderes, Olivia, Balis, Ulysses G.J., Bansal, Shweta, Barasch, Jonathan M., Bansal, Shweta, Barkell, Alex, Barwinska, Daria, Basit, Mujeeb, Basta, Jeanine, Bebiak, Jack, Beck, Laurence H., Bender, Filitsa, Berglund, Ashley, Bernard, Lauren, Berrouet, Cecilia, Berry, Brooke, Bjornstad, Petter M., Blanc, Victoria M., Blank, Kristina N., Bledsoe, Sharon, Boada, Patrick, Bogen, Steve, Bomback, Andrew S., Bonevich, Nikole, Borner, Katy, Brown, Keith, Bueckle, Andreas, Burg, Ashley R., Burgess, Adam, Bush, Lakeshia, Bush, William S., Campbell, Catherine E., Campbell, Taneisha, Canetta, Pietro A., Cantley, Lloyd G., Caprioli, Richard M., Carson, Jonas, Chen, Sarah, Chen, Yijiang M., Cheng, Yinghua, Cimino, Jim, Colona, Mia R., Conser, Ninive C., Cooperman, Leslie, Crawford, Dana C., DʼAgati, Vivette D., Dagher, Pierre C., Daniel, Stephen, Daratha, Kenn, de Boer, Ian H., Diettman, Sabine M., Dighe, Ashveena L., Donohoe, Isabel, Dowd, Frederick, Dunn, Kenneth W., Eadon, Michael T., Eddy, Sean, Elder, Michele M., Ferkowicz, Michael J., Frey, Renee, Gadegbeku, Crystal A., Gaut, Joseph P., Gilliam, Matthew, Ginley, Brandon, Gisch, Debora, Goltsev, Yury, Gonzalez-Vicente, Agustin, Greka, Anna, Grewenow, Stephanie M., Hacohen, Nir, Hall, Daniel E., Hansen, Jens, Hayashi, Lynda, He, Cijang, He, Yougqun, Hedayati, S. Susan, Henderson, Joel M., Hendricks, Allen H., Herlitz, Leal, Herr, Bruce W., Himmelfarb, Jonathan, Hodgin, Jeffrey B., Hoofnagle, Andrew N., Hoover, Paul J., Ilori, Titlayo, Iyengar, Ravi, Jain, Sanjay, Jain, Yashvardhan, Janowczyk, Andrew, Jefferson, Nichole, Johansen, Camille, Jolly, Stacey, Kakade, Vijaykumar R., Kellum, John A., Kelly, Katherine J., Kermani, Asra, Kiryluk, Krzysztof, Knight, Richard, Koewler, Robert, Kretzler, Matthias, Kudose, Satoru, Lake, Blue B., Larson, Brandon, Laszik, Zoltan G., Lecker, Stewart H., Lee, Paul J., Lee, Simon C., Lienczewski, Chrysta, Limonte, Christine, Lu, Christopher Y., Lucarelli, Nicholas, Lukowski, Jessica, Luo, Jinghui, Lutnick, Brendon, Ma, Shihong, Madabhushi, Anant, Madhavan, Sethu M., Maikhor, Shana, Mariani, Laura H., Marshall, Jamie L., McClelland, Robyn L., McMahon, Gearoid M., Mehl, Karla, Ferreira, Ricardo Melo, Menez, Steven, Menon, Rajasree, Miller, R. Tyler, Moe, Orson W., Moledina, Dennis, Montellano, Richard, Mooney, Sean D., Morales, Martha Catalina, Mukatash, Tariq, Murugan, Raghavan, Nam, Yunbi, Nguyen, Jane, Nolan, Garry, Oʼtoole, John, Oliver, George (Holt), Onul, Ingrid, Otto, Edgar, Palevsky, Paul M., Palmer, Ellen, Pamreddy, Annapurna, Parikh, Chirag R., Parikh, Samir, Park, Christopher, Park, Harold, Pasa-Tolic, Ljiljana, Patel, Jiten, Patterson, Nathan, Phuong, Jim, Pillai, Anil, Pinkeney, Roy, Poggio, Emilio, Pollack, Ari, Prasad, Pottumarthi, Pyle, Laura, Quardokus, Ellen M., Randhawa, Parmjeet, Rauchman, Michael I., Record, Elizabeth, Rennke, Helmut, Rezaei, Kasra, Rike, Adele, Rivera, Marcelino, Roberts, Glenda V., Rosas, Sylvia E., Rosenberg, Avi, Rosengart, Matthew, Rovin, Brad, Roy, Neil, Sabatello, Maya, Sambandam, Kamalanathan, Sarder, Pinaki, Sarkisova, Natalya, Sarwal, Minnie, Saul, John, Schaub, Jennifer, Schmidt, Insa, Sealfon, Rachel, Sedor, John, Sendrey, Dianna, Shang, Ning, Shankland, Stuart, Shapiro, John P., Sharma, Kumar, Sharman, Kavya, Shaw, Melissa M., Shi, Tiffany, Shpigel, Anna, Sigdel, Tara, Slade, Austen, Snyder, Jamie, Spates-Harden, Kassandra, Spraggins, Jeffrey M., Srivastava, Anand, Steck, Becky, Stillman, Isaac, Stutzke, Christy, Su, Jing, Sun, Jennifer, Sutton, Timothy A., Taliercio, Jonathan, Tan, Roderick, Torrealba, Jose, Toto, Robert D., Troyanskaya, Olga, Tublin, Mitchell, Tuttle, Katherine R., Ugwuowo, Ugochukwu, Valerius, M. Todd, Van de Plas, Raf, Varela, German, Vazquez, Miguel, Velickovic, Dusan, Venkatachalam, Manjeri, Verma, Ashish, Victoria-Castro, Angela M., Vijayan, Anitha, Corona-Villalobos, Celia P., Vinovskis, Carissa, Viswanathan, Vidya S., Vita, Tina, Waikar, Sushrut, Wang, Ashley, Wang, Ruikang, Wang, Nancy, Weins, Astrid, Wen, Natasha, Wen, Yumeng, Wilcox, Adam, Williams, James C., Jr., Kayleen Williams, Williams, Mark, Wilson, Francis P., Winfree, Seth, Winters, James, Wofford, Stephanie, Wong, Aaron, Woodle, E. Steve, Xiong, Yuguang, Xu, Alan, Yadati, Pranav, Ye, Hongping, Yu, Guanghao, Zhang, Dianbo, Zhang, Guanshi, and Zhang, Kun

Published
2024
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34. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study

Author

Dudgeon, Sarah N, Wen, Si, Hanna, Matthew G, Gupta, Rajarsi, Amgad, Mohamed, Sheth, Manasi, Marble, Hetal, Huang, Richard, Herrmann, Markus D, Szu, Clifford H., Tong, Darick, Werness, Bruce, Szu, Evan, Larsimont, Denis, Madabhushi, Anant, Hytopoulos, Evangelos, Chen, Weijie, Singh, Rajendra, Hart, Steven N., Saltz, Joel, Salgado, Roberto, and Gallas, Brandon D

Subjects
Quantitative Biology - Quantitative Methods, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Purpose: In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images (WSIs). We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64 glass slides of hematoxylin- and eosin-stained ductal carcinoma core biopsies prepared at a single clinical site. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. Results: The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. Conclusion: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the FDA via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned., Comment: 26 pages, 4 figures, 2 tables Submitted to the Journal of Pathology Informatics Project web page: https://ncihub.org/groups/eedapstudies

Published
2020
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35. 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

Author

Leo, Patrick, Chandramouli, Sacheth, Farré, Xavier, Elliott, Robin, Janowczyk, Andrew, Bera, Kaustav, Fu, Pingfu, Janaki, Nafiseh, El-Fahmawi, Ayah, Shahait, Mohammed, Kim, Jessica, Lee, David, Yamoah, Kosj, Rebbeck, Timothy R, Khani, Francesca, Robinson, Brian D, Shih, Natalie NC, Feldman, Michael, Gupta, Sanjay, McKenney, Jesse, Lal, Priti, and Madabhushi, Anant

Subjects
Clinical Research, Bioengineering, Prostate Cancer, Cancer, Patient Safety, Urologic Diseases, Eosine Yellowish-(YS), Hematoxylin, Humans, Male, Neoplasm Recurrence, Local, Prognosis, Prostate, Prostatectomy, Prostatic Neoplasms, Biochemical recurrence, Cribriform, Digital pathology, Gleason grading, Machine learning, Prostate cancer, Clinical Sciences
Abstract

BackgroundThe presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement.ObjectiveTo investigate quantitative machine-based assessment of the extent and prognostic utility of ICC, especially within individual Gleason grade groups.Design, setting, and participantsA machine learning approach was developed for ICC segmentation using 70 RP patients and validated in a cohort of 749 patients from four sites whose median year of surgery was 2007 and with median follow-up of 28 mo. ICC was segmented on one representative hematoxylin and eosin RP slide per patient and the fraction of tumor area composed of ICC, the cribriform area index (CAI), was measured.Outcome measurements and statistical analysisThe association between CAI and BCR was measured in terms of the concordance index (c index) and hazard ratio (HR).Results and limitationsCAI was correlated with BCR (c index 0.62) in the validation set of 411 patients with ICC morphology, especially those with Gleason grade group 2 cancer (n = 192; c index 0.66), and was less prognostic when patients without ICC were included (c index 0.54). A doubling of CAI in the group with ICC morphology was prognostic after controlling for Gleason grade, surgical margin positivity, preoperative prostate-specific antigen level, pathological T stage, and age (HR 1.19, 95% confidence interval 1.03-1.38; p = 0.018).ConclusionsAutomated image analysis and machine learning could provide an objective, quantitative, reproducible, and high-throughput method of quantifying ICC area. The performance of CAI for grade group 2 cancer suggests that for patients with little Gleason 4 pattern, the ICC fraction has a strong prognostic role.Patient summaryMachine-based measurement of a specific cell pattern (cribriform; sieve-like, with lots of spaces) in images of prostate specimens could improve risk stratification for patients with prostate cancer. In the future, this could help in expanding the criteria for active surveillance.

Published
2021

36. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

Author

Zhou, S. Kevin, Greenspan, Hayit, Davatzikos, Christos, Duncan, James S., van Ginneken, Bram, Madabhushi, Anant, Prince, Jerry L., Rueckert, Daniel, and Summers, Ronald M.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions., Comment: 20 pages, 7 figures

Published
2020
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37. Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study

Author

Ismail, Marwa, Hill, Virginia, Statsevych, Volodymyr, Mason, Evan, Correa, Ramon, Prasanna, Prateek, Singh, Gagandeep, Bera, Kaustav, Thawani, Rajat, Madabhushi, Anant, Ahluwalia, Manmeet, and Tiwari, Pallavi

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-na\"ive MRI provide early cues regarding likelihood of a patient developing pseudo-progression versus tumor recurrence? In this study, 74 pre-treatment Glioblastoma MRI scans with PsP (33) and tumor recurrence (41) were analyzed. First, enhancing lesion on Gd-T1w MRI and peri-lesional hyperintensities on T2w/FLAIR were segmented by experts and then registered to a brain atlas. Using patients from the two phenotypes, we construct two atlases by quantifying frequency of occurrence of enhancing lesion and peri-lesion hyperintensities, by averaging voxel intensities across the population. Analysis of differential involvement was then performed to compute voxel-wise significant differences (p-value<0.05) across the atlases. Statistically significant clusters were finally mapped to a structural atlas to provide anatomic localization of their location. Our results demonstrate that patients with tumor recurrence showed prominence of their initial tumor in the parietal lobe, while patients with PsP showed a multi-focal distribution of the initial tumor in the frontal and temporal lobes, insula, and putamen. These preliminary results suggest that lateralization of pre-treatment lesions towards certain anatomical areas of the brain may allow to provide early cues regarding assessing likelihood of occurrence of pseudo-progression from tumor recurrence on MRI scans.

Published
2020

38. MRQy: An Open-Source Tool for Quality Control of MR Imaging Data

Author

Sadri, Amir Reza, Janowczyk, Andrew, Zou, Ren, Verma, Ruchika, Beig, Niha, Antunes, Jacob, Madabhushi, Anant, Tiwari, Pallavi, and Viswanath, Satish E.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

We sought to develop a quantitative tool to quickly determine relative differences in MRI volumes both within and between large MR imaging cohorts (such as available in The Cancer Imaging Archive (TCIA)), in order to help determine the generalizability of radiomics and machine learning schemes to unseen datasets. The tool is intended to help quantify presence of (a) site- or scanner-specific variations in image resolution, field-of-view, or image contrast, or (b) imaging artifacts such as noise, motion, inhomogeneity, ringing, or aliasing; which can adversely affect relative image quality between data cohorts. We present MRQy, a new open-source quality control tool to (a) interrogate MRI cohorts for site- or equipment-based differences, and (b) quantify the impact of MRI artifacts on relative image quality; to help determine how to correct for these variations prior to model development. MRQy extracts a series of quality measures (e.g. noise ratios, variation metrics, entropy and energy criteria) and MR image metadata (e.g. voxel resolution, image dimensions) for subsequent interrogation via a specialized HTML5 based front-end designed for real-time filtering and trend visualization. MRQy was used to evaluate (a) n=133 brain MRIs from TCIA (7 sites), and (b) n=104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common MR imaging artifacts. MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible at \url{http://github.com/ccipd/MRQy} for wider community use and feedback., Comment: 28 pages, 7 figures. Submitted to Medical Physics

Published
2020
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39. Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study

Author

Braman, Nathaniel, Adoui, Mohammed El, Vulchi, Manasa, Turk, Paulette, Etesami, Maryam, Fu, Pingfu, Bera, Kaustav, Drisis, Stylianos, Varadan, Vinay, Plecha, Donna, Benjelloun, Mohammed, Abraham, Jame, and Madabhushi, Anant

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Applications, Statistics - Machine Learning
Abstract

Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant chemotherapy at a single institution were used to train (n=85) and tune (n=15) a convolutional neural network (CNN) to predict pCR. A multi-input CNN leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was identified to achieve optimal response prediction within the validation set (AUC=0.93). This model was then tested on two independent testing cohorts with pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and a 29 patient multicenter trial including data from 3 additional institutions (AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction model was found to exceed a multivariable model incorporating predictive clinical variables (AUC < .65 in testing cohorts) and a model of semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing cohorts). The results presented in this work across multiple sites suggest that with further validation deep learning could provide an effective and reliable tool to guide targeted therapy in breast cancer, thus reducing overtreatment among HER2+ patients., Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3 figures in main text

Published
2020

40. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

Author

Leo, Patrick, Janowczyk, Andrew, Elliott, Robin, Janaki, Nafiseh, Bera, Kaustav, Shiradkar, Rakesh, Farré, Xavier, Fu, Pingfu, El-Fahmawi, Ayah, Shahait, Mohammed, Kim, Jessica, Lee, David, Yamoah, Kosj, Rebbeck, Timothy R, Khani, Francesca, Robinson, Brian D, Eklund, Lauri, Jambor, Ivan, Merisaari, Harri, Ettala, Otto, Taimen, Pekka, Aronen, Hannu J, Boström, Peter J, Tewari, Ashutosh, Magi-Galluzzi, Cristina, Klein, Eric, Purysko, Andrei, Nc Shih, Natalie, Feldman, Michael, Gupta, Sanjay, Lal, Priti, and Madabhushi, Anant

Abstract

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p

Published
2021

43. A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

Author

Li, Lin, Shiradkar, Rakesh, Leo, Patrick, Algohary, Ahmad, Fu, Pingfu, Tirumani, Sree Harsha, Mahran, Amr, Buzzy, Christina, Obmann, Verena C, Mansoori, Bahar, El-Fahmawi, Ayah, Shahait, Mohammed, Tewari, Ashutosh, Magi-Galluzzi, Cristina, Lee, David, Lal, Priti, Ponsky, Lee, Klein, Eric, Purysko, Andrei S, and Madabhushi, Anant

Subjects
Aging, Prostate Cancer, Cancer, Biomedical Imaging, Patient Safety, Urologic Diseases, Good Health and Well Being, Aged, Aged, 80 and over, Biomarkers, Tumor, Clinical Decision-Making, Diagnostic Imaging, Disease Management, Humans, Kaplan-Meier Estimate, Magnetic Resonance Imaging, Male, Middle Aged, Neoplasm Recurrence, Local, Neoplasm Staging, Nomograms, Patient Selection, Perioperative Care, Prognosis, Prostatectomy, Prostatic Neoplasms, Retrospective Studies, Workflow, Prostate cancer, Biochemical recurrence, Adverse pathology, Radiomic, MRI, Clinical Sciences, Public Health and Health Services
Abstract

BackgroundWe developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher.MethodsA retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D1, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D2, N = 127).Findings"RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79)."InterpretationRadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy.FundingThe National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

Published
2021

44. Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients

Author

Koyuncu, Can F., Frederick, Mitchell J., Thompson, Lester D.R., Corredor, Germán, Khalighi, Sirvan, Zhang, Zelin, Song, Bolin, Lu, Cheng, Nag, Reetoja, Sankar Viswanathan, Vidya, Gilkey, Michael, Yang, Kailin, Koyfman, Shlomo A., Chute, Deborah J., Castro, Patricia, Lewis, James S., Jr, Madabhushi, Anant, and Sandulache, Vlad C.

Published
2023
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46. Abstract 17865: Machine Learning With Multimodal Pre Ablation Imaging for Predicting Recurrence in Atrial Fibrillation Patients

Author

Midya, Abhishek, Asaeikheybari, Golnoush, Hiremath, Amogh, Viswanathan, Vidya Sankar, Sun, Han, Harwood, Samuel, Kim, Hyun Su, Schilling, Taylor, Telfer, William, Jin, Alison, Baraboo, Justin, Pradella, Maurice, Markl, Michael, Passman, Rod S, El-Harasis, Majd, Shoemaker, Ben B, Tandon, Animesh A, Barnard, John, Chung, Mina K, and Madabhushi, Anant

Published
2023
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47. The evolving use of biomarkers in heart transplantation: Consensus of an expert panel

Author

Kobashigawa, Jon, Hall, Shelly, Agbor-Enoh, Sean, Bray, Robert, Cadeiras, Martin, Chang, Patty, Chih, Sharon, Eisen, Howie, Farr, Maryjane, Fine, Barry, Ghafourian, Kambiz, Halloran, Phil, Jackson, Annette M, Kao, Andrew, Khush, Kiran K, Madabhushi, Anant, Margulies, Kenneth B, Sani, Maryam Mojarrad, Patel, Jignesh K, Patel, Nikhil, Peyster, Eliot, Parwani, Anil, Pinney, Sean, Schlendorf, Kelley, Shah, Keyur, Shah, Palak, Stehlik, Josef, Tambur, Anat, Zeevi, Adriana, Hall, Shelley, Jackson, Annette M., Khush, Kiran K., Margulies, Kenneth B., and Patel, Jignesh K.

Published
2023
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50. Oropharyngeal cancer outcomes correlate with p16 status, multinucleation and immune infiltration

Author

Wilde, David C., Castro, Patricia D., Bera, Kaustav, Lai, Syeling, Madabhushi, Anant, Corredor, German, Koyuncu, Can, Lewis, Jr, James S., Lu, Cheng, Frederick, Mitchell J., Frederick, Allan M., Haugen, Avery E., Zevallos, Jose P., Sturgis, Erich M., Shi, Justin, Huang, Andrew T., Hernandez, David J., Skinner, Heath D., Kemnade, Jan O., Yu, Wendong, Sikora, Andrew G., and Sandulache, Vlad C.

Published
2022
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