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Khalid Abdul Jabbar on Muck Rack

Khalid Abdul Jabbar

As seen in: Nature, BMJ Open, MDPI, Texas Medical Center
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Articles

QuTILs: open-source image-based infiltrating immune cell detection for research application

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By Mark Vater, Roberto Salgado, Heather Lefebvre, Margaret E. Gatti-Mays, Hope Rugo, Karla A. Ballman, Mark Watson, Yujia Wen, Roberto A. Leon-Ferre, Khalid Niazi, W. Fraser Symmans, Lisa Carey, Khalid Abdul Jabbar, Zaibo Li, Daniel G. Stover, Elijah Blige, Ava Strahan
| Nature Verified
Abstract Stromal tumor infiltrating lymphocytes (sTILs), quantified via hematoxylin and eosin (H&E) stained histologic slides of tumor tissue, are associated with improved response to chemotherapy and better overall survival in triple-negative breast cancer (TNBC).
Open in Who Shared Issue with article?

AI-powered and manual assessment of tumor-infiltrating lymphocytes in early HER2-positive breast cancer in NSABP B-41

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By Ilana Schlam, Gong Tang, Khalid Abdul Jabbar, Haixi Yan, Xuelin Gu, Tanner Freeman, Brent Harris, Priya Rastogi, Isaac Hook, Norman Wolmark, Roberto Salgado, Sandra Swain, Sai Maley
| Nature Verified
Abstract The assessment of tumor-infiltrating lymphocyte (TILs), together with gene expression signatures (GES), has the potential to guide personalized breast cancer therapy. We included 262 patients from the phase III NSABP B-41 trial, which evaluated neoadjuvant HER2-targeted therapies in combination with chemotherapy. We conducted a manual and artificial intelligence (AI)-based analyses of TILs, as well as GES from RNA sequencing.
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"Bridging Clinic and Wildlife Care With AI-Powered Pan-Species Computat" by Khalid AbdulJabbar, Simon P Castillo et al.

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By Khalid Abdul Jabbar
| Texas Medical Center
Abstract Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples.
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