Priti Lal, MD

Associate Scholar

  •  Director of GU Pathology, Associate Professor, Pathology and Laboratory Medicine | University of Pennsylvania
  •  Botswana
  •   Bladder and Kidney Cancer | Global Oncology | Medical Education | Prostate Cancer

Languages: English (Native), Hindi (Native)

Bio statement

As part of the CGH community, my primary interest is teaching and mentoring students from countries in Africa who have an interest in gaining a deeper understanding of the current diagnostic and molecular tools as applied to prostate, kidney, and bladder cancer. Dr. Lal is the  Director of GU Pathology, Associate Professor, Pathology and Laboratory Medicine | University of Pennsylvania. She is the lead pathologist in applying the latest technology of 3D visualization of prostate cancer, focusing on men of African descent. She is also the lead pathologist from the USA for the MADCAP consortium, a collaboration between Harvard, Moffit, Penn, and ten countries in Africa. 

Recent global health projects

Improving prognostication of prostate cancer with a focus on American men of African descent:

Despite radical prostatectomy, a subset of patients experiences biochemical recurrence (BCR) that is associated with increased cancer-related mortality. Existing tools for predicting post-prostatectomy BCR are based primarily on basic clinical and microscopic parameters and on genomic tests such as Decipher which is tissue destructive. Using computational image analysis (AI) of digitalized H&E slides from the Registry, we have described six unique high-risk features of prostate cancer gland morphology. This novel nondestructive methodology termed “Histotyping”, has similar prognostic abilities as Decipher, and when combined with grade and preoperative PSA, outperforms Decipher (2).


All current prognostication models are based on information obtained from Caucasian men even though prostate cancer follows a more aggressive course in American men of African descent (AA). We have therefore initiated studies to improve prognostic algorithms for this under-represented group. To date, gland structure is the only morphologic characteristic used for prognostication of prostate cancer, and tumor stroma has not been investigated. Using digitized slides of prostate cancers from the Registry, we developed an AI-derived, automated stromal signature termed “AAstro” specific to AA men, which outperforms standard clinical predictors such as Kattan and CAPRA-S (3). Our findings highlight that using population-specific information of stromal morphology substantially improves the accuracy of prognostication and of risk stratification for prostate cancer in American men of African descent. I am a co-patent holder of this innovative technology (US Patent: US20210035694A1).

Selected publications

1) Prostate tumors of native men from West Africa show biologically distinct pathways-A comparative genomic study. https://doi.org/10.1002/pros.24238
2) Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. DOI: 10.1158/1078-0432.CCR-19-2659