Wen Foo

Positions:

Associate Professor of Pathology

Pathology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 2008

Duke University School of Medicine

Anatomic and Clinical Pathology, Pathology

Duke University School of Medicine

Cytopathology Fellowship, Pathology

Harvard Medical School

Grants:

Publications:

Structured approach to resolving discordance between PI-RADS v2.1 score and targeted prostate biopsy results: an opportunity for quality improvement.

BACKGROUND: Prostate multiparametric magnetic resonance imaging (mpMRI) can identify lesions within the prostate with characteristics identified in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 associated with clinically significant prostate cancer (csPCa) or Gleason grade group (GGG) ≥ 2 at biopsy. OBJECTIVE: To assess concordance (PI-RADS 5 lesions with csPCa) of PI-RADS v2/2.1 with targeted, fusion biopsy results and to examine causes of discordance (PI-RADS 5 lesions without csPCa) with aim to provide a structured approach to resolving discordances and develop quality improvement (QI) protocols. METHODS: A retrospective study of 392 patients who underwent mpMRI at 3 Tesla followed by fusion biopsy. PI-RADS v2/2.1 scores were assigned to lesions identified on mpMRI and compared to biopsy results expressed as GGG. Positive predictive value (PPV) of PI-RADS v2/2.1 was calculated for all prostate cancer and csPCa. Discordant cases were re-reviewed by a radiologist with expertise in prostate mpMRI to determine reason for discordance. RESULTS: A total of 521 lesions were identified on mpMRI. 121/521 (23.2%), 310/524 (59.5%), and 90/521 (17.3%) were PI-RADS 5, 4, and 3, respectively. PPV of PI-RADS 5, 4, and 3 for all PCa and csPCa was 0.80, 0.55, 0.24 and 0.63, 0.33, and 0.09, respectively. 45 cases of discordant biopsy results for PI-RADS 5 lesions were found with 27 deemed "true" discordances or "unresolved" discordances where imaging re-review confirmed PI-RADS appropriateness, while 18 were deemed "false" or resolved discordances due to downgrading of PI-RADS scores based on imaging re-review. Adjusting for resolved discordances on re-review, the PPV of PI-RADS 5 lesions for csPCa was deemed to be 0.74 and upon adjusting for presence of csPCa found in cases of unresolved discordance, PPV rose to 0.83 for PI-RADS 5 lesions. CONCLUSION: Although PIRADS 5 lesions are considered high risk for csPCa, the PPV is not 100% and a diagnostic dilemma occurs when targeted biopsy returns discordant. While PI-RADS score is downgraded in some cases upon imaging re-review, a number of "false" or "unresolved" discordances were identified in which MRI re-review confirmed initial PI-RADS score and subsequent pathology confirmed presence of csPCa in these lesions. CLINICAL IMPACT: We propose a structured approach to resolving discordant biopsy results using multi-disciplinary re-review of imaging and archived biopsy strikes as a quality improvement pathway. Further work is needed to determine the value of re-biopsy in cases of unresolved discordance and to develop robust QI systems for prostate MRI.
Authors
Arcot, R; Sekar, S; Kotamarti, S; Krischak, M; Michael, ZD; Foo, W-C; Huang, J; Polascik, TJ; Gupta, RT
MLA Citation
Arcot, Rohith, et al. “Structured approach to resolving discordance between PI-RADS v2.1 score and targeted prostate biopsy results: an opportunity for quality improvement.Abdom Radiol (Ny), vol. 47, no. 8, Aug. 2022, pp. 2917–27. Pubmed, doi:10.1007/s00261-022-03562-w.
URI
https://scholars.duke.edu/individual/pub1523848
PMID
35674785
Source
pubmed
Published In
Abdom Radiol (Ny)
Volume
47
Published Date
Start Page
2917
End Page
2927
DOI
10.1007/s00261-022-03562-w

Teaching interventional cytopathology

Interventional cytopathology is a unique area of pathology, where cytopathologists play a primary role in obtaining fine needle aspiration biopsies and/or making determinations through rapid on-site evaluations to guide sample procurement in real-time. Unsurprisingly, experience and skill are directly related to success in these endeavors, and both can be fostered with formal instruction. There is a wealth of resources available to aid in teaching interventional cytopathology, including instructional videos, courses, and model phantoms which can help to build familiarity and confidence. These tools can provide a basic framework upon which skills can be developed through in-person guidance, real-time feedback and practice. This article reviews the tools available to enhance training, details the authors’ institutional experience in teaching interventional cytopathology at a tertiary care center, and provides recommendations and pearls for success in this endeavor.
Authors
MLA Citation
Jiang, X. S., and W. C. Foo. “Teaching interventional cytopathology.” Seminars in Diagnostic Pathology, Jan. 2022. Scopus, doi:10.1053/j.semdp.2022.01.002.
URI
https://scholars.duke.edu/individual/pub1507548
Source
scopus
Published In
Seminars in Diagnostic Pathology
Published Date
DOI
10.1053/j.semdp.2022.01.002

A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy.

CONTEXT.—: Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist-deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system. OBJECTIVE.—: To develop a novel and efficient hybrid human-machine learning approach to screen prostate biopsies. DESIGN.—: We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface. RESULTS.—: Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review. CONCLUSIONS.—: This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.
Authors
Dov, D; Assaad, S; Syedibrahim, A; Bell, J; Huang, J; Madden, J; Bentley, R; McCall, S; Henao, R; Carin, L; Foo, W-C
MLA Citation
Dov, David, et al. “A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy.Arch Pathol Lab Med, vol. 146, no. 6, June 2022, pp. 727–34. Pubmed, doi:10.5858/arpa.2020-0850-OA.
URI
https://scholars.duke.edu/individual/pub1498438
PMID
34591085
Source
pubmed
Published In
Arch Pathol Lab Med
Volume
146
Published Date
Start Page
727
End Page
734
DOI
10.5858/arpa.2020-0850-OA

Characterization of a castrate-resistant prostate cancer xenograft derived from a patient of West African ancestry.

BACKGROUND: Prostate cancer is a clinically and molecularly heterogeneous disease, with highest incidence and mortality among men of African ancestry. To date, prostate cancer patient-derived xenograft (PCPDX) models to study this disease have been difficult to establish because of limited specimen availability and poor uptake rates in immunodeficient mice. Ancestrally diverse PCPDXs are even more rare, and only six PCPDXs from self-identified African American patients from one institution were recently made available. METHODS: In the present study, we established a PCPDX from prostate cancer tissue from a patient of estimated 90% West African ancestry with metastatic castration resistant disease, and characterized this model's pathology, karyotype, hotspot mutations, copy number, gene fusions, gene expression, growth rate in normal and castrated mice, therapeutic response, and experimental metastasis. RESULTS: This PCPDX has a mutation in TP53 and loss of PTEN and RB1. We have documented a 100% take rate in mice after thawing the PCPDX tumor from frozen stock. The PCPDX is castrate- and docetaxel-resistant and cisplatin-sensitive, and has gene expression patterns associated with such drug responses. After tail vein injection, the PCPDX tumor cells can colonize the lungs of mice. CONCLUSION: This PCPDX, along with others that are established and characterized, will be useful pre-clinically for studying the heterogeneity of prostate cancer biology and testing new therapeutics in models expected to be reflective of the clinical setting.
Authors
Patierno, BM; Foo, W-C; Allen, T; Somarelli, JA; Ware, KE; Gupta, S; Wise, S; Wise, JP; Qin, X; Zhang, D; Xu, L; Li, Y; Chen, X; Inman, BA; McCall, SJ; Huang, J; Kittles, RA; Owzar, K; Gregory, S; Armstrong, AJ; George, DJ; Patierno, SR; Hsu, DS; Freedman, JA
MLA Citation
Patierno, Brendon M., et al. “Characterization of a castrate-resistant prostate cancer xenograft derived from a patient of West African ancestry.Prostate Cancer Prostatic Dis, 2021. Pubmed, doi:10.1038/s41391-021-00460-y.
URI
https://scholars.duke.edu/individual/pub1466481
PMID
34645983
Source
pubmed
Published In
Prostate Cancer Prostatic Dis
Published Date
DOI
10.1038/s41391-021-00460-y

Prostate Cancer Detection Using 3-D Shear Wave Elasticity Imaging.

Transrectal ultrasound (TRUS) B-mode imaging provides insufficient sensitivity and specificity for prostate cancer (PCa) targeting when used for biopsy guidance. Shear wave elasticity imaging (SWEI) is an elasticity imaging technique that has been commercially implemented and is sensitive and specific for PCa. We have developed a SWEI system capable of 3-D data acquisition using a dense acoustic radiation force (ARF) push approach that leads to enhanced shear wave signal-to-noise ratio compared with that of the commercially available SWEI systems and facilitates screening of the entire gland before biopsy. Additionally, we imaged and assessed 36 patients undergoing radical prostatectomy using 3-D SWEI and determined a shear wave speed threshold separating PCa from healthy prostate tissue with sensitivities and specificities akin to those for multiparametric magnetic resonance imaging fusion biopsy. The approach measured the mean shear wave speed in each prostate region to be 4.8 m/s (Young's modulus E = 69.1 kPa) in the peripheral zone, 5.3 m/s (E = 84.3 kPa) in the central gland and 6.0 m/s (E = 108.0 kPa) for PCa with statistically significant (p < 0.0001) differences among all regions. Three-dimensional SWEI receiver operating characteristic analyses identified a threshold of 5.6 m/s (E = 94.1 kPa) to separate PCa from healthy tissue with a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of 81%, 82%, 69%, 89% and 0.84, respectively. Additionally, a shear wave speed ratio was assessed to normalize for tissue compression and patient variability, which yielded a threshold of 1.11 to separate PCa from healthy prostate tissue and was accompanied by a substantial increase in specificity, PPV and AUC, where the sensitivity, specificity, PPV, NPV and AUC were 75%, 90%, 79%, 88% and 0.90, respectively. This work illustrates the feasibility of using 3-D SWEI data to detect and localize PCa and demonstrates the benefits of normalizing for applied compression during data acquisition for use in biopsy targeting studies.
Authors
Morris, DC; Chan, DY; Palmeri, ML; Polascik, TJ; Foo, W-C; Nightingale, KR
MLA Citation
Morris, D. Cody, et al. “Prostate Cancer Detection Using 3-D Shear Wave Elasticity Imaging.Ultrasound Med Biol, vol. 47, no. 7, July 2021, pp. 1670–80. Pubmed, doi:10.1016/j.ultrasmedbio.2021.02.006.
URI
https://scholars.duke.edu/individual/pub1478493
PMID
33832823
Source
pubmed
Published In
Ultrasound Med Biol
Volume
47
Published Date
Start Page
1670
End Page
1680
DOI
10.1016/j.ultrasmedbio.2021.02.006