Terry Hyslop

Positions:

Professor of Biostatistics & Bioinformatics

Biostatistics & Bioinformatics
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2001

Temple University

Grants:

Combined breast MRI/biomarker strategies to identify aggressive biology

Administered By
Integrative Genomics
Role
Principal Investigator
Start Date
End Date

Combined breast MRI/biomarker strategies to identify aggressive biology

Administered By
Medicine, Medical Oncology
Awarded By
National Institutes of Health
Role
Biostatistician
Start Date
End Date

Tension-Stat3-miR-mediated metastasis

Administered By
Medicine, Medical Oncology
Awarded By
University of California - San Francisco
Role
Biostatistician
Start Date
End Date

Smartphone Enabled Point-of-Care Detection of Serum Markers of Liver Cancer

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Smartphone Enabled Point-of-Care Detection of Serum Markers of Liver Cancer

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Publications:

Serial Analysis of Circulating Tumor Cells in Metastatic Breast Cancer Receiving First-Line Chemotherapy.

BACKGROUND: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy. METHODS: Serial CTC data from 469 patients (2202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs, combined CTC status at baseline to the end of cycle 1, and tCTC. Akaike information criterion was used to select the model that best predicted PFS and OS. RESULTS: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (56.9% ), low (23.7%), intermediate (14.5%), or high (4.9%). Patients with low, intermediate, and high tCTC patterns had statistically significant inferior PFS and OS compared with those with undetectable CTCs (P < .001). Akaike Information Criterion indicated that the tCTC model best predicted PFS and OS compared with baseline CTCs and combined CTC status at baseline to the end of cycle 1 models. Validation studies in an independent cohort of 1856 MBC patients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model. CONCLUSIONS: We identified 4 novel prognostic groups in MBC based on similarities in tCTC patterns during chemotherapy. Prognostic groups included patients with very poor outcome (intermediate + high CTCs, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be used for fine-tuning of CTC-based risk stratification strategies to guide future prospective clinical trials in MBC.
Authors
Magbanua, MJM; Hendrix, LH; Hyslop, T; Barry, WT; Winer, EP; Hudis, C; Toppmeyer, D; Carey, LA; Partridge, AH; Pierga, J-Y; Fehm, T; Vidal-Martínez, J; Mavroudis, D; Garcia-Saenz, JA; Stebbing, J; Gazzaniga, P; Manso, L; Zamarchi, R; Antelo, ML; Mattos-Arruda, LD; Generali, D; Caldas, C; Munzone, E; Dirix, L; Delson, AL; Burstein, HJ; Qadir, M; Ma, C; Scott, JH; Bidard, F-C; Park, JW; Rugo, HS
MLA Citation
Magbanua, Mark Jesus M., et al. “Serial Analysis of Circulating Tumor Cells in Metastatic Breast Cancer Receiving First-Line Chemotherapy.J Natl Cancer Inst, vol. 113, no. 4, Apr. 2021, pp. 443–52. Pubmed, doi:10.1093/jnci/djaa113.
URI
https://scholars.duke.edu/individual/pub1454271
PMID
32770247
Source
pubmed
Published In
J Natl Cancer Inst
Volume
113
Published Date
Start Page
443
End Page
452
DOI
10.1093/jnci/djaa113

Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.

BACKGROUND. The incidence of ductal carcinoma in situ (DCIS) has steadily increased, as have concerns regarding overtreatment. Active surveillance is a novel treatment strategy that avoids surgical excision, but identifying patients with occult invasive disease who should be excluded from active surveillance is challenging. Radiologists are not typically expected to predict the upstaging of DCIS to invasive disease, though they might be trained to perform this task. OBJECTIVE. The purpose of this study was to determine whether a mixed-methods two-stage observer study can improve radiologists' ability to predict upstaging of DCIS to invasive disease on mammography. METHODS. All cases of DCIS calcifications that underwent stereotactic biopsy between 2010 and 2015 were identified. Two cohorts were randomly generated, each containing 150 cases (120 pure DCIS cases and 30 DCIS cases upstaged to invasive disease at surgery). Nine breast radiologists reviewed the mammograms in the first cohort in a blinded fashion and scored the probability of upstaging to invasive disease. The radiologists then reviewed the cases and results collectively in a focus group to develop consensus criteria that could improve their ability to predict upstaging. The radiologists reviewed the mammograms from the second cohort in a blinded fashion and again scored the probability of upstaging. Statistical analysis compared the performances between rounds 1 and 2. RESULTS. The mean AUC for reader performance in predicting upstaging in round 1 was 0.623 (range, 0.514-0.684). In the focus group, radiologists agreed that upstaging was better predicted when an associated mass, asymmetry, or architectural distortion was present; when densely packed calcifications extended over a larger area; and when the most suspicious features were focused on rather than the most common features. Additionally, radiologists agreed that BI-RADS descriptors do not adequately characterize risk of invasion, and that microinvasive disease and smaller areas of DCIS will have poor prediction estimates. Reader performance significantly improved in round 2 (mean AUC, 0.765; range, 0.617-0.852; p = .045). CONCLUSION. A mixed-methods two-stage observer study identified factors that helped radiologists significantly improve their ability to predict upstaging of DCIS to invasive disease. CLINICAL IMPACT. Breast radiologists can be trained to better predict upstaging of DCIS to invasive disease, which may facilitate discussions with patients and referring providers.
Authors
Grimm, LJ; Neely, B; Hou, R; Selvakumaran, V; Baker, JA; Yoon, SC; Ghate, SV; Walsh, R; Litton, TP; Devalapalli, A; Kim, C; Soo, MS; Hyslop, T; Hwang, ES; Lo, JY
MLA Citation
Grimm, Lars J., et al. “Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.Ajr Am J Roentgenol, vol. 216, no. 4, Apr. 2021, pp. 903–11. Pubmed, doi:10.2214/AJR.20.23679.
URI
https://scholars.duke.edu/individual/pub1456309
PMID
32783550
Source
pubmed
Published In
Ajr. American Journal of Roentgenology
Volume
216
Published Date
Start Page
903
End Page
911
DOI
10.2214/AJR.20.23679

Abstract GS2-05: Microscaled proteogenomic methods for precision oncology

Authors
Satpathy, S; Jaehnig, E; Karsten, K; Kim, B-J; Saltzman, A; Chan, D; Holloway, K; Anurag, M; Huang, C; Singh, P; Gao, A; Namai, N; Dou, Y; Wen, B; Vasaikar, S; Mutch, D; Watson, M; Ma, C; Ademuyiwa, F; Rimawi, M; Hoog, J; Jacobs, S; Malovannaya, A; Hyslop, T; Mani, DR; Perou, C; Miles, G; Zhang, B; Gillette, M; Carr, S; Ellis, M
MLA Citation
Satpathy, Shankha, et al. “Abstract GS2-05: Microscaled proteogenomic methods for precision oncology.” General Session Abstracts, American Association for Cancer Research, 2020. Crossref, doi:10.1158/1538-7445.sabcs19-gs2-05.
URI
https://scholars.duke.edu/individual/pub1445224
Source
crossref
Published In
General Session Abstracts
Published Date
DOI
10.1158/1538-7445.sabcs19-gs2-05

Implementation and Impact of a Risk-Stratified Prostate Cancer Screening Algorithm as a Clinical Decision Support Tool in a Primary Care Network.

BACKGROUND: Implementation methods of risk-stratified cancer screening guidance throughout a health care system remains understudied. OBJECTIVE: Conduct a preliminary analysis of the implementation of a risk-stratified prostate cancer screening algorithm in a single health care system. DESIGN: Comparison of men seen pre-implementation (2/1/2016-2/1/2017) vs. post-implementation (2/2/2017-2/21/2018). PARTICIPANTS: Men, aged 40-75 years, without a history of prostate cancer, who were seen by a primary care provider. INTERVENTIONS: The algorithm was integrated into two components in the electronic health record (EHR): in Health Maintenance as a personalized screening reminder and in tailored messages to providers that accompanied prostate-specific antigen (PSA) results. MAIN MEASURES: Primary outcomes: percent of men who met screening algorithm criteria; percent of men with a PSA result. Logistic repeated measures mixed models were used to test for differences in the proportion of individuals that met screening criteria in the pre- and post-implementation periods with age, race, family history, and PSA level included as covariates. KEY RESULTS: During the pre- and post-implementation periods, 49,053 and 49,980 men, respectively, were seen across 26 clinics (20.6% African American). The proportion of men who met screening algorithm criteria increased from 49.3% (pre-implementation) to 68.0% (post-implementation) (p < 0.001); this increase was observed across all races, age groups, and primary care clinics. Importantly, the percent of men who had a PSA did not change: 55.3% pre-implementation, 55.0% post-implementation. The adjusted odds of meeting algorithm-based screening was 6.5-times higher in the post-implementation period than in the pre-implementation period (95% confidence interval, 5.97 to 7.05). CONCLUSIONS: In this preliminary analysis, following implementation of an EHR-based algorithm, we observed a rapid change in practice with an increase in screening in higher-risk groups balanced with a decrease in screening in low-risk groups. Future efforts will evaluate costs and downstream outcomes of this strategy.
Authors
Shah, A; Polascik, TJ; George, DJ; Anderson, J; Hyslop, T; Ellis, AM; Armstrong, AJ; Ferrandino, M; Preminger, GM; Gupta, RT; Lee, WR; Barrett, NJ; Ragsdale, J; Mills, C; Check, DK; Aminsharifi, A; Schulman, A; Sze, C; Tsivian, E; Tay, KJ; Patierno, S; Oeffinger, KC; Shah, K
MLA Citation
Shah, Anand, et al. “Implementation and Impact of a Risk-Stratified Prostate Cancer Screening Algorithm as a Clinical Decision Support Tool in a Primary Care Network.J Gen Intern Med, vol. 36, no. 1, 2021, pp. 92–99. Pubmed, doi:10.1007/s11606-020-06124-2.
URI
https://scholars.duke.edu/individual/pub1441099
PMID
32875501
Source
pubmed
Published In
J Gen Intern Med
Volume
36
Published Date
Start Page
92
End Page
99
DOI
10.1007/s11606-020-06124-2

Abstract P4-05-03: Mutational analysis of triple-negative breast cancer (TNBC): CALGB 40603 (Alliance)

Authors
Hoadley, KA; Powell, BC; Kanavy, D; Marron, D; Mose, LE; Hyslop, T; Berry, DA; Hahn, O; Tolaney, SM; Sikov, WM; Perou, CM; Carey, LA
MLA Citation
Hoadley, Katherine A., et al. “Abstract P4-05-03: Mutational analysis of triple-negative breast cancer (TNBC): CALGB 40603 (Alliance).” Poster Session Abstracts, American Association for Cancer Research, 2020. Crossref, doi:10.1158/1538-7445.sabcs19-p4-05-03.
URI
https://scholars.duke.edu/individual/pub1445225
Source
crossref
Published In
Poster Session Abstracts
Published Date
DOI
10.1158/1538-7445.sabcs19-p4-05-03

Research Areas:

Breast Neoplasms
Cancer Disparities
Case-Control Studies
Cohort Studies
Colorectal Neoplasms
Gastrointestinal Hormones
Gastrointestinal Tract
Health Disparities
Lung Neoplasms
Models, Statistical
Neoplasm Invasiveness
Prognosis
Socioeconomic Factors
Spatial analysis (Statistics)
Survival Analysis
Urogenital System