Vinnie Y T Liu
Research Subjects
Vinnie Y T Liu has a total of 72 co-authors. Here are the top co-authors by number of shared publications:
- Andre Esteva
Artera Inc, Los Altos, CA
11 shared publications - Felix Y. Feng
Department of Radiation Oncology, University of California San Francisco , San Francisco, CA
11 shared publications - Agnieszka Krzyzanowska
Department of Translational Medicine, Lund University, Lund, Sweden
10 shared publications - Alexandra Kraft
Artera Inc., Los Altos, California
10 shared publications - Anders Bjartell
Department of Urology, Skåne University Hospital, Malmö, Sweden; Department of Translational Medicine, Medical Faculty, Lund University, Lund, Sweden
10 shared publications - Emmalyn Chen
Artera Inc., Los Altos, California
10 shared publications
Latest Publications
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Demographic and clinical characteristics of the UPCA referral cohort.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Demographic and clinical characteristics of the UPCA referral cohort.
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Multimodal artificial intelligence (MMAI) biomarker score distribution by A) NCCN and B) CAPRA risk groups.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Multimodal artificial intelligence (MMAI) biomarker score distribution by A) NCCN and B) CAPRA risk groups.
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S2 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Univariable analyses of multimodal artificial intelligence (MMAI) score for biochemical recurrence (BCR) and adverse pathology (AP) at radical prostatectomy within NCCN Subgroup.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S2 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Univariable analyses of multimodal artificial intelligence (MMAI) score for biochemical recurrence (BCR) and adverse pathology (AP) at radical prostatectomy within NCCN Subgroup.
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S3 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Comparison of multimodal artificial intelligence (MMAI) score to individual clinical variables, NCCN risk groups, and CAPRA score.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S3 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Comparison of multimodal artificial intelligence (MMAI) score to individual clinical variables, NCCN risk groups, and CAPRA score.
- Top Cited Papers
- 10 November 2025
- Published by American Society of Clinical Oncology (ASCO) in Journal of Clinical Oncology
- Vol. 43 (32) , 3494-3504
Development and Validation of an Artificial Intelligence Digital Pathology Biomarker to Predict Benefit of Long-Term Hormonal Therapy and Radiotherapy in Men With High-Risk Prostate Cancer Across Multiple Phase III Trials
PURPOSE: Long-term androgen deprivation therapy (ADT) improves survival in men with high-risk localized prostate cancer (PCa) receiving radiotherapy (RT). Predictive biomarkers are needed to guide ADT duration. METHODS: A multimodal artificial intelligence (MMAI)–derived predictive biomarker was trained for long-term (LT) versus short-term (ST) ADT using pretreatment digital prostate biopsy images and clinical data (age, prostate-specific antigen, Gleason, and T stage) from six NRG Oncology phase III randomized radiotherapy trials. The novel MMAI-derived biomarker was developed to predict the differential benefit of LT-ADT on the primary end point, distant metastasis (DM). MMAI predictive utility was validated on a seventh randomized trial, RTOG 9202 (N = 1,192), which randomly assigned men to RT + ST-ADT (4 months) versus RT + LT-ADT (28 months). Fine-Gray and cumulative incidence analyses for DM, and secondarily, death with DM, were performed. Deaths without DM were treated as competing risks. RESULTS: In the validation cohort (median follow-up, 17.2 years), LT-ADT significantly improved DM from 26% to 17% (subdistribution hazard ratio [sHR], 0.64 [95% CI, 0.50 to 0.82], P < .001). A significant biomarker-treatment predictive interaction was observed ( P = .04) for DM, whereby MMAI biomarker–positive men (n = 785, 66%) had reduced DM with LT-ADT versus ST-ADT (sHR, 0.55 [95% CI, 0.41 to 0.73], P < .001), whereas no treatment benefit was observed for MMAI biomarker–negative men (n = 407; sHR, 1.06 [95% CI, 0.61 to 1.84], P = .84). The estimated 15-year DM risk difference between RT + LT-ADT and RT + ST-ADT was 14% in MMAI biomarker–positive men and 0% in MMAI biomarker–negative men. The MMAI biomarker was also prognostic for DM, irrespective of treatment (sHR, 2.35 [95% CI, 1.72 to 3.19], P < .001). CONCLUSION: To our knowledge, the MMAI model is the first validated predictive biomarker to guide ADT duration with RT in localized/locally advanced PCa. Approximately one third of men with high-risk PCa could safely be spared the additional 24 months of ADT and the associated morbidity.
- 10 November 2025
- Published by American Society of Clinical Oncology (ASCO) in Journal of Clinical Oncology
- Vol. 43 (32) , 3494-3504
Top Cited PapersDevelopment and Validation of an Artificial Intelligence Digital Pathology Biomarker to Predict Benefit of Long-Term Hormonal Therapy and Radiotherapy in Men With High-Risk Prostate Cancer Across Multiple Phase III Trials
PURPOSE: Long-term androgen deprivation therapy (ADT) improves survival in men with high-risk localized prostate cancer (PCa) receiving radiotherapy (RT). Predictive biomarkers are needed to guide ADT duration. METHODS: A multimodal artificial intelligence (MMAI)–derived predictive biomarker was trained for long-term (LT) versus short-term (ST) ADT using pretreatment digital prostate biopsy images and clinical data (age, prostate-specific antigen, Gleason, and T stage) from six NRG Oncology phase III randomized radiotherapy trials. The novel MMAI-derived biomarker was developed to predict the differential benefit of LT-ADT on the primary end point, distant metastasis (DM). MMAI predictive utility was validated on a seventh randomized trial, RTOG 9202 (N = 1,192), which randomly assigned men to RT + ST-ADT (4 months) versus RT + LT-ADT (28 months). Fine-Gray and cumulative incidence analyses for DM, and secondarily, death with DM, were performed. Deaths without DM were treated as competing risks. RESULTS: In the validation cohort (median follow-up, 17.2 years), LT-ADT significantly improved DM from 26% to 17% (subdistribution hazard ratio [sHR], 0.64 [95% CI, 0.50 to 0.82], P < .001). A significant biomarker-treatment predictive interaction was observed ( P = .04) for DM, whereby MMAI biomarker–positive men (n = 785, 66%) had reduced DM with LT-ADT versus ST-ADT (sHR, 0.55 [95% CI, 0.41 to 0.73], P < .001), whereas no treatment benefit was observed for MMAI biomarker–negative men (n = 407; sHR, 1.06 [95% CI, 0.61 to 1.84], P = .84). The estimated 15-year DM risk difference between RT + LT-ADT and RT + ST-ADT was 14% in MMAI biomarker–positive men and 0% in MMAI biomarker–negative men. The MMAI biomarker was also prognostic for DM, irrespective of treatment (sHR, 2.35 [95% CI, 1.72 to 3.19], P < .001). CONCLUSION: To our knowledge, the MMAI model is the first validated predictive biomarker to guide ADT duration with RT in localized/locally advanced PCa. Approximately one third of men with high-risk PCa could safely be spared the additional 24 months of ADT and the associated morbidity.
- Open Access
- 3 June 2025
- Published by Elsevier in The Lancet Digital Health
- Vol. 7 (7) , 100885
External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol
- 3 June 2025
- Published by Elsevier in The Lancet Digital Health
- Vol. 7 (7) , 100885
Open AccessExternal validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol
- Open Access
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Supplementary Table S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Demographic and clinical characteristics of the UPCA referral cohort.
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Demographic and clinical characteristics of the UPCA referral cohort.
- Open Access
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Supplementary Table S2 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Univariable analyses of multimodal artificial intelligence (MMAI) score for biochemical recurrence (BCR) and adverse pathology (AP) at radical prostatectomy within NCCN Subgroup.
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S2 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Univariable analyses of multimodal artificial intelligence (MMAI) score for biochemical recurrence (BCR) and adverse pathology (AP) at radical prostatectomy within NCCN Subgroup.
- Open Access
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Data from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Purpose:A multimodal artificial intelligence (MMAI) biomarker was developed using clinical trial data from North American men with localized prostate cancer treated with definitive radiation, using biopsy digital pathology images and key clinical information (age, PSA, and T-stage) to generate prognostic scores. This study externally validates the biomarker in a prospective, real-world dataset of men who underwent radical prostatectomy (RP) for localized prostate cancer at a tertiary referral center in Sweden.Experimental Design:Association between the MMAI scores (continuous and categorical) and endpoints of interest was assessed with Fine–Gray and cumulative incidence analyses for biochemical recurrence (BCR) and logistic regression for adverse pathology (AP) at RP.Results:The analysis included 143 patients with evaluable biopsy pathology images and complete clinical data to generate MMAI scores. The median follow-up was 8.8 years. At diagnosis, the median PSA was 7.5 ng/mL, the median age was 64 years, 29% had a Gleason grade group ≥3, and 88 men were evaluable for AP at RP. MMAI was significantly associated with BCR [subdistribution HR, 2.45; 95% confidence interval (CI), 1.77–3.38; P < 0.001] and AP at RP (OR, 4.85; 95% CI, 2.54–10.78; P < 0.001). Estimated 5-year BCR rates for MMAI intermediate to high versus low were 25% (95% CI, 16%–36%) versus 4% (95% CI, 1%–11%), respectively.Conclusions:The MMAI biomarker, previously shown to be prognostic for distant metastasis and prostate cancer–specific mortality in men receiving definitive radiation, was prognostic for post-RP endpoints: BCR and AP. This biomarker validation study further supports the use of MMAI biomarkers in men with prostate cancer outside North America and those treated with RP.
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Open AccessData from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Purpose:A multimodal artificial intelligence (MMAI) biomarker was developed using clinical trial data from North American men with localized prostate cancer treated with definitive radiation, using biopsy digital pathology images and key clinical information (age, PSA, and T-stage) to generate prognostic scores. This study externally validates the biomarker in a prospective, real-world dataset of men who underwent radical prostatectomy (RP) for localized prostate cancer at a tertiary referral center in Sweden.Experimental Design:Association between the MMAI scores (continuous and categorical) and endpoints of interest was assessed with Fine–Gray and cumulative incidence analyses for biochemical recurrence (BCR) and logistic regression for adverse pathology (AP) at RP.Results:The analysis included 143 patients with evaluable biopsy pathology images and complete clinical data to generate MMAI scores. The median follow-up was 8.8 years. At diagnosis, the median PSA was 7.5 ng/mL, the median age was 64 years, 29% had a Gleason grade group ≥3, and 88 men were evaluable for AP at RP. MMAI was significantly associated with BCR [subdistribution HR, 2.45; 95% confidence interval (CI), 1.77–3.38; P < 0.001] and AP at RP (OR, 4.85; 95% CI, 2.54–10.78; P < 0.001). Estimated 5-year BCR rates for MMAI intermediate to high versus low were 25% (95% CI, 16%–36%) versus 4% (95% CI, 1%–11%), respectively.Conclusions:The MMAI biomarker, previously shown to be prognostic for distant metastasis and prostate cancer–specific mortality in men receiving definitive radiation, was prognostic for post-RP endpoints: BCR and AP. This biomarker validation study further supports the use of MMAI biomarkers in men with prostate cancer outside North America and those treated with RP.
- Open Access
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Multimodal artificial intelligence (MMAI) biomarker score distribution by A) NCCN and B) CAPRA risk groups.
- 13 May 2025
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S1 from Validation of a Digital Pathology–Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy
Multimodal artificial intelligence (MMAI) biomarker score distribution by A) NCCN and B) CAPRA risk groups.