Marisol Macarena Palominos-Rivera
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Marisol Macarena Palominos-Rivera has a total of 10 co-authors. Here are the top co-authors by number of 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 - Andre Esteva
Artera Inc, Los Altos, CA
10 shared publications - Emmalyn Chen
Artera Inc., Los Altos, California
10 shared publications - Felix Y. Feng
Department of Radiation Oncology, University of California San Francisco , San Francisco, CA
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.
- 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.
- Open Access
- 13 May 2025
- 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.
- 13 May 2025
- 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.
- Open Access
- 21 February 2025
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 31 (8) , 1546-1553
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.
- 21 February 2025
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 31 (8) , 1546-1553
Open AccessValidation 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.