(UroToday.com) The 2022 GU ASCO Annual meeting included a session on the optimization management of localized prostate cancer, specifically looking at artificial intelligence (AI), active surveillance, and intervention, featuring a presentation by Dr. Daniel Spratt discussing an AI-derived digital pathology-based biomarker to predict the benefit of ADT in localized prostate cancer with validation in NRG/RTOG 9408. The current standard of care for men with intermediate- and high-risk localized prostate cancer treated with radiotherapy is the addition of ADT.
Dr. Spratt notes that biomarkers are either prognostic or predictive. Prognostic biomarkers estimate the absolute risk of recurrence and personalize treatment through avoiding futile treatment intensification. Predictive biomarkers identify the relative impact of a given therapy and personalize treatment decisions irrespective of prognosis. Presently, there are no validated predictive biomarkers to guide ADT use or duration in such men. However, there is a wealth of unused biological information unrecognized in prostate cancer histopathology. Using AI may identify non-human interpretable features to enable the creation and validation of the first predictive biomarker to guide ADT use in localized prostate cancer.
For this study, pre-treatment biopsy slides were digitized from five phase III NRG Oncology randomized trials of men receiving radiotherapy with or without ADT. The training set to develop the AI-derived predictive biomarker included NRG/RTOG 9202, 9413, 9910, and 0126, and was trained to predict distant metastasis. A multimodal deep learning architecture was developed to learn from both clinicopathologic and digital imaging histopathology data and identify differential outcomes by treatment type. After the model was locked, an independent biostatistician performed validation on NRG/RTOG 9408, a phase III randomized trial of radiotherapy +/- 4 months of ADT.
Of note, in the NRG/RTOG 9408 trial, 55% of patients had NCCN intermediate risk disease. The distant metastasis rates were calculated using cumulative incidence functions in biomarker positive and negative groups, and biomarker-treatment interaction was assessed using Fine-Gray regression such that death without distant metastasis was treated as a competing event.
Clinical and histopathological data were available for 5,654 of 7,957 eligible patients (71.1%). The training cohort included 3,935 patients and had a median follow-up of 13.6 years (IQR 10.2, 17.7). After the AI-derived predictive ADT classifier was trained, it was validated in NRG/RTOG 9408 (n = 1719, median follow-up 17.6 years, IQR 15.0, 19.7). Importantly, the MMAI predictive model was primarily driven by digital histopathology:
In the NRG/RTOG 9408 validation cohort that had digital histopathology data, ADT significantly improved distant metastasis (HR 0.62, 95% CI 0.44, 0.87), consistent with the published trial results. The biomarker-treatment interaction was significant (p-value = 0.0021). In patients with AI-biomarker positive disease (n = 673, 39%), ADT had a greater benefit compared to radiotherapy alone (HR 0.33, 95% CI 0.19, 0.57). In the biomarker negative subgroup (n = 1046, 61%), the addition of ADT did not improve outcomes over radiotherapy alone (HR 1.00, 95% CI 0.64, 1.57). The 15-year distant metastasis rate difference between radiotherapy versus radiotherapy + ADT in the biomarker negative group was 0.4% vs biomarker positive group 9.8%. There was a consistent differential benefit seen by the biomarker score across all endpoints:
Dr. Spratt concluded his presentation of an AI-derived digital pathology-based biomarker to predict the benefit of ADT in localized prostate cancer with validation in NRG/RTOG 9408 with the following summary points:
- The ArteraAI-Predict ADT model was successfully developed using digital histopathology image data and standard clinicopathologic variables
- This study represents the successful validation in a phase III randomized trial of the first predictive biomarker of ADT benefit with radiotherapy in localized intermediate risk prostate cancer using a novel AI-derived digital pathology-based platform
- This AI-derived predictive biomarker demonstrates that a majority of patients treated with radiotherapy on NRG/RTOG 9408 did not require ADT and could have avoided the associated costs and side effects of this treatment
Presented by: Daniel E. Spratt, MD, University Hospitals Seidman Cancer Center, Cleveland, OH
Co-Authors: Yilun Sun, Douwe Van der Wal, Shih-Cheng Huang, Osama Mohamad, Andrew J. Armstrong, Jonathan David Tward, Paul Nguyen, Emmalyn Chen, Sandy DeVries, Jedidiah Mercer Monson, Holly A Campbell, Michelle J. Ferguson, Jean-Paul Bahary, Phuoc T. Tran, Joseph P. Rodgers, Andre Esteva, Felix Feng
Written by: Zachary Klaassen, MD, MSc – Urologic Oncologist, Assistant Professor of Urology, Georgia Cancer Center, Augusta University/Medical College of Georgia, @zklaassen_md on Twitter during the 2022 American Society of Clinical Oncology Genitourinary (ASCO GU) Cancers Symposium, Thursday, Feb 17 – Saturday, Feb 19, 2022