Jarey H. Wang , Matthew P. Deek , Adrianna A. Mendes , Yang Song , Amol Shetty , Soha Bazyar , Kim Van der Eecken , Emmalyn Chen , Timothy N. Showalter , Trevor J. Royce , Tamara Todorovic , Huei-Chung Huang , Scott A. Houck , Rikiya Yamashita , Ana P. Kiess , Daniel Y. Song , Tamara Lotan , Theodore DeWeese , Luigi Marchionni , Lei Ren , Piet Ost
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引用次数: 0
Abstract
Background
There is a need for clinically actionable prognostic and predictive tools to guide the management of oligometastatic castration-sensitive prostate cancer (omCSPC).
Methods
This is a multicenter retrospective study to assess the prognostic and predictive performance of a multimodal artificial intelligence biomarker (MMAI; the ArteraAI Prostate Test) in men with omCSPC (n = 222). The cohort also included 51 patients from the STOMP and ORIOLE phase 2 clinical trials which randomized patients to observation versus metastasis-directed therapy (MDT). MMAI scores were computed from digitized histopathology slides and clinical variables. Overall survival (OS) and time to castration-resistant prostate cancer (TTCRPC) were assessed for the entire cohort from time of diagnosis. Metastasis free survival (MFS) was assessed for the trial cohort from time of randomization.
Results
In the overall cohort, patients with a high MMAI score had significantly worse OS (HR = 6.46, 95 % CI = 1.44–28.9; p = 0.01) and shorter TTCRPC (HR = 2.07, 95 % CI = 1.15–3.72; p = 0.015). In a multivariable Cox model, MMAI score remained the only variable significantly associated with OS (HR = 6.51, 95 % CI = 1.32–32.2; p = 0.02). In the subset of patients randomized in the STOMP and ORIOLE trials, high MMAI score corresponded to improved MFS with MDT (p = 0.039) compared to patients with a low score, with pinteraction = 0.04.
Conclusion
The ArteraAI MMAI biomarker is prognostic for OS and TTCRPC among patients with omCSPC and may predict for response to MDT. Further work is needed to validate the MMAI biomarker in a broader mCSPC cohort.
期刊介绍：
Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.
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