Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images. Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215). Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively). Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.
Frontiers in bioinformatics. 2024 Jan 23*** epublish ***
Grigorios M Karageorgos, Sanghee Cho, Elizabeth McDonough, Chrystal Chadwick, Soumya Ghose, Jonathan Owens, Kyeong Joo Jung, Raghu Machiraju, Robert West, James D Brooks, Parag Mallick, Fiona Ginty
GE Research, Niskayuna, NY, United States., Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, United States., Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States., Department of Urology, Stanford University School of Medicine, Stanford, CA, United States., Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States.