Grigorios M. Karageorgos, Sanghee Cho, E. McDonough, Chrystal Chadwick, Soumya Ghose, Jonathan R. Owens, Kyeong Joo Jung, R. Machiraju, Robert West, James D. Brooks, Parag Mallick, Fiona Ginty
{"title":"基于深度学习的自动流水线,用于多路复用前列腺癌图像中的血管检测和分布分析","authors":"Grigorios M. Karageorgos, Sanghee Cho, E. McDonough, Chrystal Chadwick, Soumya Ghose, Jonathan R. Owens, Kyeong Joo Jung, R. Machiraju, Robert West, James D. Brooks, Parag Mallick, Fiona Ginty","doi":"10.3389/fbinf.2023.1296667","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images\",\"authors\":\"Grigorios M. Karageorgos, Sanghee Cho, E. McDonough, Chrystal Chadwick, Soumya Ghose, Jonathan R. Owens, Kyeong Joo Jung, R. Machiraju, Robert West, James D. Brooks, Parag Mallick, Fiona Ginty\",\"doi\":\"10.3389/fbinf.2023.1296667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":73066,\"journal\":{\"name\":\"Frontiers in bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fbinf.2023.1296667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2023.1296667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
导言:前列腺癌是一种高度异质性疾病,具有不同程度的侵袭性和对治疗的反应。血管生成是癌症的标志之一,为肿瘤提供氧气和营养。微血管密度与较高的格里森评分和较差的预后有关。人工分割显微图像中的血管(BVs)具有挑战性,不仅耗时,而且容易造成评分者之间的差异。本研究提出了一种自动管道,用于多路复用前列腺癌图像中的血管检测和分布分析:方法:结合 CD31、CD34 和胶原蛋白 IV 图像,训练深度学习模型来分割 BV。此外,还利用训练好的模型分析了前列腺癌患者队列(N = 215)中与疾病进展相关的 BV 大小和分布模式:结果:与两位审稿人提供的地面实况注释相比,该模型能够准确检测和分割BV。与审稿人1相比,精确度(P)、召回率(R)和骰子相似系数(DSC)分别为0.93(标清0.04)、0.97(标清0.02)和0.71(标清0.07);与审稿人2相比,精确度(P)、召回率(R)和骰子相似系数(DSC)分别为0.95(标清0.05)、0.94(标清0.07)和0.70(标清0.08)。血管数量与 5 年复发有明显相关性(调整后 p = 0.0042),而血管数量和面积与 Gleason 等级有明显相关性(调整后 p 分别 = 0.032 和 0.003):所提出的方法有望简化和规范血管分析,为前列腺癌的生物学研究提供更多见解,并可广泛应用于其他癌症。
Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images
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.