{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:02:57Z","timestamp":1775793777491,"version":"3.50.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2017,11,9]],"date-time":"2017-11-09T00:00:00Z","timestamp":1510185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NCI ITCR","award":["U01CA188547"],"award-info":[{"award-number":["U01CA188547"]}]},{"DOI":"10.13039\/100015624","name":"Leidos","doi-asserted-by":"crossref","award":["15x040"],"award-info":[{"award-number":["15x040"]}],"id":[{"id":"10.13039\/100015624","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012234","name":"Shenzhen Peacock Plan","doi-asserted-by":"crossref","award":["KQTD2016053112051497"],"award-info":[{"award-number":["KQTD2016053112051497"]}],"id":[{"id":"10.13039\/501100012234","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005749","name":"Science and Technology Project of Guangdong Province, China","doi-asserted-by":"publisher","award":["2015B010131011"],"award-info":[{"award-number":["2015B010131011"]}],"id":[{"id":"10.13039\/501100005749","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and\/or semi-quantitative parameters obtained by pathologist\u2019s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/chengjun583\/KIRP-topological-features<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx723","type":"journal-article","created":{"date-parts":[[2017,11,8]],"date-time":"2017-11-08T04:18:14Z","timestamp":1510114694000},"page":"1024-1030","source":"Crossref","is-referenced-by-count":73,"title":["Identification of topological features in renal tumor microenvironment associated with patient survival"],"prefix":"10.1093","volume":"34","author":[{"given":"Jun","family":"Cheng","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China"}]},{"given":"Xiaokui","family":"Mo","sequence":"additional","affiliation":[{"name":"Center for Biostatistics, The Ohio State University Wexner Medical Center, The Ohio State University, Columbus, OH, USA"}]},{"given":"Xusheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA"}]},{"given":"Anil","family":"Parwani","sequence":"additional","affiliation":[{"name":"Department of Pathology, The Ohio State University, Columbus, OH, USA"}]},{"given":"Qianjin","family":"Feng","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China"}]},{"given":"Kun","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA"},{"name":"Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA"},{"name":"Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,11,9]]},"reference":[{"key":"2023012712480573000_btx723-B1","first-page":"979111","article-title":"Nucleus segmentation in histology images with hierarchical multilevel thresholding","author":"Phoulady","year":"2016","journal-title":"Proceedings SPIE 9791, Medical Imaging 2016 Digital Pathology"},{"key":"2023012712480573000_btx723-B2","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TBME.2009.2035102","article-title":"Improved automatic detection and segmentation of cell nuclei in histopathology images","volume":"57","author":"Al-Kofahi","year":"2010","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"2023012712480573000_btx723-B3","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/TMI.2016.2528120","article-title":"AggNet: deep learning from crowds for mitosis detection in breast cancer histology images","volume":"35","author":"Albarqouni","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2023012712480573000_btx723-B4","doi-asserted-by":"crossref","first-page":"108ra113","DOI":"10.1126\/scitranslmed.3002564","article-title":"Systematic analysis of breast cancer morphology uncovers stromal features associated with survival","volume":"3","author":"Beck","year":"2011","journal-title":"Sci. Transl. Med"},{"key":"2023012712480573000_btx723-B5","doi-asserted-by":"crossref","first-page":"10690","DOI":"10.1038\/srep10690","article-title":"New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images","volume":"5","author":"Chen","year":"2015","journal-title":"Sci. Rep"},{"key":"2023012712480573000_btx723-B6","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1111\/j.1464-410X.2008.07756.x","article-title":"Prognostic factors in a prospective series of papillary renal cell carcinoma","volume":"102","author":"Gontero","year":"2008","journal-title":"BJU Int"},{"key":"2023012712480573000_btx723-B7","first-page":"1561","article-title":"Diagnostic Pathology: Genitourinary","volume-title":"Am. J. Surg. Pathol.","author":"Hansel","year":"2010"},{"key":"2023012712480573000_btx723-B8","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1111\/j.0006-341X.2000.00337.x","article-title":"Time-dependent ROC curves for censored survival data and a diagnostic marker","volume":"56","author":"Heagerty","year":"2000","journal-title":"Biometrics"},{"key":"2023012712480573000_btx723-B9","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1111\/j.0006-341X.2005.030814.x","article-title":"Survival model predictive accuracy and ROC curves","volume":"61","author":"Heagerty","year":"2005","journal-title":"Biometrics"},{"key":"2023012712480573000_btx723-B10","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/(SICI)1096-9896(199710)183:2<131::AID-PATH931>3.0.CO;2-G","article-title":"The Heidelberg classification of renal cell tumours","volume":"183","author":"Kovacs","year":"1997","journal-title":"J. Pathol"},{"key":"2023012712480573000_btx723-B11","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1093\/jjco\/hyp075","article-title":"Is there a role of the histologic subtypes of papillary renal cell carcinoma as a prognostic factor?","volume":"39","author":"Ku","year":"2009","journal-title":"Jpn. J. Clin. Oncol"},{"key":"2023012712480573000_btx723-B12","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1200\/JCO.2011.41.0902","article-title":"Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98","volume":"31","author":"Loi","year":"2013","journal-title":"J. Clin. Oncol"},{"key":"2023012712480573000_btx723-B13","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1002\/1097-0142(20000801)89:3<604::AID-CNCR16>3.0.CO;2-Q","article-title":"Prognostic utility of the recently recommended histologic classification and revised TNM staging system of renal cell carcinoma: a swiss experience with 588 tumors","volume":"89","author":"Moch","year":"2000","journal-title":"Cancer"},{"key":"2023012712480573000_btx723-B14","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1200\/JCO.2005.07.055","article-title":"Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience","volume":"23","author":"Patard","year":"2005","journal-title":"J. Clin. Oncol"},{"key":"2023012712480573000_btx723-B15","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1016\/0895-4356(95)00048-8","article-title":"Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates","volume":"48","author":"Peduzzi","year":"1995","journal-title":"J. Clin. Epidemiol"},{"key":"2023012712480573000_btx723-B16","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1016\/j.yexcr.2010.02.045","article-title":"Hallmarks of cancer: interactions with the tumor stroma","volume":"316","author":"Pietras","year":"2010","journal-title":"Exp. Cell Res"},{"key":"2023012712480573000_btx723-B17","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.urology.2006.09.052","article-title":"Survival analysis of 130 patients with papillary renal cell carcinoma: prognostic utility of type 1 and type 2 subclassification","volume":"69","author":"Pignot","year":"2007","journal-title":"Urology"},{"key":"2023012712480573000_btx723-B18","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.juro.2011.09.053","article-title":"Clinical and pathological features associated with prognosis in patients with papillary renal cell carcinoma","volume":"187","author":"Sukov","year":"2012","journal-title":"J. Urol"},{"key":"2023012712480573000_btx723-B19","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1038\/nature08486","article-title":"Pten in stromal fibroblasts suppresses mammary epithelial tumours","volume":"461","author":"Trimboli","year":"2009","journal-title":"Nature"},{"key":"2023012712480573000_btx723-B20","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1038\/modpathol.2012.126","article-title":"Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer","volume":"25","author":"Veta","year":"2012","journal-title":"Mod. Pathol"},{"key":"2023012712480573000_btx723-B21","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1136\/amiajnl-2012-001538","article-title":"Identifying survival associated morphological features of triple negative breast cancer using multiple datasets","volume":"20","author":"Wang","year":"2013","journal-title":"J. Am. Med. Inform. Assoc"},{"key":"2023012712480573000_btx723-B22","doi-asserted-by":"crossref","first-page":"503.","DOI":"10.1038\/srep00503","article-title":"Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach","volume":"2","author":"Wienert","year":"2012","journal-title":"Sci. Rep"},{"key":"2023012712480573000_btx723-B23","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1109\/ISBI.2014.6868041","article-title":"Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology","author":"Xu","year":"2014","journal-title":"2014 IEEE 11th International Symposium on Biomedical Imaging"},{"key":"2023012712480573000_btx723-B24","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TMI.2015.2458702","article-title":"Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology","volume":"35","author":"Xu","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2023012712480573000_btx723-B25","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1364\/BOE.2.002160","article-title":"Differential diagnosis of breast cancer using quantitative, label-free and molecular vibrational imaging","volume":"2","author":"Yang","year":"2011","journal-title":"Biomed. Opt. Express"},{"key":"2023012712480573000_btx723-B26","doi-asserted-by":"crossref","first-page":"157ra143","DOI":"10.1126\/scitranslmed.3004330","article-title":"Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling","volume":"4","author":"Yuan","year":"2012","journal-title":"Sci. Transl. Med"},{"key":"2023012712480573000_btx723-B27","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1109\/TMI.2014.2361481","article-title":"Towards large-scale histopathological image analysis: Hashing-based image retrieval","volume":"34","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2023012712480573000_btx723-B28","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.media.2015.10.005","article-title":"High-throughput histopathological image analysis via robust cell segmentation and hashing","volume":"26","author":"Zhang","year":"2015","journal-title":"Med. Image Anal"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/6\/1024\/48914911\/bioinformatics_34_6_1024.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/6\/1024\/48914911\/bioinformatics_34_6_1024.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T13:40:51Z","timestamp":1674826851000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/6\/1024\/4609352"}},"subtitle":[],"editor":[{"given":"Robert","family":"Murphy","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2017,11,9]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,3,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx723","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,3,15]]},"published":{"date-parts":[[2017,11,9]]}}}