{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T23:21:16Z","timestamp":1775344876310,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDP\/00776\/2020"],"award-info":[{"award-number":["UIDP\/00776\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient\u2019s medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/jimaging7080148","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T21:27:40Z","timestamp":1629235660000},"page":"148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer"],"prefix":"10.3390","volume":"7","author":[{"given":"Laura","family":"Provid\u00eancia","sequence":"first","affiliation":[{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, 4169-007 Porto, Portugal"},{"name":"Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Centre (CI-IPOP), 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2334-7280","authenticated-orcid":false,"given":"In\u00eas","family":"Domingues","sequence":"additional","affiliation":[{"name":"Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Centre (CI-IPOP), 4200-072 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Santos","sequence":"additional","affiliation":[{"name":"Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Centre (CI-IPOP), 4200-072 Porto, Portugal"},{"name":"Instituto de Ci\u00eancia Biom\u00e9dicas Abel Salazar, Rua de Jorge Viterbo Ferreira n\u00ba 228, 4050-313 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.beem.2008.01.011","article-title":"Mechanisms of bone metastasis in prostate cancer: Clinical implications","volume":"22","author":"Msaouel","year":"2008","journal-title":"Best Pract. Res. Clin. Endocrinol. Metabol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1053\/hp.2000.6698","article-title":"Metastatic patterns of prostate cancer: An autopsy study of 1589 patients","volume":"31","author":"Bubendorf","year":"2000","journal-title":"Hum. Pathol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gandaglia, G., Abdollah, F., Schiffmann, J., Trudeau, V., Shariat, S., Kim, S., Perrotte, P., Montorsi, F., Briganti, A., and Trinh, Q.D. (2014). Distribution of Metastatic Sites in Patients With Prostate Cancer: A Population-Based Analysis. Prostate, 74.","DOI":"10.1002\/pros.22742"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.juro.2010.03.034","article-title":"Skeletal Related Events, Bone Metastasis and Survival of Prostate Cancer: A Population Based Cohort Study in Denmark (1999 to 2007)","volume":"184","author":"Norgaard","year":"2010","journal-title":"J. Urol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1002\/1097-0142(19880101)61:1<195::AID-CNCR2820610133>3.0.CO;2-Y","article-title":"Stratification of patients with metastatic prostate cancer based on extent of disease on initial bone scan","volume":"61","author":"Soloway","year":"1988","journal-title":"Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1053\/j.semnuclmed.2011.07.005","article-title":"The Bone Scan","volume":"42","author":"Brenner","year":"2012","journal-title":"Semin. Nuclear Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1097\/00006231-200108000-00005","article-title":"Whole body PET for the evaluation of bony metastases in patients with breast cancer: Comparison with 99Tcm-MDP bone scintigraphy","volume":"22","author":"Ohta","year":"2001","journal-title":"Nuclear Med. Commun."},{"key":"ref_8","first-page":"287","article-title":"The detection of bone metastases in patients with high-risk prostate cancer: 99mTc-MDP Planar bone scintigraphy, single- and multi-field-of-view SPECT, 18F-fluoride PET, and 18F-fluoride PET\/CT","volume":"47","author":"Metser","year":"2006","journal-title":"J. Nuclear Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"202","DOI":"10.4329\/wjr.v7.i8.202","article-title":"Imaging of bone metastasis: An update","volume":"7","year":"2015","journal-title":"World J. Radiol."},{"key":"ref_10","unstructured":"Mettler, F.A., and Guiberteau, M.J. (2019). Essentials of Nuclear Medicine and Molecular Imaging, Elsevier."},{"key":"ref_11","first-page":"48","article-title":"Nuclear medicine 2: Principles and technique of bone scintigraphy","volume":"115","author":"Purden","year":"2019","journal-title":"Nursing Times"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1097\/MNM.0b013e3283503ebf","article-title":"Computer-aided quantitative bone scan assessment of prostate cancer treatment response","volume":"33","author":"Brown","year":"2012","journal-title":"Nuclear Med. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1097\/00006231-200605000-00002","article-title":"A new computer-based decision-support system for the interpretation of bone scans","volume":"27","author":"Sadik","year":"2006","journal-title":"Nuclear Med. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.2967\/jnumed.108.055061","article-title":"Computer-Assisted Interpretation of Planar Whole-Body Bone Scans","volume":"49","author":"Sadik","year":"2008","journal-title":"J. Nuclear Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Papandrianos, N., Papageorgiou, E., Anagnostis, A., and Feleki, A. (2020). A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans. Appl. Sci., 10.","DOI":"10.3390\/app10030997"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Papandrianos, N., Papageorgiou, E., Anagnostis, A., and Papageorgiou, K. (2020). Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0237213"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Papandrianos, N., Papageorgiou, E., Anagnostis, A., and Papageorgiou, K. (2020). Efficient Bone Metastasis Diagnosis in Bone Scintigraphy Using a Fast Convolutional Neural Network Architecture. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080532"},{"key":"ref_18","unstructured":"Dang, J. (2016). Classification in Bone Scintigraphy Images Using Convolutional Neural Networks. [Master\u2019s Thesis, Lund University]."},{"key":"ref_19","unstructured":"Belcher, L. (2017). Convolutional Neural Networks for Classification of Prostate Cancer Metastases Using Bone Scan Images. [Master\u2019s Thesis, Lund University]. Student Paper."},{"key":"ref_20","unstructured":"(2020, December 11). EXINI Diagnostics AB. Available online: https:\/\/exini.com\/."},{"key":"ref_21","unstructured":"ABSI (2020, December 12). 510(k) Premarket Submission to U.S. Food & Drug Administration, Available online: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf19\/K191262.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.eururo.2012.01.037","article-title":"A Novel Automated Platform for Quantifying the Extent of Skeletal Tumour Involvement in Prostate Cancer Patients Using the Bone Scan Index","volume":"62","author":"Ulmert","year":"2012","journal-title":"Eur. Urol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Domingues, I., and Cardoso, J.S. (2014, January 24\u201331). Using Bayesian surprise to detect calcifications in mammogram images. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6943784"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_26","unstructured":"Sch\u00f6lkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., and Platt, J. (2000). Support vector method for novelty detection. Advances in Neural Information Processing Systems 12. Max-Planck-Gesellschaft, MIT Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Domingues, I., Amorim, J.P., Abreu, P.H., Duarte, H., and Santos, J. (2018, January 8\u201313). Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification. Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE, Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489599"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Marques, F., Duarte, H., Santos, J., Domingues, I., Amorim, J.P., and Abreu, P.H. (2019, January 8\u201312). An iterative oversampling approach for ordinal classification. Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, Limassol Cyprus.","DOI":"10.1145\/3297280.3297560"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lapa, P., Gon\u00e7alves, I., Rundo, L., and Castelli, M. (2019, January 12\u201316). Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI. Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA.","DOI":"10.1145\/3319619.3326864"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/2191-219X-3-9","article-title":"Bone Scan Index: A prognostic imaging biomarker for high-risk prostate cancer patients receiving primary hormonal therapy","volume":"3","author":"Kaboteh","year":"2013","journal-title":"EJNMMI Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/bju.13160","article-title":"Bone Scan Index predicts outcome in patients with metastatic hormone-sensitive prostate cancer","volume":"117","author":"Poulsen","year":"2016","journal-title":"BJU Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"84449","DOI":"10.18632\/oncotarget.19680","article-title":"Prognostic value of bone scan index as an imaging biomarker in metastatic prostate cancer: A meta-analysis","volume":"8","author":"Li","year":"2017","journal-title":"Oncotarget"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mustansar, N. (2018). Utility of Bone Scan Quantitative Parameters for the Evaluation of Prostate Cancer Patients. J. Nuclear Med. Radiat. Ther., 9.","DOI":"10.4172\/2155-9619.1000391"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/8\/148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:23Z","timestamp":1760165123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/8\/148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["jimaging7080148"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7080148","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,17]]}}}