{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T00:01:04Z","timestamp":1755993664398,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"DOI":"10.1145\/3632047.3632074","type":"proceedings-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T18:48:15Z","timestamp":1709059695000},"page":"184-187","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer: a feasibility study exploiting high energy physics potential to evaluate the model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5738-4927","authenticated-orcid":false,"given":"Mariagrazia","family":"Monteleone","sequence":"first","affiliation":[{"name":"Dipartimento di Elettronica,Informazione e Bioingegneria, Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5269-8517","authenticated-orcid":false,"given":"Simone","family":"Gennai","sequence":"additional","affiliation":[{"name":"INFN sezione di Milano-Bicocca, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-1301","authenticated-orcid":false,"given":"Pietro","family":"Govoni","sequence":"additional","affiliation":[{"name":"Dipartimento di Fisica, Universita degli Studi di Milano Bicocca, Italy and INFN sezione di Milano-Bicocca, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4787-8649","authenticated-orcid":false,"given":"Chiara","family":"Paganelli","sequence":"additional","affiliation":[{"name":"Dipartimento di Elettronica,Informazione e Bioingegneria, Politecnico di Milano, Italy"}]}],"member":"320","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. Large Hadron Collider https:\/\/home.cern\/science\/accelerators\/large-hadron-collider."},{"key":"e_1_3_2_1_2_1","unstructured":"[n. d.]. Shap-hypetune: https:\/\/github.com\/cerlymarco\/shap-hypetune."},{"volume-title":"Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques. Ph.\u00a0D. Dissertation","author":"Mohamed\u00a0Bukhari Abdelbasit Sahar","key":"e_1_3_2_1_3_1","unstructured":"Sahar Mohamed\u00a0Bukhari Abdelbasit. 2023. Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques. Ph.\u00a0D. Dissertation. Rochester Institute of Technology."},{"key":"e_1_3_2_1_4_1","volume-title":"A radiogenomic dataset of non-small cell lung cancer. Scientific data 5, 1","author":"Bakr Shaimaa","year":"2018","unstructured":"Shaimaa Bakr, Olivier Gevaert, Sebastian Echegaray, Kelsey Ayers, Mu Zhou, Majid Shafiq, Hong Zheng, Jalen\u00a0Anthony Benson, Weiruo Zhang, Ann\u00a0NC Leung, 2018. A radiogenomic dataset of non-small cell lung cancer. Scientific data 5, 1 (2018), 1\u20139."},{"key":"e_1_3_2_1_5_1","unstructured":"Shaimaa Bakr Olivier Gevaert Sebastian Echegaray Kelsey Ayers Mu Zhou Majid Shafiq Hong Zheng Weiruo Zhang Ann Leung Michael Kadoch 2017. Data for NSCLC Radiogenomics collection. The Cancer Imaging Archive."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"e_1_3_2_1_8_1","volume-title":"Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation","author":"CMS","year":"2020","unstructured":"CMS collaboration 2020. Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation (2020)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1748-0221\/3\/08\/S08004"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/JHEP02(2014)057"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-021-00399-1"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12111607"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2021.12.002"},{"key":"e_1_3_2_1_14_1","volume-title":"AMIA Annual Symposium Proceedings, Vol.\u00a02020","author":"Li Yingxue","year":"2020","unstructured":"Yingxue Li, Tingyu Chen, Tiange Chen, Xiang Li, Caihong Zeng, Zhihong Liu, and Guotong Xie. 2020. An interpretable Machine Learning survival model for predicting long-term kidney outcomes in IgA nephropathy. In AMIA Annual Symposium Proceedings, Vol.\u00a02020. American Medical Informatics Association, 737."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2020.2993278"},{"key":"e_1_3_2_1_16_1","volume-title":"A unified approach to interpreting model predictions. Advances in neural information processing systems 30","author":"Lundberg M","year":"2017","unstructured":"Scott\u00a0M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jointm.2021.09.002"},{"key":"e_1_3_2_1_18_1","volume-title":"Imaging biomarker roadmap for cancer studies. Nature reviews Clinical oncology 14, 3","author":"O\u2019connor PB","year":"2017","unstructured":"James\u00a0PB O\u2019connor, Eric\u00a0O Aboagye, Judith\u00a0E Adams, Hugo\u00a0JWL Aerts, Sally\u00a0F Barrington, Ambros\u00a0J Beer, Ronald Boellaard, Sarah\u00a0E Bohndiek, Michael Brady, Gina Brown, 2017. Imaging biomarker roadmap for cancer studies. Nature reviews Clinical oncology 14, 3 (2017), 169\u2013186."},{"key":"e_1_3_2_1_19_1","volume-title":"Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825\u20132830."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1515\/cclm-2022-0291"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455928"},{"key":"e_1_3_2_1_22_1","volume-title":"AI in health and medicine. Nature medicine 28, 1","author":"Rajpurkar Pranav","year":"2022","unstructured":"Pranav Rajpurkar, Emma Chen, Oishi Banerjee, and Eric\u00a0J Topol. 2022. AI in health and medicine. Nature medicine 28, 1 (2022), 31\u201338."},{"key":"e_1_3_2_1_23_1","volume-title":"Computational radiomics system to decode the radiographic phenotype. Cancer research 77, 21","author":"Van\u00a0Griethuysen JM","year":"2017","unstructured":"Joost\u00a0JM Van\u00a0Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina\u00a0GH Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, and Hugo\u00a0JWL Aerts. 2017. Computational radiomics system to decode the radiographic phenotype. Cancer research 77, 21 (2017), e104\u2013e107."},{"key":"e_1_3_2_1_24_1","volume-title":"\u201chow-to","author":"Van\u00a0Timmeren E","year":"2020","unstructured":"Janita\u00a0E Van\u00a0Timmeren, Davide Cester, Stephanie Tanadini-Lang, Hatem Alkadhi, and Bettina Baessler. 2020. Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection. Insights into imaging 11, 1 (2020), 1\u201316."}],"event":{"name":"ICBRA 2023: 2023 the 10th International Conference on Bioinformatics Research and Application","acronym":"ICBRA 2023","location":"Barcelona Spain"},"container-title":["Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632047.3632074","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632047.3632074","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T23:56:48Z","timestamp":1755907008000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632047.3632074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":24,"alternative-id":["10.1145\/3632047.3632074","10.1145\/3632047"],"URL":"https:\/\/doi.org\/10.1145\/3632047.3632074","relation":{},"subject":[],"published":{"date-parts":[[2023,9,22]]},"assertion":[{"value":"2024-02-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}