{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:14:19Z","timestamp":1760228059081,"version":"build-2065373602"},"reference-count":14,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"South Western Sydney Local Health District (SWSLHD)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>The Orthanc server is a light-weight open-source picture imaging and archiving system (PACS) used to store digital imaging and communications in medicine (DICOM) data. It is widely used in research environments as it is free, open-source and scalable. To enable the use of Orthanc stored radiotherapy (RT) data in data mining and machine learning tasks, the records need to be extracted, validated, linked, and presented in a usable format. This paper reports patient data collection and processing (PDCP), a set of tools created using python for extracting, transforming, and loading RT data from Orthanc PACs. PDCP enables querying, retrieving, and validating patient imaging summaries; analysing associations between patient DICOM data; retrieving patient imaging data into a local directory; preparing the records for use in various research questions; tracking the patient\u2019s data collection process and identifying reasons behind excluding patient\u2019s data. PDCP targeted simplifying the data preparation process in such applications, and it was made expandable to facilitate additional data preparation tasks.<\/jats:p>","DOI":"10.3390\/software1020009","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T14:49:38Z","timestamp":1651848578000},"page":"215-222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PDCP: A Set of Tools for Extracting, Transforming, and Loading Radiotherapy Data from the Orthanc Research PACS"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5092-949X","authenticated-orcid":false,"given":"Ali","family":"Haidar","sequence":"first","affiliation":[{"name":"South Western Sydney Clinical Campuses, University of New South Wales, Sydney 2170, Australia"},{"name":"Ingham Institute for Applied Medical Research, Liverpool 2170, Australia"},{"name":"Liverpool and Macarthur Cancer Therapy Centres, Liverpool 2170, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4674-1480","authenticated-orcid":false,"given":"Farhannah","family":"Aly","sequence":"additional","affiliation":[{"name":"South Western Sydney Clinical Campuses, University of New South Wales, Sydney 2170, Australia"},{"name":"Ingham Institute for Applied Medical Research, Liverpool 2170, Australia"},{"name":"Liverpool and Macarthur Cancer Therapy Centres, Liverpool 2170, Australia"}]},{"given":"Lois","family":"Holloway","sequence":"additional","affiliation":[{"name":"South Western Sydney Clinical Campuses, University of New South Wales, Sydney 2170, Australia"},{"name":"Ingham Institute for Applied Medical Research, Liverpool 2170, Australia"},{"name":"Liverpool and Macarthur Cancer Therapy Centres, Liverpool 2170, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.radonc.2014.03.024","article-title":"Estimating the demand for radiotherapy from the evidence: A review of changes from 2003 to 2012","volume":"112","author":"Barton","year":"2014","journal-title":"Radiother. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.radonc.2013.11.001","article-title":"International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining","volume":"110","author":"Roelofs","year":"2014","journal-title":"Radiother. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10278-018-0082-y","article-title":"The Orthanc ecosystem for medical imaging","volume":"31","author":"Jodogne","year":"2018","journal-title":"J. Digit. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1111\/1754-9485.13275","article-title":"Artificial intelligence in medical imaging and radiation oncology: Opportunities and challenges","volume":"65","author":"Thwaites","year":"2021","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2764","DOI":"10.1038\/s41598-019-39206-1","article-title":"Deep learning in head & neck cancer outcome prediction","volume":"9","author":"Diamant","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10117","DOI":"10.1038\/s41598-017-10371-5","article-title":"Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer","volume":"7","author":"Vallieres","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dasu, T., and Johnson, T. (2003). Exploratory Data Mining and Data Cleaning, John Wiley & Sons.","DOI":"10.1002\/0471448354"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/0360-3016(80)90211-4","article-title":"Computed tomography for radiotherapy planning","volume":"6","author":"Battista","year":"1980","journal-title":"Int. J. Radiat. Oncol. *Biol. *Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1148\/rg.293075172","article-title":"DICOM-RT and its utilization in radiation therapy","volume":"29","author":"Law","year":"2009","journal-title":"Radiographics"},{"key":"ref_10","first-page":"K9","article-title":"Data from head-neck-PET-CT","volume":"10","author":"Perrin","year":"2017","journal-title":"Cancer Imaging Arch."},{"key":"ref_11","first-page":"K9","article-title":"Data from NSCLC-radiomics","volume":"10","author":"Aerts","year":"2019","journal-title":"Cancer Imaging Arch."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Haidar, A. (2022). PDCP Examples (0.0.1). Zenodo.","DOI":"10.3390\/software1020009"},{"key":"ref_14","unstructured":"Haidar, A. (2021). Head-Neck-PET-CT combined GTVs 2D images. Zenodo."}],"container-title":["Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2674-113X\/1\/2\/9\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:07:05Z","timestamp":1760137625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2674-113X\/1\/2\/9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,6]]},"references-count":14,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["software1020009"],"URL":"https:\/\/doi.org\/10.3390\/software1020009","relation":{},"ISSN":["2674-113X"],"issn-type":[{"type":"electronic","value":"2674-113X"}],"subject":[],"published":{"date-parts":[[2022,5,6]]}}}