{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:12:23Z","timestamp":1788858743387,"version":"build-2803163510"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005609","name":"University of Turku","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005609","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Phys Eng Sci Med"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Extracting anatomic reference from computed tomography (CT) images is a crucial step in fully automated image analysis solutions for CT and hybrid imaging of prostate cancer. Several deep learning-based applications can be used to perform segmentation of different anatomical structures in CT, but they might produce a false prostate segment for post-treatment scans of patients treated with prostatectomy. In this study, our aim was to both systematically assess the performance of a state-of-the-art CT segmentation tool, TotalSegmentator, for post-prostatectomy patients and to investigate the potential of using a convolutional neural network (CNN) to automatically detect prostatectomy from CT images. We collected a dataset of CT images from 542 patients, 269 of which were treated with robotic-assisted laparoscopic radical prostatectomy (RP), 194 of which were treated with radiation and\/or androgen deprivation therapy only, and 79 of which were treatment-naive. We used TotalSegmentator to perform multi-organ segmentation for all the patients, computed the volumes of the greatest connected components of the prostate segments, and studied the use of a cut-off threshold for the resulting volumes to detect RP with five-fold cross-validation. Additionally, we trained and evaluated a light-weight CNN for classifying patients treated with and without RP. According to our results, TotalSegmentator produced a false prostate segment for 98.5% patients treated with RP. The prostate segments were significantly smaller in the RP cohort than the other two cohorts (12.1\u00a0\u00b1\u00a05.8\u00a0cm\n                    <jats:inline-formula>\n                      <jats:tex-math>$$^3$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    vs. 22.8\u00a0\u00b1\u00a08.9\u00a0cm\n                    <jats:inline-formula>\n                      <jats:tex-math>$$^3$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and 22.4\u00a0\u00b1\u00a010.9\u00a0cm\n                    <jats:inline-formula>\n                      <jats:tex-math>$$^3$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:italic>p<\/jats:italic>\n                    -values: 1.3e\u221217 and 2.1e\u221217). The use of cut-off thresholds for TotalSegmentator\u2019s prostate volumes resulted in RP detection accuracy of 77.5\u00a0\u00b1\u00a01.3%, sensitivity of 83.0\u00a0\u00b1\u00a03.9%, specificity of 72.2\u00a0\u00b1\u00a03.5%, precision of 74.4\u00a0\u00b1\u00a03.1%, and area under receiver operating characteristic curve (auROC) of 84.1\u00a0\u00b1\u00a02.3%. Conversely, our proposed CNN approach detected RP with accuracy of 86.5\u00a0\u00b1\u00a04.2%, sensitivity of 78.9\u00a0\u00b1\u00a012.5%, specificity of 93.5\u00a0\u00b1\u00a03.9%, precision of 92.7\u00a0\u00b1\u00a03.9%, and auROC of 95.1\u00a0\u00b1\u00a01.7%, outperforming the threshold approach in a statistically significant way according to DeLong\u2019s tests (4 out of 5\n                    <jats:italic>p<\/jats:italic>\n                    -values&lt;0.045). To conclude, TotalSegmentator systematically produces false prostate segments with patients treated with RP but, by utilizing a CNN, RP can be detected automatically based on CT data only. This enables automated correction of TotalSegmentator masks. If developed further, our CNN method could provide a stand-alone application for identifying presence of prostatectomy efficiently without the need to consult treatment records and, consequently, assist in developing fully-automated image analysis tools.\n                  <\/jats:p>","DOI":"10.1007\/s13246-026-01801-8","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T08:59:18Z","timestamp":1788857958000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning for automatic detection of prostatectomy in CT images of prostate cancer patients"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7775-7656","authenticated-orcid":false,"given":"Oona","family":"Rainio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2170-9188","authenticated-orcid":false,"given":"Anting","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4375-2861","authenticated-orcid":false,"given":"Ahti","family":"Vehmanen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7199-0062","authenticated-orcid":false,"given":"Maria K.","family":"Jaakkola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9390-1198","authenticated-orcid":false,"given":"Jukka","family":"Kemppainen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0982-8360","authenticated-orcid":false,"given":"Riku","family":"Kl\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"1801_CR1","unstructured":"Ferlay J et al (2024) Global cancer observatory: cancer tomorrow. 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