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BibTeX
Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients
Cancers, 2021-11, Vol.13 (22), p.5672
Bourbonne, Vincent
Jaouen, Vincent
Nguyen, Truong An
Tissot, Valentin
Doucet, Laurent
Hatt, Mathieu
Visvikis, Dimitris
Pradier, Olivier
Valéri, Antoine
Fournier, Georges
Schick, Ulrike
2021
Details
Autor(en) / Beteiligte
Bourbonne, Vincent
Jaouen, Vincent
Nguyen, Truong An
Tissot, Valentin
Doucet, Laurent
Hatt, Mathieu
Visvikis, Dimitris
Pradier, Olivier
Valéri, Antoine
Fournier, Georges
Schick, Ulrike
Titel
Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients
Ist Teil von
Cancers, 2021-11, Vol.13 (22), p.5672
Ort / Verlag
Basel: MDPI AG
Erscheinungsjahr
2021
Link zum Volltext
Quelle
EZB Free E-Journals
Beschreibungen/Notizen
Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, we aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from preoperative multimodal MRI. All patients who underwent a preoperative MRI and radical prostatectomy with extensive lymph node dissection were retrospectively included in a single institution. Patients were randomly divided into the training (60%) and testing (40%) sets. Radiomic features were extracted from the index tumor volumes, delineated on the apparent diffusion coefficient corrected map and the T2 sequences. A ComBat harmonization method was applied to account for inter-site heterogeneity. A prediction model was trained using a neural network approach (Multilayer Perceptron Network, SPSS v24.0©) combining clinical, radiomic and all features. It was then evaluated on the testing set and compared to the current available models using the Receiver Operative Characteristics and the C-Index. Two hundred and eighty patients were included, with a median age of 65.2 y (45.3–79.6), a mean PSA level of 9.5 ng/mL (1.04–63.0) and 79.6% of ISUP ≥ 2 tumors. LNI occurred in 51 patients (18.2%), with a median number of extracted nodes of 15 (10–19). In the testing set, with their respective cutoffs applied, the Partin, Roach, Yale, MSKCC, Briganti 2012 and 2017 models resulted in a C-Index of 0.71, 0.66, 0.55, 0.67, 0.65 and 0.73, respectively, while our proposed combined model resulted in a C-Index of 0.89 in the testing set. Radiomic features extracted from the preoperative MRI scans and combined with clinical features through a neural network seem to provide added predictive performance compared to state of the art models regarding LNI risk prediction in PCa.
Sprache
Englisch
Identifikatoren
ISSN: 2072-6694
eISSN: 2072-6694
DOI: 10.3390/cancers13225672
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_16e452384f4c44e9aed8e538056cc96b
Format
–
Schlagworte
Biopsy
,
Diffusion coefficient
,
Learning algorithms
,
lymph node involvement
,
Lymph nodes
,
Lymphatic system
,
Machine learning
,
Magnetic resonance imaging
,
Morbidity
,
MRI
,
Neural networks
,
Patients
,
prediction
,
Prediction models
,
Prostate cancer
,
Prostatectomy
,
Radiomics
,
Risk factors
,
Statistical analysis
,
Surgery
,
Tumors
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