A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients
| Autor: | Tobias Maurer, Jan C. Peeken, Markus Kroenke, Matthias Eiber, Stephanie E. Combs, Mohamed A. Shouman, Isabel Rauscher, Jürgen E. Gschwend |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Local binary patterns Prostate cancer Prostate carcinoma Radiomics Positron Emission Tomography Computed Tomography medicine PSMA Humans Radiology Nuclear Medicine and imaging Lymph node business.industry Prostatic Neoplasms Radioguided Surgery General Medicine Gold standard (test) medicine.disease Radioguided surgery medicine.anatomical_structure Surgery Computer-Assisted Lymphatic Metastasis Prostate Carcinoma Psma Ct Lymph Node Original Article Tomography Radiology Lymph Nodes Neoplasm Recurrence Local business Recurrent Prostate Carcinoma Tomography X-Ray Computed CT |
| Zdroj: | European Journal of Nuclear Medicine and Molecular Imaging Eur. J. Nucl. Med. Mol. Imaging 47, 2968-2977 (2020) |
| ISSN: | 1619-7089 1619-7070 |
| Popis: | Purpose In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)–positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). Methods Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. Results Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node–specific decision curve analysis, there was a clinical net benefit above LN short diameter. Conclusion The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features. |
| Databáze: | OpenAIRE |
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