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Kapetanou E, Malamas S, Leventis D, Karantanas AH, Klontzas ME. Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT). JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01028-7. [PMID: 38383807 DOI: 10.1007/s10278-024-01028-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/13/2024] [Accepted: 01/17/2024] [Indexed: 02/23/2024]
Abstract
Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes.
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Affiliation(s)
- Elisavet Kapetanou
- Biomedical Engineering Graduate Programme, School of Medicine, University of Crete, Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
| | - Stylianos Malamas
- Department of Computer Science-University of Crete, Heraklion, Greece
| | - Dimitrios Leventis
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece.
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece.
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Trovato P, Simonetti I, Morrone A, Fusco R, Setola SV, Giacobbe G, Brunese MC, Pecchi A, Triggiani S, Pellegrino G, Petralia G, Sica G, Petrillo A, Granata V. Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics. J Clin Med 2024; 13:547. [PMID: 38256682 PMCID: PMC10816509 DOI: 10.3390/jcm13020547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.
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Affiliation(s)
- Piero Trovato
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Igino Simonetti
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Alessio Morrone
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy;
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy;
| | - Annarita Pecchi
- Department of Radiology, University of Modena and Reggio Emilia, 41121 Modena, Italy;
| | - Sonia Triggiani
- Postgraduate School of Radiodiagnostics, University of Milan, 20122 Milan, Italy; (S.T.); (G.P.)
| | - Giuseppe Pellegrino
- Postgraduate School of Radiodiagnostics, University of Milan, 20122 Milan, Italy; (S.T.); (G.P.)
| | - Giuseppe Petralia
- Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| | - Giacomo Sica
- Radiology Unit, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (P.T.); (I.S.); (S.V.S.); (A.P.); (V.G.)
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Maddalo M, Bertolotti L, Mazzilli A, Flore AGM, Perotta R, Pagnini F, Ziglioli F, Maestroni U, Martini C, Caruso D, Ghetti C, De Filippo M. Small Renal Masses: Developing a Robust Radiomic Signature. Cancers (Basel) 2023; 15:4565. [PMID: 37760532 PMCID: PMC10527518 DOI: 10.3390/cancers15184565] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38-87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.
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Affiliation(s)
- Michele Maddalo
- Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy; (M.M.); (A.M.); (C.G.)
| | - Lorenzo Bertolotti
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
| | - Aldo Mazzilli
- Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy; (M.M.); (A.M.); (C.G.)
| | | | - Rocco Perotta
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
| | - Francesco Pagnini
- Diagnostic Department, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy;
| | - Francesco Ziglioli
- Department of Urology, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy; (F.Z.); (U.M.)
| | - Umberto Maestroni
- Department of Urology, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy; (F.Z.); (U.M.)
| | - Chiara Martini
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
- Diagnostic Department, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy;
| | - Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza-University of Rome, 00100 Rome, Italy
| | - Caterina Ghetti
- Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy; (M.M.); (A.M.); (C.G.)
| | - Massimo De Filippo
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
- Diagnostic Department, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy;
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