|
1
|
Stanzione A, Galatola R, Cuocolo R, Romeo V, Verde F, Mainenti PP, Brunetti A, Maurea S. Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study. Diagnostics (Basel) 2022; 12:diagnostics12030578. [PMID: 35328133 PMCID: PMC8947112 DOI: 10.3390/diagnostics12030578] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/19/2022] [Accepted: 02/21/2022] [Indexed: 12/22/2022] Open
Abstract
In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.
Collapse
Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (R.G.); (V.R.); (F.V.); (A.B.); (S.M.)
| | - Roberta Galatola
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (R.G.); (V.R.); (F.V.); (A.B.); (S.M.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
- Interdepartmental Research Center on Management and Innovation in Healthcare-CIRMIS, University of Naples “Federico II”, 80100 Naples, Italy
- Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80100 Naples, Italy
- Correspondence:
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (R.G.); (V.R.); (F.V.); (A.B.); (S.M.)
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (R.G.); (V.R.); (F.V.); (A.B.); (S.M.)
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Research Council, 80131 Naples, Italy;
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (R.G.); (V.R.); (F.V.); (A.B.); (S.M.)
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (R.G.); (V.R.); (F.V.); (A.B.); (S.M.)
| |
Collapse
|
|
2
|
Crimì F, Quaia E, Cabrelle G, Zanon C, Pepe A, Regazzo D, Tizianel I, Scaroni C, Ceccato F. Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review. Int J Mol Sci 2022; 23:ijms23020637. [PMID: 35054823 PMCID: PMC8776161 DOI: 10.3390/ijms23020637] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 12/19/2022] Open
Abstract
Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.
Collapse
Affiliation(s)
- Filippo Crimì
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Emilio Quaia
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Giulio Cabrelle
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Chiara Zanon
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Alessia Pepe
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Institute of Radiology, University-Hospital of Padova, 35128 Padua, Italy
| | - Daniela Regazzo
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
| | - Irene Tizianel
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
| | - Carla Scaroni
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
| | - Filippo Ceccato
- Department of Medicine DIMED, University of Padova, 35128 Padua, Italy; (F.C.); (E.Q.); (G.C.); (C.Z.); (A.P.); (D.R.); (I.T.); (C.S.)
- Endocrine Disease Unit, University-Hospital of Padova, 35128 Padua, Italy
- Correspondence: ; Tel.: +39-049-8211323; Fax: +39-049-657391
| |
Collapse
|