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van der Velden D, Staal F, Aalbersberg E, Castagnoli F, Wilthagen E, Beets-Tan R. Prognostic value of CT characteristics in GEP-NET: a systematic review. Crit Rev Oncol Hematol 2022; 175:103713. [DOI: 10.1016/j.critrevonc.2022.103713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/04/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022] Open
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Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern. Diagnostics (Basel) 2022; 12:diagnostics12051115. [PMID: 35626271 PMCID: PMC9140199 DOI: 10.3390/diagnostics12051115] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 04/27/2022] [Indexed: 02/07/2023] Open
Abstract
To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.
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Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography. Cancers (Basel) 2022; 14:cancers14092132. [PMID: 35565261 PMCID: PMC9102628 DOI: 10.3390/cancers14092132] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The assessment of breast lesions through mammographic images is currently challenging, especially in dense breasts. Contrast-enhanced mammography has been shown to overcome the limitations of standard mammography but it greatly depends on the interpretative skills of the physician. The aim of this study was to evaluate the potentialities of statistical and artificial intelligence algorithms as a tool for helping the radiologists in the interpretation of images. The most remarkable results were achieved in discriminating benign from malignant lesions and in the identification of the presence of the hormone receptor. A tool to support the physician’s decision-making process may be designed starting from simple logistic regression and tree-based algorithms. This type of tool may help the radiologist in assessing the investigated breast and in choosing the appropriate follow-up without resorting to histology. Abstract Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.
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Magnetic Resonance Features of Liver Mucinous Colorectal Metastases: What the Radiologist Should Know. J Clin Med 2022; 11:jcm11082221. [PMID: 35456314 PMCID: PMC9027866 DOI: 10.3390/jcm11082221] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/01/2022] [Accepted: 04/12/2022] [Indexed: 02/06/2023] Open
Abstract
Purpose: The aim of this study is to assess MRI features of mucinous liver metastases compared to non-mucinous metastases and hepatic hemangioma. Methods: A radiological archive was assessed from January 2017 to June 2021 to select patients subjected to liver resection for CRCLM and MRI in the staging phase. We selected 20 patients with hepatic hemangioma (study group B). We evaluated (a) the maximum diameter of the lesions, in millimeters, on T1-W flash 2D in phase and out phase, on axial HASTE T2-W and on portal phase axial VIBE T1 W; and (b) the signal intensity (SI) in T1-W sequences, in T2-W sequences, Diffusion-Weighted Imaging (DWI) sequences and apparent diffusion coefficient (ADC) maps so as to observe (c) the presence and the type of contrast enhancement during the contrast study. The chi-square test was employed to analyze differences in percentage values of the categorical variable, while the non-parametric Kruskal−Wallis test was used to test for statistically significant differences between the median values of the continuous variables. A p-value < 0.05 was considered statistically significant. Results: The final study population included 52 patients (33 men and 19 women) with 63 years of median age (range 37−82 years) and 157 metastases. In 35 patients, we found 118 non-mucinous type metastases (control group), and in 17 patients, we found 39 mucinous type metastases (study group A). During follow-up, recurrence occurred in 12 patients, and three exhibited mucinous types among them. In the study group, all lesions (100%) showed hypointense SI on T1-W, very high SI (similar to hepatic hemangioma) in T2-W with restricted diffusion and iso-hypointense signals in the ADC map. During the contrast study, the main significant feature is the peripheral progressive enhancement.
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Granata V, Fusco R, Belli A, Borzillo V, Palumbo P, Bruno F, Grassi R, Ottaiano A, Nasti G, Pilone V, Petrillo A, Izzo F. Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma. Infect Agent Cancer 2022; 17:13. [PMID: 35346300 PMCID: PMC8961950 DOI: 10.1186/s13027-022-00429-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023] Open
Abstract
Background This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC). Methods Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021.
Results We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs. Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance. Conclusions Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.
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Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022; 40:919-929. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye. METHODS This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed. RESULTS Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting. CONCLUSIONS Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.
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Affiliation(s)
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy.
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Alessandra Bruno
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100, L'Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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Committeri U, Fusco R, Di Bernardo E, Abbate V, Salzano G, Maglitto F, Dell’Aversana Orabona G, Piombino P, Bonavolontà P, Arena A, Perri F, Maglione MG, Setola SV, Granata V, Iaconetta G, Ionna F, Petrillo A, Califano L. Radiomics Metrics Combined with Clinical Data in the Surgical Management of Early-Stage (cT1-T2 N0) Tongue Squamous Cell Carcinomas: A Preliminary Study. BIOLOGY 2022; 11:biology11030468. [PMID: 35336841 PMCID: PMC8945467 DOI: 10.3390/biology11030468] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022]
Abstract
Objective: To predict the risk of metastatic lymph nodes and the tumor grading related to oral tongue squamous cell carcinoma (OTSCC) through the combination of clinical data with radiomics metrics by computed tomography, and to develop a supportive approach in the management of the lymphatic cervical areas, with particular attention to the early stages (T1−T2). Between March 2016 and February 2020, patients with histologically confirmed OTSCC, treated by partial glossectomy and ipsilateral laterocervical lymphadenectomy and subjected to computed tomography (CT) before surgery, were identified by two centers: 81 patients (49 female and 32 male) with 58 years as the median age (range 19−86 years). Univariate analysis with non-parametric tests and multivariate analysis with machine learning approaches were used. Clinical, hematological parameters and radiological features extracted by CT were considered individually and in combination. All clinical parameters showed statistically significant differences (p < 0.05) for the Kruskal−Wallis test when discriminating both the tumor grading and the metastatic lymph nodes. DOI, PLR, SII, and SIRI showed an accuracy of 0.70 (ROC analysis) when identifying the tumor grading, while an accuracy ≥ 0.78 was shown by DOI, NLR, PLR, SII, and SIRI when discriminating metastatic lymph nodes. In the context of the analysis of radiomics metrics, the original_glszm_HighGrayLevelZoneEmphasis feature was selected for identifying the tumor grading (accuracy of 0.70), while the wavelet_HHH_glrlm_LowGrayLevelRunEmphasis predictor was selected for determining metastatic lymph nodes (accuracy of 0.96). Remarkable findings were also obtained when classifying patients with a machine learning approach. Radiomics features alone can predict tumor grading with an accuracy of 0.76 using a logistic regression model, while an accuracy of 0.82 can be obtained by running a CART algorithm through a combination of three clinical parameters (SIRI, DOI, and PLR) with a radiomics feature (wavelet_LLL_glszm_SizeZoneNonUniformityNormalized). In the context of predicting metastatic lymph nodes, an accuracy of 0.94 was obtained using 15 radiomics features in a logistic regression model, while both CART and CIDT achieved an asymptotic accuracy value of 1.00 using only one radiomics feature. Radiomics features and clinical parameters have an important role in identifying tumor grading and metastatic lymph nodes. Machine learning approaches can be used as an easy-to-use tool to stratify patients with early-stage OTSCC, based on the identification of metastatic and non-metastatic lymph nodes.
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Affiliation(s)
- Umberto Committeri
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy; (R.F.); (E.D.B.)
| | - Elio Di Bernardo
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy; (R.F.); (E.D.B.)
| | - Vincenzo Abbate
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Giovanni Salzano
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Fabio Maglitto
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Giovanni Dell’Aversana Orabona
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Pasquale Piombino
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Paola Bonavolontà
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Antonio Arena
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
| | - Francesco Perri
- Head and Neck Medical Oncology Unit, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy;
| | - Maria Grazia Maglione
- Division of Surgical Oncology Maxillo-Facial Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (M.G.M.); (F.I.)
| | - Sergio Venanzio Setola
- Divisions of Radiology, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (S.V.S.); (V.G.)
| | - Vincenza Granata
- Divisions of Radiology, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (S.V.S.); (V.G.)
| | - Giorgio Iaconetta
- Department of Neurosurgery, University of Salerno, 84084 Salerno, Italy;
| | - Franco Ionna
- Division of Surgical Oncology Maxillo-Facial Unit, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (M.G.M.); (F.I.)
| | - Antonella Petrillo
- Divisions of Radiology, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (S.V.S.); (V.G.)
- Correspondence:
| | - Luigi Califano
- Maxillofacial Surgery Operative Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Federico II University of Naples, 80131 Naples, Italy; (U.C.); (V.A.); (G.S.); (F.M.); (G.D.O.); (P.P.); (P.B.); (A.A.); (L.C.)
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Fusco R, Di Bernardo E, Piccirillo A, Rubulotta MR, Petrosino T, Barretta ML, Mattace Raso M, Vallone P, Raiano C, Di Giacomo R, Siani C, Avino F, Scognamiglio G, Di Bonito M, Granata V, Petrillo A. Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions. Curr Oncol 2022; 29:1947-1966. [PMID: 35323359 PMCID: PMC8947713 DOI: 10.3390/curroncol29030159] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.
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Affiliation(s)
- Roberta Fusco
- Medical Oncolody Division, Igea SpA, 80013 Naples, Italy; (R.F.); (E.D.B.)
| | - Elio Di Bernardo
- Medical Oncolody Division, Igea SpA, 80013 Naples, Italy; (R.F.); (E.D.B.)
| | - Adele Piccirillo
- Department of Electrical Engineering and Information Technologies, Università degli Studi di Napoli Federico II, 80125 Naples, Italy;
| | - Maria Rosaria Rubulotta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
| | - Teresa Petrosino
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
| | - Maria Luisa Barretta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
| | - Mauro Mattace Raso
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
| | - Paolo Vallone
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
| | - Concetta Raiano
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
| | - Raimondo Di Giacomo
- Senology Surgical Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (R.D.G.); (C.S.); (F.A.)
| | - Claudio Siani
- Senology Surgical Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (R.D.G.); (C.S.); (F.A.)
| | - Franca Avino
- Senology Surgical Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (R.D.G.); (C.S.); (F.A.)
| | - Giosuè Scognamiglio
- Pathology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (G.S.); (M.D.B.)
| | - Maurizio Di Bonito
- Pathology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (G.S.); (M.D.B.)
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
- Correspondence: ; Tel.: +39-081-590-714; Fax: +39-081-590-3825
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy; (M.R.R.); (T.P.); (M.L.B.); (M.M.R.); (P.V.); (C.R.); (A.P.)
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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Palumbo D, Mori M, Prato F, Crippa S, Belfiori G, Reni M, Mushtaq J, Aleotti F, Guazzarotti G, Cao R, Steidler S, Tamburrino D, Spezi E, Del Vecchio A, Cascinu S, Falconi M, Fiorino C, De Cobelli F. Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach. Cancers (Basel) 2021; 13:cancers13194938. [PMID: 34638421 PMCID: PMC8508250 DOI: 10.3390/cancers13194938] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary If pancreatic adenocarcinoma is assessed to be technically resectable, curative surgery is still suggested as the primary treatment option; however, the recurrence rate can be very high even in this selected population. The aim of our retrospective study was to develop a preoperative model to accurately stratify upfront resectable patients according to the risk of early distant disease relapse after surgery (<12 months from index procedure). Through a machine learning-based approach, we identified one biochemical marker (serum level of CA19.9), one radiological finding (necrosis) and one radiomic feature (SurfAreaToVolumeRatio), all significantly associated with the early resurge of distant recurrence. A model composed of these three variables only allowed identification of those patients at high risk for early distant disease relapse (50% chance of developing metastases within 12 months after surgery), who would benefit from neoadjuvant chemotherapy instead of upfront surgery. Abstract Despite careful selection, the recurrence rate after upfront surgery for pancreatic adenocarcinoma can be very high. We aimed to construct and validate a model for the prediction of early distant recurrence (<12 months from index surgery) after upfront pancreaticoduodenectomy. After exclusions, 147 patients were retrospectively enrolled. Preoperative clinical and radiological (CT-based) data were systematically evaluated; moreover, 182 radiomics features (RFs) were extracted. Most significant RFs were selected using minimum redundancy, robustness against delineation uncertainty and an original machine learning bootstrap-based method. Patients were split into training (n = 94) and validation cohort (n = 53). Multivariable Cox regression analysis was first applied on the training cohort; the resulting prognostic index was then tested in the validation cohort. Clinical (serum level of CA19.9), radiological (necrosis), and radiomic (SurfAreaToVolumeRatio) features were significantly associated with the early resurge of distant recurrence. The model combining these three variables performed well in the training cohort (p = 0.0015, HR = 3.58, 95%CI = 1.98–6.71) and was then confirmed in the validation cohort (p = 0.0178, HR = 5.06, 95%CI = 1.75–14.58). The comparison of survival curves between low and high-risk patients showed a p-value <0.0001. Our model may help to better define resectability status, thus providing an actual aid for pancreatic adenocarcinoma patients’ management (upfront surgery vs. neoadjuvant chemotherapy). Independent validations are warranted.
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Affiliation(s)
- Diego Palumbo
- Department of Radiology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (D.P.); (J.M.); (G.G.); (S.S.); (F.D.C.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
| | - Martina Mori
- Department of Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (M.M.); (A.D.V.)
| | - Francesco Prato
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
| | - Stefano Crippa
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Giulio Belfiori
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Michele Reni
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
- Department of Oncology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Junaid Mushtaq
- Department of Radiology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (D.P.); (J.M.); (G.G.); (S.S.); (F.D.C.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
| | - Francesca Aleotti
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Giorgia Guazzarotti
- Department of Radiology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (D.P.); (J.M.); (G.G.); (S.S.); (F.D.C.)
| | - Roberta Cao
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
| | - Stephanie Steidler
- Department of Radiology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (D.P.); (J.M.); (G.G.); (S.S.); (F.D.C.)
| | - Domenico Tamburrino
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff CF24 3AA, UK;
| | - Antonella Del Vecchio
- Department of Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (M.M.); (A.D.V.)
| | - Stefano Cascinu
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
- Department of Oncology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Massimo Falconi
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Claudio Fiorino
- Department of Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (M.M.); (A.D.V.)
- Correspondence:
| | - Francesco De Cobelli
- Department of Radiology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (D.P.); (J.M.); (G.G.); (S.S.); (F.D.C.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy; (F.P.); (S.C.); (G.B.); (M.R.); (F.A.); (R.C.); (S.C.); (M.F.)
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Danti G, Flammia F, Matteuzzi B, Cozzi D, Berti V, Grazzini G, Pradella S, Recchia L, Brunese L, Miele V. Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging. Radiol Med 2021; 126:1497-1507. [PMID: 34427861 PMCID: PMC8702509 DOI: 10.1007/s11547-021-01408-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/30/2021] [Indexed: 12/17/2022]
Abstract
Neuroendocrine neoplasms (NENs) are heterogeneous tumours with a common phenotype descended from the diffuse endocrine system. NENs are found nearly anywhere in the body but the most frequent location is the gastrointestinal tract. Gastrointestinal neuroendocrine neoplasms (GI-NENs) are rather uncommon, representing around 2% of all gastrointestinal tumours and 20–30% of all primary neoplasms of the small bowel. GI-NENs have various clinical manifestations due to the different substances they can produce; some of these tumours appear to be associated with familial syndromes, such as multiple endocrine neoplasm and neurofibromatosis type 1. The current WHO classification (2019) divides NENs into three major categories: well-differentiated NENs, poorly differentiated NENs, and mixed neuroendocrine-non-neuroendocrine neoplasms. The diagnosis, localization, and staging of GI-NENs include morphology and functional imaging, above all contrast-enhanced computed tomography (CECT), and in the field of nuclear medicine imaging, a key role is played by 68Ga-labelled-somatostatin analogues (68Ga-DOTA-peptides) positron emission tomography/computed tomography (PET/TC). In this review of recent literature, we described the objectives of morphological/functional imaging and potential future possibilities of prognostic imaging in the assessment of GI-NENs.
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Affiliation(s)
- Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Benedetta Matteuzzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Valentina Berti
- Department of Nuclear Medicine, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Laura Recchia
- Department of Medicine and Health Sciences, University of Molise, 86100, Campobasso, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences, University of Molise, 86100, Campobasso, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
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Imaging of Pancreatic Neuroendocrine Neoplasms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18178895. [PMID: 34501485 PMCID: PMC8430610 DOI: 10.3390/ijerph18178895] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 12/25/2022]
Abstract
Pancreatic neuroendocrine neoplasms (panNENs) represent the second most common pancreatic tumors. They are a heterogeneous group of neoplasms with varying clinical expression and biological behavior, from indolent to aggressive ones. PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones. They are histopathologically classified according to the 2017 World Health Organization (WHO) classification system. Although the final diagnosis of neuroendocrine tumor relies on histologic examination of biopsy or surgical specimens, both morphologic and functional imaging are crucial for patient care. Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring. Functional imaging techniques with somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) are used for functional and metabolic assessment that is helpful for therapy management and post-therapeutic re-staging. This article reviews the morphological and functional imaging modalities now available and the imaging features of panNENs. Finally, future imaging challenges, such as radiomics analysis, are illustrated.
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Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A. Computed tomography-based radiomics approach in pancreatic tumors characterization. LA RADIOLOGIA MEDICA 2021; 126:10.1007/s11547-021-01405-0. [PMID: 34386897 DOI: 10.1007/s11547-021-01405-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/27/2021] [Indexed: 12/26/2022]
Abstract
Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging.
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Affiliation(s)
- Grigory Karmazanovsky
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
- Radiology Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan Gruzdev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia.
| | - Valeriya Tikhonova
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Evgeny Kondratyev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Amiran Revishvili
- Arrhythmology Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
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Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients. Cancers (Basel) 2021; 13:cancers13163992. [PMID: 34439148 PMCID: PMC8393664 DOI: 10.3390/cancers13163992] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 12/29/2022] Open
Abstract
PURPOSE To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. METHODS Seventy-four patients (median age 63 years, range 42-86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36-86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. RESULTS A total of 38 patients (median age 61; range 41-78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). CONCLUSIONS Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group.
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Granata V, Grassi R, Fusco R, Belli A, Cutolo C, Pradella S, Grazzini G, La Porta M, Brunese MC, De Muzio F, Ottaiano A, Avallone A, Izzo F, Petrillo A. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma. Infect Agent Cancer 2021; 16:53. [PMID: 34281580 PMCID: PMC8287696 DOI: 10.1186/s13027-021-00393-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023] Open
Abstract
This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, Naples, Italy
- Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Milan, Italy
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Silvia Pradella
- Radiology Division, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Giulia Grazzini
- Radiology Division, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Maria Chiara Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Alessandro Ottaiano
- Abdominal Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 2021; 16:39. [PMID: 34078424 PMCID: PMC8173908 DOI: 10.1186/s13027-021-00379-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/12/2021] [Indexed: 02/06/2023] Open
Abstract
Background Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. Methods The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. Results We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. Conclusions In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Roberta Fusco
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy.
| | - Maria Luisa Barretta
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Carmine Picone
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Andrea Belli
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Renato Patrone
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Marilina Ferrante
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Diletta Cozzi
- Division of Radiology, "Azienda Ospedaliera Universitaria Careggi", Florence, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy.,Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
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Huang WP, Liu SY, Han YJ, Li LM, Liang P, Gao JB. Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm. Front Oncol 2021; 11:677814. [PMID: 34079766 PMCID: PMC8166224 DOI: 10.3389/fonc.2021.677814] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/27/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose It is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN. Methods Eighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong's test were mainly used for model assessment and AUC comparison. Results It retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong's test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs. Conclusion The arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively.
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Affiliation(s)
- Wen-Peng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Si-Yun Liu
- Pharmaceutical Diagnostics, General Electric Company (GE) Healthcare, Beijing, China
| | - Yi-Jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Ming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Granata V, Coppola F, Grassi R, Fusco R, Tafuto S, Izzo F, Reginelli A, Maggialetti N, Buccicardi D, Frittoli B, Rengo M, Bortolotto C, Prost R, Lacasella GV, Montella M, Ciaghi E, Bellifemine F, De Muzio F, Danti G, Grazzini G, De Filippo M, Cappabianca S, Barresi C, Iafrate F, Stoppino LP, Laghi A, Grassi R, Brunese L, Neri E, Miele V, Faggioni L. Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal. Front Endocrinol (Lausanne) 2021; 12:748944. [PMID: 34917023 PMCID: PMC8670531 DOI: 10.3389/fendo.2021.748944] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/12/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. MATERIALS AND METHODS A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. RESULTS The final SR version was built by including n=16 items in the "Patient Clinical Data" section, n=13 items in the "Clinical Evaluation" section, n=8 items in the "Imaging Protocol" section, and n=17 items in the "Report" section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively. CONCLUSIONS The present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | | | - Salvatore Tafuto
- Medical Oncology Unit, Istituto Nazionale Tumori IRCCS ‘Fondazione G. Pascale’, Naples, Italy
| | - Francesco Izzo
- Department of Surgery, Istituto Nazionale Tumori -IRCCS- Fondazione G. Pascale, Naples, Italy
| | - Alfonso Reginelli
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | | | | | - Barbara Frittoli
- Department of Radiology, Ospedali Civili, Hospital of Brescia, University of Brescia, Brescia, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome - I.C.O.T. Hospital, Latina, Italy
| | - Chandra Bortolotto
- Department of Radiology, I.R.C.C.S. Policlinico San Matteo Foundation, Pavia, Italy
| | - Roberto Prost
- Radiology Unit, Azienda Ospedaliera Brotzu, Cagliari, Italy
| | - Giorgia Viola Lacasella
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | - Marco Montella
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | | | | | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Ginevra Danti
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
- *Correspondence: Ginevra Danti,
| | - Giulia Grazzini
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
| | - Massimo De Filippo
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Maggiore Hospital, Parma, Italy
| | - Salvatore Cappabianca
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | - Carmelo Barresi
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, Siena University Hospital, Siena, Italy
| | - Franco Iafrate
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Rome, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Emanuele Neri
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, University of Pisa, Pisa, Italy
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