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Taşçi F, Metin Y, Metin NO, Rakici S, Gözükara MG, Taşçi E. Comparative effectiveness of two abbreviated rectal MRI protocols in assessing tumor response to neoadjuvant chemoradiotherapy in patients with rectal cancer. Oncol Lett 2024; 28:565. [PMID: 39385951 PMCID: PMC11462512 DOI: 10.3892/ol.2024.14696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 08/02/2024] [Indexed: 10/12/2024] Open
Abstract
The present study aimed to compare the effectiveness of two abbreviated magnetic resonance imaging (MRI) protocols in assessing the response to neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer. Data from the examinations of 62 patients with rectal cancer who underwent neoadjuvant CRT and standard contrast-enhanced rectal MRI were retrospectively evaluated. Standard contrast-enhanced T2-weighted imaging (T2-WI), post-contrast T1-weighted imaging (T1-WI) and diffusion-weighted imaging (DWI) MRI, as well as two abbreviated protocols derived from these images, namely protocol AB1 (T2-WI and DWI) and protocol AB2 (post-contrast fat-suppressed (FS) T1-WI and DWI), were assessed. Measurements of lesion length and width, lymph node short-axis length, tumor staging, circumferential resection margin (CRM), presence of extramural venous invasion (EMVI), luminal mucin accumulation (MAIN), mucinous response, mesorectal fascia (MRF) involvement, and MRI-based tumor regression grade (mrTRG) were obtained. The reliability and compatibility of the AB1 and AB2 protocols in the evaluation of tumor response were analyzed. The imaging performed according to the AB1 and AB2 protocols revealed significant decreases in lesion length, width and lymph node size after CRT. These protocols also showed reductions in lymph node positivity, CRM, MRF, EMVI.Furthermore, both protocols were found to be reliable in determining lesion length and width. Additionally, compliance was observed between the protocols in determining lymph node size and positivity, CRM involvement, and EMVI after CRT. In conclusion, the use of abbreviated MRI protocols, specifically T2-WI with DWI sequences or post-contrast FS T1-WI with DWI sequences, is effective for evaluating tumor response in patients with rectal cancer following neoadjuvant CRT. The AB protocols examined in this study yielded similar results in terms of lesion length and width, lymph node positivity, CRM involvement, EMVI, MAIN, and MRF involvement.
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Affiliation(s)
- Filiz Taşçi
- Department of Radiology, Faculty of Medicine, Recep Tayyip Erdogan University, 53000 Rize, Turkey
| | - Yavuz Metin
- Faculty of Medicine, Ankara University, 06230 Ankara, Turkey
| | - Nurgül Orhan Metin
- Radiology Unit, Beytepe Murat Erdi Eker State Hospital, 06800 Ankara, Turkey
| | - Sema Rakici
- Department of Radiation Oncology, Faculty of Medicine, Recep Tayyip Erdogan University, 53000 Rize, Turkey
| | - Melih Gaffar Gözükara
- Health Directorate, Ankara Yıldırım Beyazıt University Faculty of Medicine, 06800 Ankara, Turkey
| | - Erencan Taşçi
- Güneysu Physical Therapy Unit, Faculty of Medicine, Recep Tayyip Erdogan University, 53000 Rize, Turkey
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2
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Petrillo A, Fusco R, Petrosino T, Vallone P, Granata V, Rubulotta MR, Pariante P, Raiano N, Scognamiglio G, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Sorgente E, Pecori B, Cerciello V, Boldrini L. A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer. LA RADIOLOGIA MEDICA 2024; 129:864-878. [PMID: 38755477 DOI: 10.1007/s11547-024-01817-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.
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Affiliation(s)
- Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Teresa Petrosino
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Paolo Vallone
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Maria Rosaria Rubulotta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Paolo Pariante
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Nicola Raiano
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Giosuè Scognamiglio
- Pathology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annarita Fanizzi
- Direzione Scientifica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- SSD Fisica Sanitaria, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miria Lafranceschina
- Struttura Semplice Dipartimentale Di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale Di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Valeria De Soccio
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Botta
- Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy
| | - Eugenio Sorgente
- Radiation Protection and Innovative Technology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Biagio Pecori
- Radiation Protection and Innovative Technology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenzo Cerciello
- Medical Physics, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Luca Boldrini
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
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Mesny E, Leporq B, Chapet O, Beuf O. Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions. Magn Reson Imaging 2024; 108:129-137. [PMID: 38354843 DOI: 10.1016/j.mri.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 02/16/2024]
Abstract
Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.
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Affiliation(s)
- Emmanuel Mesny
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France; Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France.
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
| | - Olivier Chapet
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France
| | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
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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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5
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, 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, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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Li Y, Zhang H, Yue L, Fu C, Grimm R, Li W, Guo W, Tong T. Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker. Eur J Radiol 2024; 170:111203. [PMID: 38007855 DOI: 10.1016/j.ejrad.2023.111203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/16/2023] [Accepted: 11/14/2023] [Indexed: 11/28/2023]
Abstract
PURPOSE To evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM). METHODS Patients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two-three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated. RESULTS Of the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652-0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586-0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537-0.86) in the validation set. CONCLUSIONS Baseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.
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Affiliation(s)
- Yue Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lei Yue
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Caixia Fu
- MR Collaboration, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Wenhua Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Weijian Guo
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Fukumura Y, Kuroda M, Yoshida S, Nakamura Y, Nakamitsu Y, Al-Hammad WE, Kuroda K, Kamizaki R, Shimizu Y, Tanabe Y, Sugimoto K, Oita M, Sugianto I, Barham M, Tekiki N, Kamaruddin NN, Yanagi Y, Asaumi J. Characteristic Mean Kurtosis Values in Simple Diffusion Kurtosis Imaging of Dentigerous Cysts. Diagnostics (Basel) 2023; 13:3619. [PMID: 38132203 PMCID: PMC10742570 DOI: 10.3390/diagnostics13243619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
We evaluated the usefulness of simple diffusion kurtosis (SD) imaging, which was developed to generate diffusion kurtosis images simultaneously with an apparent diffusion coefficient (ADC) map for 27 cystic disease lesions in the head and neck region. The mean kurtosis (MK) and ADC values were calculated for the cystic space. The MK values were dentigerous cyst (DC): 0.74, odontogenic keratocyst (OKC): 0.86, ranula (R): 0.13, and mucous cyst (M): 0, and the ADC values were DC: 1364 × 10-6 mm2/s, OKC: 925 × 10-6 mm2/s, R: 2718 × 10-6 mm2/s, and M: 2686 × 10-6 mm2/s. The MK values of DC and OKC were significantly higher than those of R and M, whereas their ADC values were significantly lower. One reason for the characteristic signal values in diffusion-weighted images of DC may be related to content components such as fibrous tissue and exudate cells. When imaging cystic disease in the head and neck region using SD imaging, the maximum b-value setting at the time of imaging should be limited to approximately 1200 s/mm2 for accurate MK value calculation. This study is the first to show that the MK values of DC are characteristically higher than those of other cysts.
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Affiliation(s)
- Yuka Fukumura
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
| | - Masahiro Kuroda
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Suzuka Yoshida
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
| | - Yoshihide Nakamura
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
| | - Yuki Nakamitsu
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Wlla E. Al-Hammad
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
- Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Kazuhiro Kuroda
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
- Department of Health and Welfare Science, Graduate School of Health and Welfare Science, Okayama Prefectural University, Okayama 719-1197, Japan
| | - Ryo Kamizaki
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Yudai Shimizu
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
| | - Yoshinori Tanabe
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
| | - Kohei Sugimoto
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan
- Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan
| | - Masataka Oita
- Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan
| | - Irfan Sugianto
- Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, Indonesia
| | - Majd Barham
- Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, Palestine
| | - Nouha Tekiki
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
| | - Nurul N. Kamaruddin
- Department of Oral Rehabilitation and Regenerative Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan
- Department of Dental Materials, Faculty of dentistry, Hasanuddin University, Sulawesi 90245, Indonesia
| | - Yoshinobu Yanagi
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
| | - Junichi Asaumi
- Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan; (Y.F.)
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Fusco R, Granata V. Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta-analysis". Eur J Radiol 2023; 169:111192. [PMID: 37976763 DOI: 10.1016/j.ejrad.2023.111192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 11/05/2023] [Indexed: 11/19/2023]
Abstract
We read with interest the article from Dr Jia LL and colleagues in Eur J Radiol in which they assessed the methodological quality of radiomics-based studies for non-invasive preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer. They systematically evaluated the prediction models diagnostic accuracy of twenty-nine studies between February 2014 and March 2022 and we congratulate the Authors on their accuracy in reporting recent published manuscript about radiomics-based studies to predict KRAS mutations in patients with colorectal cancer however they did not report the impact of contrast administration and the different phases of the contrast study (arterial, portal and transient phase) compared to the EOB phase in this research field.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples I-80131, Italy.
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, 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, Naples, Italy
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Petrillo A, Fusco R, Barretta ML, Granata V, Mattace Raso M, Porto A, Sorgente E, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Trombadori CML, Belli P, Trecate G, Tenconi C, De Santis MC, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Boldrini L. Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome. LA RADIOLOGIA MEDICA 2023; 128:1347-1371. [PMID: 37801198 DOI: 10.1007/s11547-023-01718-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/01/2023] [Indexed: 10/07/2023]
Abstract
OBJECTIVE The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. METHODS A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer. RESULTS The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.
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Affiliation(s)
- Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Maria Luisa Barretta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Mauro Mattace Raso
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annamaria Porto
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Eugenio Sorgente
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annarita Fanizzi
- Direzione Scientifica-IRCCS, Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- SSD Fisica Sanitaria-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miria Lafranceschina
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | | | - Paolo Belli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - Giovanna Trecate
- Department of Radiodiagnostic and Magnetic Resonance, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Chiara Tenconi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Maria Carmen De Santis
- De Santis Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Valeria De Soccio
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Botta
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
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De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [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: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
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Franco D, Granata V, Fusco R, Grassi R, Nardone V, Lombardi L, Cappabianca S, Conforti R, Briganti F, Grassi R, Caranci F. Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01655-0. [PMID: 37289266 DOI: 10.1007/s11547-023-01655-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). METHODS GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. RESULTS A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. CONCLUSIONS AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.
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Affiliation(s)
- Donatella Franco
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Research & Development and Medical Oncology Division, Igea SpA, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Department of Precision Medicine, "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
| | - Valerio Nardone
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Laura Lombardi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Salvatore Cappabianca
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renata Conforti
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Francesco Briganti
- Advanced Biomedical Sciences Department, Federico II University, Naples, Italy
| | - Roberto Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Ferdinando Caranci
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
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Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms. Clin Imaging 2023; 96:15-22. [PMID: 36736182 DOI: 10.1016/j.clinimag.2023.01.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/20/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs). METHODS This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m2). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs. RESULTS The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients. CONCLUSION The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, 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, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, Miele V. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence. BIOLOGY 2023; 12:biology12020213. [PMID: 36829492 PMCID: PMC9952965 DOI: 10.3390/biology12020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Salerno, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Orlando Catalano
- Radiology Unit, Istituto Diagnostico Varelli, Via Cornelia dei Gracchi 65, 80126 Naples, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56216 Pisa, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Federica Flammia
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federico Bruno
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Alessandro Ottaiano
- SSD Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori IRCCS-Fondazione G. Pascale, 80130 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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Gao PF, Lu N, Liu W. MRI VS. FDG-PET for diagnosis of response to neoadjuvant therapy in patients with locally advanced rectal cancer. Front Oncol 2023; 13:1031581. [PMID: 36741013 PMCID: PMC9890074 DOI: 10.3389/fonc.2023.1031581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/02/2023] [Indexed: 01/19/2023] Open
Abstract
Aim In this study, we aimed to compare the diagnostic values of MRI and FDG-PET for the prediction of the response to neoadjuvant chemoradiotherapy (NACT) of patients with locally advanced Rectal cancer (RC). Methods Electronic databases, including PubMed, Embase, and the Cochrane library, were systematically searched through December 2021 for studies that investigated the diagnostic value of MRI and FDG-PET in the prediction of the response of patients with locally advanced RC to NACT. The quality of the included studies was assessed using QUADAS. The pooled sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), and the area under the ROC (AUC) of MRI and FDG-PET were calculated using a bivariate generalized linear mixed model, random-effects model, and hierarchical regression. Results A total number of 74 studies with recruited 4,105 locally advanced RC patients were included in this analysis. The pooled sensitivity, specificity, PLR, NLR, and AUC for MRI were 0.83 (95% CI: 0.77-0.88), 0.85 (95% CI: 0.79-0.89), 5.50 (95% CI: 4.11-7.35), 0.20 (95% CI: 0.14-0.27), and 0.91 (95% CI: 0.88-0.93), respectively. The summary sensitivity, specificity, PLR, NLR and AUC for FDG-PET were 0.81 (95% CI: 0.77-0.85), 0.75 (95% CI: 0.70-0.80), 3.29 (95% CI: 2.64-4.10), 0.25 (95% CI: 0.20-0.31), and 0.85 (95% CI: 0.82-0.88), respectively. Moreover, there were no significant differences between MRI and FDG-PET in sensitivity (P = 0.565), and NLR (P = 0.268), while the specificity (P = 0.006), PLR (P = 0.006), and AUC (P = 0.003) of MRI was higher than FDG-PET. Conclusions MRI might superior than FGD-PET for the prediction of the response of patients with locally advanced RC to NACT.
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Affiliation(s)
- Peng Fei Gao
- Department of Traditional Chinese medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | - Na Lu
- Department of Radiology, Huashan Hospital North, Fudan University, Shanghai, China
| | - Wen Liu
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China,*Correspondence: Wen Liu,
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Granata V, Fusco R, Setola SV, Simonetti I, Picone C, Simeone E, Festino L, Vanella V, Vitale MG, Montanino A, Morabito A, Izzo F, Ascierto PA, Petrillo A. Immunotherapy Assessment: A New Paradigm for Radiologists. Diagnostics (Basel) 2023; 13:diagnostics13020302. [PMID: 36673112 PMCID: PMC9857844 DOI: 10.3390/diagnostics13020302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/31/2022] [Accepted: 01/08/2023] [Indexed: 01/14/2023] Open
Abstract
Immunotherapy denotes an exemplar change in an oncological setting. Despite the effective application of these treatments across a broad range of tumors, only a minority of patients have beneficial effects. The efficacy of immunotherapy is affected by several factors, including human immunity, which is strongly correlated to genetic features, such as intra-tumor heterogeneity. Classic imaging assessment, based on computed tomography (CT) or magnetic resonance imaging (MRI), which is useful for conventional treatments, has a limited role in immunotherapy. The reason is due to different patterns of response and/or progression during this kind of treatment which differs from those seen during other treatments, such as the possibility to assess the wide spectrum of immunotherapy-correlated toxic effects (ir-AEs) as soon as possible. In addition, considering the unusual response patterns, the limits of conventional response criteria and the necessity of using related immune-response criteria are clear. Radiomics analysis is a recent field of great interest in a radiological setting and recently it has grown the idea that we could identify patients who will be fit for this treatment or who will develop ir-AEs.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Carmine Picone
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Ester Simeone
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Lucia Festino
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Vito Vanella
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Maria Grazia Vitale
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Agnese Montanino
- Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Alessandro Morabito
- Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Paolo Antonio Ascierto
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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18
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Cutolo C, Fusco R, Simonetti I, De Muzio F, Grassi F, Trovato P, Palumbo P, Bruno F, Maggialetti N, Borgheresi A, Bruno A, Chiti G, Bicci E, Brunese MC, Giovagnoni A, Miele V, Barile A, Izzo F, Granata V. Imaging Features of Main Hepatic Resections: The Radiologist Challenging. J Pers Med 2023; 13:jpm13010134. [PMID: 36675795 PMCID: PMC9862253 DOI: 10.3390/jpm13010134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/31/2022] [Accepted: 01/06/2023] [Indexed: 01/12/2023] Open
Abstract
Liver resection is still the most effective treatment of primary liver malignancies, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), and of metastatic disease, such as colorectal liver metastases. The type of liver resection (anatomic versus non anatomic resection) depends on different features, mainly on the type of malignancy (primary liver neoplasm versus metastatic lesion), size of tumor, its relation with blood and biliary vessels, and the volume of future liver remnant (FLT). Imaging plays a critical role in postoperative assessment, offering the possibility to recognize normal postoperative findings and potential complications. Ultrasonography (US) is the first-line diagnostic tool to use in post-surgical phase. However, computed tomography (CT), due to its comprehensive assessment, allows for a more accurate evaluation and more normal findings than the possible postoperative complications. Magnetic resonance imaging (MRI) with cholangiopancreatography (MRCP) and/or hepatospecific contrast agents remains the best tool for bile duct injuries diagnosis and for ischemic cholangitis evaluation. Consequently, radiologists should be familiar with the surgical approaches for a better comprehension of normal postoperative findings and of postoperative complications.
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Affiliation(s)
- Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
- Correspondence:
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Piero Trovato
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
| | - Alessandra Bruno
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
| | - Giuditta Chiti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Eleonora Bicci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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Sansone M, Fusco R, Grassi F, Gatta G, Belfiore MP, Angelone F, Ricciardi C, Ponsiglione AM, Amato F, Galdiero R, Grassi R, Granata V, Grassi R. Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography. Curr Oncol 2023; 30:839-853. [PMID: 36661713 PMCID: PMC9858566 DOI: 10.3390/curroncol30010064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/31/2022] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to significant improvement in patients' survival. The Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies, it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level. OBJECTIVE in this study, the focus is the development of mammographic image processing techniques that allow the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factors. METHODS a total of 168 patients were enrolled in the internal training and test set while a total of 51 patients were enrolled to compose the external validation cohort. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms. RESULTS the accuracy of different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, as the best classifier with the 7 features selected by a wrapper method, showed an accuracy of 0.95, a sensitivity of 0.96, and a specificity of 0.90. CONCLUSIONS our preliminary results showed that the Radiomics analysis and ML approach allow us to objectively identify BD.
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Affiliation(s)
- Mario Sansone
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Francesca Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Gianluca Gatta
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Francesca Angelone
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Alfonso Maria Ponsiglione
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Francesco Amato
- Department of Electrical Engineering Information Technology, University of Naples Federico II, 80125 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberto Grassi
- Department of Precision Medicine, Division of Radiology, University of Campania Luigi Vanvitelli, 80127 Naples, Italy
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Silvestro L, De Bellis M, Di Girolamo E, Grazzini G, Chiti G, Brunese MC, Belli A, Patrone R, Palaia R, Avallone A, Petrillo A, Izzo F. Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence. Cancers (Basel) 2023; 15:351. [PMID: 36672301 PMCID: PMC9857317 DOI: 10.3390/cancers15020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 41012 Napoli, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Lucrezia Silvestro
- Division of Clinical Experimental Oncology Abdomen, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Mario De Bellis
- Division of Gastroenterology and Digestive Endoscopy, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Elena Di Girolamo
- Division of Gastroenterology and Digestive Endoscopy, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Giulia Grazzini
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Giuditta Chiti
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Andrea Belli
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Raffaele Palaia
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Antonio Avallone
- Division of Clinical Experimental Oncology Abdomen, 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, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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Zhang J, Xing X, Wang Q, Chen Y, Yuan H, Lang N. Preliminary study of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted MRI and diffusion kurtosis imaging on differential diagnosis of spinal metastases and chordoma. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:3130-3138. [PMID: 35648206 DOI: 10.1007/s00586-022-07269-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/03/2022] [Accepted: 05/15/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Quantitative comparison of diffusion parameters from various models of diffusion-weighted (DWI) and diffusion kurtosis (DKI) imaging for distinguishing spinal metastases and chordomas. METHODS DWI and DKI examinations were performed in 31 and 13 cases of spinal metastases and chordomas, respectively. DWI derived apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), water molecular distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α). DKI derived mean diffusivity (MD) and mean kurtosis (MK). Independent sample t-testing compared statistical differences among parameters. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis evaluated the parameters' correlations. RESULTS ADC, D, f, DDC, α, and MD were significantly lower in spinal metastases than chordomas (all P < 0.05). MK was significantly higher in spinal metastases than chordomas (P < 0.05). D had the highest area under the ROC curve (AUC) of 0.886, greater than MD (AUC = 0.706) or DDC (AUC = 0.742) in differentiating the two tumors (both P < 0.05). Combining D with f and α statistically significantly increased the AUC for diagnosis (to 0.995) relative to D alone (P < 0.05). There was a certain correlation among DDC, ADC, and D (all P < 0.05). CONCLUSIONS Monoexponential, biexponential, and stretched-exponential models of DWI and DKI can potentially differentiate spinal metastases and chordomas. D combined with f and α performed best.
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Affiliation(s)
- Jiahui Zhang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Qizheng Wang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
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22
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Multimodality Imaging Assessment of Desmoid Tumors: The Great Mime in the Era of Multidisciplinary Teams. J Pers Med 2022; 12:jpm12071153. [PMID: 35887650 PMCID: PMC9319486 DOI: 10.3390/jpm12071153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 11/17/2022] Open
Abstract
Desmoid tumors (DTs), also known as desmoid fibromatosis or aggressive fibromatosis, are rare, locally invasive, non-metastatic soft tissue tumors. Although histological results represent the gold standard diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. Although histological analysis represents the gold standard for diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. DTs represent a challenge for the radiologist, being able to mimic different pathological conditions. A proper diagnosis is required to establish an adequate therapeutic approach. Multimodality imaging, including ultrasound (US), computed tomography (CT) and Magnetic Resonance Imaging (MRI), should be preferred. Different imaging techniques can also guide minimally invasive treatments and monitor their effectiveness. The purpose of this review is to describe the state-of-the-art multidisciplinary imaging of DTs; and its role in patient management.
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Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022; 17:25. [PMID: 35681237 PMCID: PMC9185934 DOI: 10.1186/s13027-022-00441-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/30/2022] [Indexed: 12/13/2022] Open
Abstract
This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy.
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
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24
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 2022; 127:763-772. [PMID: 35653011 DOI: 10.1007/s11547-022-01501-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/27/2022] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures. RESULTS The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model. CONCLUSIONS Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Fisciano, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Federica Dell'Aversana
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Lucrezia Silvestro
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Antonio Avallone
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Federica Flammia
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy
| | - Fabiana Tatangelo
- Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, 80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, 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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25
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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: 7.0] [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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26
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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: 1.3] [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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27
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Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS. The Conversion of MRI Data With Multiple b-Values into Signature-Like Pictures to Predict Treatment Response for Rectal Cancer. J Magn Reson Imaging 2021; 56:562-569. [PMID: 34913210 DOI: 10.1002/jmri.28033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/05/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Diffusion weighted imaging (DWI) at multiple b-values has been used to predict the pathological complete response (pCR) to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Non-Gaussian models fit the signal decay of diffusion by several physical values from different approaches of approximation. PURPOSE To develop a deep learning method to analyze DWI data scanned at multiple b-values independent on Gaussian or non-Gaussian models and to apply to a rectal cancer neoadjuvant chemoradiotherapy model. STUDY TYPE Retrospective. POPULATION A total of 472 participants (age: 56.6 ± 10.5 years; 298 males and 174 females) with locally advanced adenocarcinoma were enrolled and chronologically divided into a training group (n = 200; 42 pCR/158 non-pCR), a validation group (n = 72; 11 pCR/61 non-pCR) and a test group (n = 200; 44 pCR/156 non-pCR). FIELD STRENGTH/SEQUENCE A 3.0 T MRI scanner. DWI with a single-shot spin echo-planar imaging pulse sequence at 12 b-values (0, 20, 50, 100, 200, 400, 600, 800, 1000, 1200, 1400, and 1600 sec/mm2 ). ASSESSMENT DWI signals from manually delineated tumor region were converted into a signature-like picture by concatenating all histograms from different b-values. Pathological results (pCR/non-pCR) were used as the ground truth for deep learning. Gaussian and non-Gaussian methods were used for comparison. STATISTICAL TESTS Analysis of variance for age; Chi-square for gender and pCR/non-pCR; area under the receiver operating characteristic (ROC) curve (AUC); DeLong test for AUC. P < 0.05 for significant difference. RESULTS The AUC in the test group is 0.924 (95% CI: 0.866-0.983) for the signature-like pictures converted from 35 bins, and it is 0.931 (95% CI: 0.884-0.979) for the signature-like pictures converted from 70 bins, which is significantly (Z = 3.258, P < 0.05) larger than Dapp , the best predictor in non-Gaussian methods with AUC = 0.773 (95% CI: 0.682-0.865). DATA CONCLUSION The proposed signature-like pictures provide more accurate pretreatment prediction of the response to neoadjuvant chemoradiotherapy than the fitted methods for locally advanced rectal cancer. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Xiao-Yan Zhang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing, China
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28
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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29
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Shi YJ, Li XT, Zhang XY, Zhu HT, Liu YL, Wei YY, Sun YS. Non-gaussian models of 3-Tesla diffusion-weighted MRI for the differentiation of pancreatic ductal adenocarcinomas from neuroendocrine tumors and solid pseudopapillary neoplasms. Magn Reson Imaging 2021; 83:68-76. [PMID: 34314825 DOI: 10.1016/j.mri.2021.07.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/23/2021] [Accepted: 07/20/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE To assess the MRI performance in differentiating pancreatic ductal adenocarcinomas (PDACs), from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine tumors (PNETs) using non-gaussian diffusion-weighted imaging models. METHODS This was a retrospective study of patients diagnosed with PDACs (01/2015-06/2019) or with PNETs or SPNs diagnosed (01/2011-12/2019) at our hospital. The lesions were randomized 1:1 to the primary and validation cohorts. The regions of interest (ROIs) were manually drawn on each slice at DWI (b = 1500 s/mm2) from 3 T MRI. D (diffusion coefficient), D* (pseudodiffusion coefficient), f (perfusion fraction), distributed diffusion coefficient (DDC), α (diffusion heterogeneity index), mean diffusivity (MD) and mean kurtosis (MK) were obtained. The parameters with largest performance for differentiation were used to establish a diagnostic model. RESULTS There were 148, 56, and 60 patients with PDAC, PNET, and SPN, respectively. For differentiating PDACs from SPNs, f and MK values were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation groups, respectively. For distinguishing PDACs from PNETs, α and MK values were used to establish a diagnostic model with AUCs of 0.87 and 0.86 in the primary and validation groups, respectively. The accuracy rate of the subjective evaluation with the assistance of non-gaussian DWI models for differentiating PDAC from SPNs and PNETs were higher than that of subjective diagnosis alone (P < 0.05). CONCLUSIONS The non-gaussian DWI models could assist radiologists in accurately differentiating PDACs from PNETs and SPNs.
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Affiliation(s)
- Yan-Jie Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Xiao-Ting Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Xiao-Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Hai-Tao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Yu-Liang Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Yi-Yuan Wei
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China.
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30
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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: 3.5] [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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31
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Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY, Ding Y. MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends. Cancer Manag Res 2021; 13:4317-4328. [PMID: 34103987 PMCID: PMC8179813 DOI: 10.2147/cmar.s309252] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/08/2021] [Indexed: 12/29/2022] Open
Abstract
Complete tumor response can be achieved in a certain proportion of patients with locally advanced rectal cancer, who achieve maximal response to neoadjuvant therapy (NAT). For these patients, a watch-and-wait (WW) or nonsurgical strategy has been proposed and is becoming widely practiced in order to avoid unnecessary surgical complications. Therefore, a non-invasive, reliable diagnostic tool for accurately evaluating complete tumor response is needed. Magnetic resonance imaging (MRI) plays a crucial role in both primary staging and restaging tumor response to NAT in rectal cancer without relying on resected specimen. In recent years, numerous efforts have been made to research the value of MRI in predicting and evaluating complete response in rectal cancer. Current MRI evaluation is mainly based on morphological and functional images. Morphologic MRI yields high soft tissue resolution, multiplanar images, and provides detailed depictions of rectal cancer and its surrounding structures. Functional MRI may help to distinguish residual tumor from fibrosis, therefore improving the diagnostic performance of morphologic MRI in identifying complete tumor response. Both morphologic and functional MRI have several promising parameters that may help accurately evaluate and/or predict complete response of rectal cancer. However, these parameters still have limitations and the results remain inconsistent. Recent development of new techniques, such as textural analysis, radiomics analysis and deep learning, demonstrate great potential based on MRI-derived parameters. This article aimed to review and help better understand the strengths, limitations, and future trends of these MRI-derived methods in evaluating complete response in rectal cancer.
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Affiliation(s)
- Qiaoyu Xu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yanyan Xu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Hongliang Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Tao Jiang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Bee Yen Ooi
- Department of Radiology, Hospital Seberang Jaya, Penang, Malaysia
| | - Yi Ding
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
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Validation of the standardized index of shape tool to analyze DCE-MRI data in the assessment of neo-adjuvant therapy in locally advanced rectal cancer. Radiol Med 2021; 126:1044-1054. [PMID: 34041663 DOI: 10.1007/s11547-021-01369-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/05/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Standardized index of shape (SIS) tool validation to examine dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in preoperative chemo-radiation therapy (pCRT) assessment of locally advanced rectal cancer (LARC) in order to guide the surgeon versus more or less conservative treatment. MATERIALS AND METHODS A total of 194 patients (January 2008-November 2020), with III-IV locally advanced rectal cancer and subjected to pCRT were included. Three expert radiologists performed DCE-MRI analysis using SIS tool. Degree of absolute agreement among measurements, degree of consistency among measurements, degree of reliability and level of variability were calculated. Patients with a pathological tumour regression grade (TRG) 1 or 2 were classified as major responders (complete responders have TRG 1). RESULTS Good significant correlation was obtained between SIS measurements (range 0.97-0.99). The degree of absolute agreement ranges from 0.93 to 0.99, the degree of consistency from 0.81 to 0.9 and the reliability from 0.98 to 1.00 (p value < < 0.001). The variability coefficient ranges from 3.5% to 26%. SIS value obtained to discriminate responders by non-responders a sensitivity of 95.9%, a specificity of 84.7% and an accuracy of 91.8% while to detect complete responders, a sensitivity of 99.2%, a specificity of 63.9% and an accuracy of 86.1%. CONCLUSION SIS tool is suitable to assess pCRT response both to identify major responders and complete responders in order to guide the surgeon versus more or less conservative treatment.
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Nardi C, Tomei M, Pietragalla M, Calistri L, Landini N, Bonomo P, Mannelli G, Mungai F, Bonasera L, Colagrande S. Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging. Eur J Radiol 2021; 136:109529. [PMID: 33453571 DOI: 10.1016/j.ejrad.2021.109529] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/02/2020] [Accepted: 01/05/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY). METHODS Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient. RESULTS The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively. CONCLUSIONS Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.
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Affiliation(s)
- Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Maddalena Tomei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Michele Pietragalla
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Nicholas Landini
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy; Department of Radiology, Ca' Foncello General Hospital.Piazzale Ospedale 1, 31100, Treviso, Italy.
| | - Pierluigi Bonomo
- Radiation Oncology, University of Florence - Azienda Ospedaliero-Universitaria Careggi. Largo Brambilla 3, 50134, Florence, Italy.
| | - Giuditta Mannelli
- Department of Experimental and Clinical Medicine, Head and Neck Oncology and Robotic Surgery, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Palagi 1, 50134, Florence, Italy.
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Stefano Colagrande
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
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Yang L, Xia C, Zhao J, Zhou X, Wu B. The value of intravoxel incoherent motion and diffusion kurtosis imaging in the assessment of tumor regression grade and T stages after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. Eur J Radiol 2020; 136:109504. [PMID: 33421885 DOI: 10.1016/j.ejrad.2020.109504] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/09/2020] [Accepted: 12/20/2020] [Indexed: 02/08/2023]
Abstract
PURPOSE To evaluate the role of IVIM and diffusion kurtosis imaging (DKI) in identifying pathologic complete response (pCR) and T stages after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). METHOD Forty-two patients with biopsy-proven rectal adenocarcinoma, who underwent both pre-and post-CRT MRI with IVIM and DKI sequences on a 3 T scanner, were enrolled prospectively. According to the pathologic ypTNM stages and tumor regression grade (TRG), patients were grouped into pCR (TRG0) and non-pCR (TRG1-3) groups and low T stage (ypT0-2) and high T stage (ypT3-4) groups. IVIM parameters (the slow diffusion coefficient [D], fast diffusion coefficient [D*], perfusion fraction [f]), DKI parameters (mean diffusivity [MD] and mean kurtosis [MK]), and mono-exponential ADC were calculated and analyzed between groups. RESULTS The pCR group had significantly higher post-CRT ADC, D*, f, and MD values than non-pCR group, and higher percent changes in the ADC, f, and MD values (all P < 0.05). The post-CRT MD values yielded the highest AUC (0.788) with higher sensitivity than post-ADC values (82.9 % vs. 77.1 %, respectively). Post-CRT ADC and MD values and the percent changes in the ADC and MD values were also negatively correlated with TRG (all P < 0.05). Besides, negative correlations were found among the pre-CRT MD, post-CRT ADC, D, f, and MD values and the ypT stages (all P < 0.05). CONCLUSIONS Both IVIM and DKI parameters could provide more information when evaluating pCR and T stages after nCRT. In particular, the diagnostic performance of the MD values was more valuable than ADC values in being able to determine pCR.
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Affiliation(s)
- Lanqing Yang
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China
| | - Chunchao Xia
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China
| | - Jin Zhao
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China
| | - Xiaoyue Zhou
- MR Collaboration, Siemens Healthcare Ltd., Shanghai, PR China
| | - Bing Wu
- From the Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China.
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Fusco R, Granata V, Petrillo A. Introduction to Special Issue of Radiology and Imaging of Cancer. Cancers (Basel) 2020; 12:E2665. [PMID: 32961946 PMCID: PMC7565136 DOI: 10.3390/cancers12092665] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 09/15/2020] [Indexed: 12/12/2022] Open
Abstract
The increase in knowledge in oncology and the possibility of creating personalized medicine by selecting a more appropriate therapy related to the different tumor subtypes, as well as the management of patients with cancer within a multidisciplinary team has improved the clinical outcomes [...].
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Affiliation(s)
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori—IRCCS—Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (R.F.); (A.P.)
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The Effect of Rectal Distention on the Intravoxel Incoherent Motion Parameters: Using Sonography Transmission Gel. J Comput Assist Tomogr 2020; 44:759-765. [DOI: 10.1097/rct.0000000000001083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Shi YJ, Zhu HT, Liu YL, Wei YY, Qin XB, Zhang XY, Li XT, Sun YS. Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors. Front Oncol 2020; 10:1624. [PMID: 32974201 PMCID: PMC7473210 DOI: 10.3389/fonc.2020.01624] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 07/27/2020] [Indexed: 12/31/2022] Open
Abstract
Objective To develop and validate a radiomics model of diffusion kurtosis imaging (DKI) and T2 weighted imaging for discriminating pancreatic neuroendocrine tumors (PNETs) from solid pseudopapillary tumors (SPTs). Materials and Methods Sixty-six patients with histopathological confirmed PNETs (n = 31) and SPTs (n = 35) were enrolled in this study. ROIs of tumors were manually drawn on each slice at T2WI and DWI (b = 1,500 s/mm2) from 3T MRI. Intraclass correlation coefficients were used to evaluate the interobserver agreement. Mean diffusivity (MD) and mean kurtosis (MK) were derived from DKI. The least absolute shrinkage and selection operator regression were used for feature selection. Results MD and MK had a moderate diagnostic performancewith the area under curve (AUC) of 0.71 and 0.65, respectively. A radiomics model, which incorporated sex and age of patients and radiomics signature of the tumor, showed excellent discrimination performance with AUC of 0.97 and 0.86 in the primary and validation cohort. Moreover, the new model had better diagnostic performance than that of MD (P = 0.023) and MK (P = 0.004), and showed excellent differentiation with a sensitivity of 95.00% and specificity of 91.67% in primary cohort, and the sensitivity of 90.91% and specificity of 81.82% in the validation cohort. The accuracy of radiomics analysis, radiologist 1, and radiologist 2 for diagnosing SPTs and PNETs were 92.42, 77.27, and 78.79%, respectively. The accuracy of radiomics analysis was significantly higher than that of subjective diagnosis (P < 0.05). Conclusions Radiomics model could improve the diagnostic accuracy of SPTs and PNETs and contribute to determining an appropriate treatment strategy for pancreatic tumors.
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Affiliation(s)
- Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Yu-Liang Liu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Yi-Yuan Wei
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Xiu-Bo Qin
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Xiao-Yan Zhang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, China
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The role of diffusion-weighted and dynamic contrast enhancement perfusion-weighted imaging in the evaluation of salivary glands neoplasms. Radiol Med 2020; 125:851-863. [PMID: 32266692 DOI: 10.1007/s11547-020-01182-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 03/23/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To evaluate the association of magnetic resonance diffusion-weighted imaging (DwI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PwI) with a temporal resolution of 5 s, wash-in < 120 s, and wash-out ratio > 30% in the evaluation of salivary glands neoplasms. METHODS DwI and DCE-PwI of 92 salivary glands neoplasms were assessed. The apparent diffusion coefficient (ADC) was calculated by drawing three regions of interest with an average area of 0.30-0.40 cm2 on three contiguous axial sections. The time/intensity curve was generated from DCE-PwI images by drawing a region of interest that included at least 50% of the largest lesion section. Vessels, calcifications, and necrotic/haemorrhagic or cystic areas within solid components were excluded. The association of ADC ≥ 1.4 × 10-3 mm2/s with type A curves (progressive wash-in) and ADC 0.9-1.4 × 10-3 mm2/s with type C curves (rapid wash-in/slow wash-out) were tested as parameters of benignity and malignancy, respectively. Type B curve (rapid wash-in/rapid wash-out) was not used as a reference parameter. RESULTS ADC ≥ 1.4 × 10-3 mm2/s and type A curves were observed only in benign neoplasms. ADC of 0.9-1.4 × 10-3 mm2/s and type C curves association showed specificity of 94.9% and positive predictive value of 81.8% for epithelial malignancies. The association of ADC < 0.9 × 10-3 mm2/s with type B and C curves showed diagnostic accuracy of 94.6% and 100% for Warthin tumour and lymphoma, respectively. CONCLUSIONS ADC ≥ 1.4 × 10-3 mm2/s and type A curves association was indicative of benignity. Lymphomas exhibited ADC < 0.7 × 10-3 mm2/s and type C curves. The association of ADC < 0.9 × 10-3 mm2/s and type B and C curves had accuracy 94.6% and 88.5% for Warthin tumour and epithelial malignancies, respectively.
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Fusco R, Granata V, Maio F, Sansone M, Petrillo A. Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data. Eur Radiol Exp 2020; 4:8. [PMID: 32026095 PMCID: PMC7002809 DOI: 10.1186/s41747-019-0141-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 12/05/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND To investigate the potential of semiquantitative time-intensity curve parameters compared to textural radiomic features on arterial phase images by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for early prediction of breast cancer neoadjuvant therapy response. METHODS A retrospective study of 45 patients subjected to DCE-MRI by public datasets containing examination performed prior to the start of treatment and after the treatment first cycle ('QIN Breast DCE-MRI' and 'QIN-Breast') was performed. In total, 11 semiquantitative parameters and 50 texture features were extracted. Non-parametric test, receiver operating characteristic analysis with area under the curve (ROC-AUC), Spearman correlation coefficient, and Kruskal-Wallis test with Bonferroni correction were applied. RESULTS Fifteen patients with pathological complete response (pCR) and 30 patients with non-pCR were analysed. Significant differences in median values between pCR patients and non-pCR patients were found for entropy, long-run emphasis, and busyness among the textural features, for maximum signal difference, washout slope, washin slope, and standardised index of shape among the dynamic semiquantitative parameters. The standardised index of shape had the best results with a ROC-AUC of 0.93 to differentiate pCR versus non-pCR patients. CONCLUSIONS The standardised index of shape could become a clinical tool to differentiate, in the early stages of treatment, responding to non-responding patients.
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Affiliation(s)
- Roberta Fusco
- Radiology Division, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, Naples, Italy.
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, Naples, Italy
| | - Francesca Maio
- Radiology Division, Universita' Degli Stui di Napoli Federico II, Via Pansini, Naples, Italy
| | - Mario Sansone
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II, Via Claudio, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Via Mariano Semmola, Naples, Italy
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Bates DDB, Mazaheri Y, Lobaugh S, Golia Pernicka JS, Paroder V, Shia J, Zheng J, Capanu M, Petkovska I, Gollub MJ. Evaluation of diffusion kurtosis and diffusivity from baseline staging MRI as predictive biomarkers for response to neoadjuvant chemoradiation in locally advanced rectal cancer. Abdom Radiol (NY) 2019; 44:3701-3708. [PMID: 31154482 DOI: 10.1007/s00261-019-02073-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the role of diffusion kurtosis and diffusivity as potential imaging biomarkers to predict response to neoadjuvant chemoradiation therapy (CRT) from baseline staging magnetic resonance imaging (MRI) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS This retrospective study included 45 consecutive patients (31 male/14 female) who underwent baseline MRI with high b-value sequences (up to 1500 mm/s2) for LARC followed by neoadjuvant chemoradiation and surgical resection. The mean age was 57.4 years (range 34.2-72.9). An abdominal radiologist using open source software manually segmented T2-weighted images. Segmentations were used to derive diffusion kurtosis and diffusivity from diffusion-weighted images as well as volumetric data. These data were analyzed with regard to tumor regression grade (TRG) using the four-tier American Joint Committee on Cancer (AJCC) classification, TRG 0-3. Proportional odds regression was used to analyze the four-level ordinal outcome. A sensitivity analysis was performed using univariable logistic regression for binary TRG groups, TRG 0/1 (> 90% response), or TRG 2/3 (< 90% response). p < 0.05 was considered significant throughout. RESULTS In the univariable proportional odds regression analysis, higher diffusivity summary (Dsum) values were observed to be significantly associated with higher odds of being in one or more favorable TRG group (TRG 0 or 1). In other words, on average, patients with higher Dsum values were more likely to be in a more favorable TRG group. These results are mostly consistent with the sensitivity analysis, in which higher values for most Dsum values [all but region of interest (ROI)-max D median (p = 0.08)] were observed to be significantly associated with higher odds of being TRG 0 or 1. Tumor volume of interest (VOI) and ROI volume, ROI kurtosis mean and median, and VOI kurtosis mean and median were not significantly associated with TRG. CONCLUSION Diffusivity derived from the baseline staging MRI, but not diffusion kurtosis or volumetric data, is associated with TRG and therefore shows promise as a potential imaging biomarker to predict the response to neoadjuvant chemotherapy in LARC. CLINICAL RELEVANCE STATEMENT Diffusivity shows promise as a potential imaging biomarker to predict AJCC TRG following neoadjuvant CRT, which has implications for risk stratification. Patients with TRG 0/1 have 5-year disease-free survival (DFS) of 90-98%, as opposed to those who are TRG 2/3 with 5-year DFS of 68-73%.
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Affiliation(s)
- David D B Bates
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| | - Yousef Mazaheri
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Stephanie Lobaugh
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer S Golia Pernicka
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Viktoriya Paroder
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Junting Zheng
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marinela Capanu
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Iva Petkovska
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Marc J Gollub
- Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
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Do DWI and quantitative DCE perfusion MR have a prognostic value in high-grade serous ovarian cancer? Radiol Med 2019; 124:1315-1323. [PMID: 31473928 DOI: 10.1007/s11547-019-01075-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 08/13/2019] [Indexed: 01/23/2023]
Abstract
PURPOSE To evaluate whether perfusion and diffusion parameters from staging MR in ovarian cancer (OC) patients may predict the presence of residual tumor at surgery and the progression-free survival (PFS) in 12 months. MATERIALS AND METHODS Patients who are from a single institution, candidate for OC to cytoreductive surgery and undergoing MR for staging purposes were included in this study. Inclusion criteria were: preoperative MR including diffusion-weighted imaging (DWI) and perfusion dynamic contrast-enhanced (DCE) sequence; cytoreductive surgery performed within a month from MR; and minimum follow-up of 12 months. Patients' characteristics including the presence of residual tumor at surgery (R0 or R1) and relapse within 12 months from surgery were recorded. DWI parameters included apparent diffusion coefficient (ADC) of the largest ovarian mass (O-ADC) and normalized ovarian ADC as a ratio between ovarian ADC and muscle ADC (M-ADC). DCE quantitative parameters included were descriptors of tumor vascular properties such as forward and backward transfer constants, plasma volume and volume of extracellular space. Statistical analysis was performed, and p values < 0.05 were considered significant. RESULTS Forty-nine patients were included. M-ADC showed a slightly significant association with the presence of residual tumor at surgery. None of the other functional parameters showed either difference between R0 and R1 patients or association with PFS in the first 12 months. CONCLUSIONS This preliminary study demonstrated a slightly significant association between normalized ovarian ADC and the presence of residual tumor at surgery. The other perfusion and diffusion parameters were not significant for the endpoints of this study.
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Liu S, Wen L, Hou J, Nie S, Zhou J, Cao F, Lu Q, Qin Y, Fu Y, Yu X. Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging. Abdom Radiol (NY) 2019; 44:2689-2698. [PMID: 31030244 DOI: 10.1007/s00261-019-02032-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). METHODS Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR. RESULTS The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower CorrelatD value. Higher CorrelatD*, Correlatf, SumAvergADC, and SumAvergD values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, CorrelatD* (P < 0.001), DifVarncADC (P = 0.024), and DifVarncD (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%. CONCLUSION Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.
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Affiliation(s)
- Siye Liu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Lu Wen
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Jing Hou
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Shaolin Nie
- Department of Colorectal Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Jumei Zhou
- Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Fang Cao
- Department of Pathology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Qiang Lu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Yuhui Qin
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China
| | - Yi Fu
- Department of Medical Service, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410006, Hunan, People's Republic of China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, the Affiliated Cancer Hospital of Xiangya School of Medicine & Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410006, Hunan, People's Republic of China.
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Granata V, Fusco R, Reginelli A, Delrio P, Selvaggi F, Grassi R, Izzo F, Petrillo A. Diffusion kurtosis imaging in patients with locally advanced rectal cancer: current status and future perspectives. J Int Med Res 2019; 47:2351-2360. [PMID: 31032670 PMCID: PMC6567719 DOI: 10.1177/0300060519827168] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Morphological magnetic resonance imaging is currently the best imaging technique for local staging in patients with rectal cancer. However, morphological sequences have some limitations, especially after preoperative chemoradiotherapy (pCRT). Diffusion-weighted imaging has been applied to rectal cancer for detection of lesions, characterization of tissue, and evaluation of the response to therapy. In 2005, a non-Gaussian diffusion model called diffusion kurtosis imaging (DKI) was suggested. Several electronic databases were evaluated in the present review. The search included articles published from January 2000 to May 2018. The references of all articles were also evaluated. All titles and abstracts were assessed, and only the studies of DKI in patients with rectal cancer were retained. We identified 35 potentially relevant references through the electronic search. According to the inclusion and exclusion criteria, we retained five clinical studies that met the inclusion criteria. DKI is a useful tool for assessment of tumor aggressiveness, the nodal status, and the risk of early metastases as well as prediction of the response to pCRT. The results of DKI should be considered in treatment decision-making during the work-up of patients with rectal cancer.
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Affiliation(s)
- Vincenza Granata
- 1 Division of Radiology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Roberta Fusco
- 1 Division of Radiology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Alfonso Reginelli
- 2 Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Paolo Delrio
- 3 Division of Gastrointestinal Surgical Oncology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Francesco Selvaggi
- 4 Division of Colorectal Surgery, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Roberto Grassi
- 2 Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Francesco Izzo
- 5 Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
| | - Antonella Petrillo
- 1 Division of Radiology, Istituto Nazionale Tumori - IRCCS "Fondazione G. Pascale," Napoli, Italy
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Granata V, Fusco R, Setola SV, Palaia R, Albino V, Piccirillo M, Grimm R, Petrillo A, Izzo F. Diffusion kurtosis imaging and conventional diffusion weighted imaging to assess electrochemotherapy response in locally advanced pancreatic cancer. Radiol Oncol 2019; 53:15-24. [PMID: 30681974 PMCID: PMC6411027 DOI: 10.2478/raon-2019-0004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 11/18/2018] [Indexed: 02/06/2023] Open
Abstract
Background The aim of the study was to evaluate diagnostic performance of functional parameters derived by conventional mono-exponential approach of diffusion weighted imaging (DWI) and by diffusion kurtosis imaging (DKI) in the assessment of pancreatic tumours treated with electrochemotherapy (ECT). Patients and methods Twenty-one consecutive patients with locally advanced pancreatic adenocarcinoma subjected to ECT were enrolled in a clinical approved trial. Among twenty-one enrolled patients, 13/21 (61.9%) patients were subjected to MRI before and after ECT. DWI was performed with a 1.5 T scanner; a free breathing axial single shot echo planar DWI pulse sequence parameters were acquired using seven b value = 0, 50, 100, 150, 400, 800, 1000 s/mm2. Apparent diffusion coefficient by conventional mono-exponential approach and mean of diffusion coefficient (MD) and mean of diffusional kurtosis (MK) by DKI approach were derived from DWI. Receiver operating characteristic (ROC) analysis was performed and sensitivity, specificity, positive and negative predictive value were calculated. Results Among investigated diffusion parameters, only the MD derived by DKI showed a significant variation of values between pre and post treatment (p = 0.02 at Wilcoxon test) and a significant statistically difference for percentage change between responders and not responders (p = 0.01 at Kruskal Wallis test). MD had a good diagnostic performance with a sensitivity of 80%, a specificity of 100% and area under ROC of 0.933. Conclusions MD derived by DKI allows identifying responders and not responders patients subject to ECT treatment. MD had higher diagnostic performance to assess ECT response compared to conventional DWI derived parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia
- Vincenza Granata, Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia. Phone: +39 081 5903 714; Fax:+39 0815903825;
| | | | - Sergio Venanzio Setola
- Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia
| | - Raffaele Palaia
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
| | - Vittorio Albino
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
| | - Mauro Piccirillo
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
| | | | - Antonella Petrillo
- Division of Radiology, Instituto Nazionale Tumori – IRCCS – Fondazione G. Pascale, Napoli, Italia
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Unit, Instituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, Napoli, Italia
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