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Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis. Curr Oncol Rep 2023; 25:257-267. [PMID: 36749493 DOI: 10.1007/s11912-023-01372-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting. RECENT FINDINGS Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.
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Kataoka M, Iima M, Miyake KK, Matsumoto Y. Multiparametric imaging of breast cancer: An update of current applications. Diagn Interv Imaging 2022; 103:574-583. [DOI: 10.1016/j.diii.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 11/21/2022]
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Armani M, Carton M, Tardivon A. Lésions mammaires ACR 3 en IRM chez des femmes à très haut risque de cancer du sein : analyse rétrospective sur trois ans. IMAGERIE DE LA FEMME 2022. [DOI: 10.1016/j.femme.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Vilar VS, Gomes AI, Federicci ÉEF, Ribeiro RLDM, Rudner MA, Racy ACS. FAST breast magnetic resonance imaging: a new approach for breast cancer screening? EINSTEIN-SAO PAULO 2022; 20:eAO0073. [PMID: 35857951 PMCID: PMC9278930 DOI: 10.31744/einstein_journal/2022ao0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022] Open
Abstract
Objective To develop an abbreviated breast magnetic resonance imaging protocol (FAST) and to compare it with the complete protocol (FULL) to determine its diagnostic accuracy for detecting malignant or suspicious lesions (BI-RADS 4, 5 and 6) and the time required for image interpretation using BI-RADS categorization. Methods Retrospective study with 100 consecutive women who underwent breast magnetic resonance imaging between January and February 2014. All patients were submitted to a complete breast magnetic resonance imaging protocol, which was then compared with an abbreviated protocol (pre-contrast sequence, second post-contrast sequence and subtraction of pre- from post-contrast images). Results Of 100 patients, 4 were classified as BI-RADS 5 or 6 and 16 as BI-RADS 4. In these 20 patients, there was full agreement among readers regarding the final BI-RADS categorization in both (FAST and FULL) protocols. Conclusion The FAST protocol reduces interpretation time without compromising the accuracy of the method for detection of malignant or suspicious lesions.
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Park HS, Lee KS, Seo BK, Kim ES, Cho KR, Woo OH, Song SE, Lee JY, Cha J. Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients. Cancers (Basel) 2021; 13:cancers13236013. [PMID: 34885124 PMCID: PMC8656976 DOI: 10.3390/cancers13236013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/17/2021] [Accepted: 11/27/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine. Abstract This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.
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Affiliation(s)
- Hyun-Soo Park
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
| | - Kwang-sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Korea;
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
- Correspondence:
| | - Eun-Sil Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea; (K.-R.C.); (S.-E.S.)
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea;
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea; (K.-R.C.); (S.-E.S.)
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Korea;
| | - Jaehyung Cha
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si 15355, Korea; (H.-S.P.); (E.-S.K.); (J.C.)
- Cheng Hyang NF Co., Ltd., 44-5 Daehak-ro, Jongno-gu, Seoul 03122, Korea
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Abstract
Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.
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Affiliation(s)
- Anabel M Scaranelo
- Medical Imaging Department, 12366University of Toronto, Ontario, Canada.,Breast Imaging Division, Joint Department of Medical Imaging, University of Health Network, Sinai Health and Women's College Hospital, Toronto, Ontario, Canada
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Samreen N, Mercado C, Heacock L, Chacko C, Partridge SC, Chhor C. Screening Breast MRI Primer: Indications, Current Protocols, and Emerging Techniques. JOURNAL OF BREAST IMAGING 2021; 3:387-398. [PMID: 38424773 DOI: 10.1093/jbi/wbaa116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Indexed: 03/02/2024]
Abstract
Breast dynamic contrast-enhanced MRI (DCE-MRI) is the most sensitive imaging modality for the detection of breast cancer. Screening MRI is currently performed predominantly in patients at high risk for breast cancer, but it could be of benefit in patients at intermediate risk for breast cancer and patients with dense breasts. Decreasing scan time and image interpretation time could increase cost-effectiveness, making screening MRI accessible to a larger group of patients. Abbreviated breast MRI (Ab-MRI) reduces scan time by decreasing the number of sequences obtained, but as multiple delayed contrast enhanced sequences are not obtained, no kinetic information is available. Ultrafast techniques rapidly acquire multiple sequences during the first minute of gadolinium contrast injection and provide information about both lesion morphology and vascular kinetics. Diffusion-weighted imaging is a noncontrast MRI technique with the potential to detect mammographically occult cancers. This review article aims to discuss the current indications of breast MRI as a screening tool, examine the standard breast DCE-MRI technique, and explore alternate screening MRI protocols, including Ab-MRI, ultrafast MRI, and noncontrast diffusion-weighted MRI, which can decrease scan time and interpretation time.
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Affiliation(s)
- Naziya Samreen
- New York University, Department of Radiology, Garden City, NY, USA
| | - Cecilia Mercado
- NYU School of Medicine, Department of Radiology, New York, NY, USA
| | - Laura Heacock
- NYU School of Medicine, Department of Radiology, New York, NY, USA
| | - Celin Chacko
- New York University, Department of Radiology, Garden City, NY, USA
| | | | - Chloe Chhor
- NYU School of Medicine, Department of Radiology, New York, NY, USA
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Musall BC, Abdelhafez AH, Adrada BE, Candelaria RP, Mohamed RMM, Boge M, Le-Petross H, Arribas E, Lane DL, Spak DA, Leung JWT, Hwang KP, Son JB, Elshafeey NA, Mahmoud HS, Wei P, Sun J, Zhang S, White JB, Ravenberg EE, Litton JK, Damodaran S, Thompson AM, Moulder SL, Yang WT, Pagel MD, Rauch GM, Ma J. Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer. J Magn Reson Imaging 2021; 54:251-260. [PMID: 33586845 DOI: 10.1002/jmri.27557] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. PURPOSE To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). STUDY TYPE Prospective. POPULATION/SUBJECTS Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. FIELD STRENGTH/SEQUENCE A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. STATISTICAL TESTS Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. RESULTS About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05). DATA CONCLUSION FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: 4.
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Affiliation(s)
- Benjamin C Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abeer H Abdelhafez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rania M M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Huong Le-Petross
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Elsa Arribas
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Deanna L Lane
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David A Spak
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jessica W T Leung
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nabil A Elshafeey
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hagar S Mahmoud
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shu Zhang
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Senthil Damodaran
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Stacy L Moulder
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark D Pagel
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Samreen N, Madsen LB, Chacko C, Heller SL. Magnetic resonance imaging in the evaluation of pathologic nipple discharge: indications and imaging findings. Br J Radiol 2021; 94:20201013. [PMID: 33544650 DOI: 10.1259/bjr.20201013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Pathologic nipple discharge (PND) is typically unilateral, spontaneous, involves a single duct, and is serous or bloody in appearance. In patients with PND, breast MRI can be helpful as an additional diagnostic tool when conventional imaging with mammogram and ultrasound are negative. MRI is able to detect the etiology of nipple discharge in 56-61% of cases when initial imaging with mammogram and ultrasound are negative. Advantages to using MRI in evaluation of PND include good visualization of the retroareolar breast and better evaluation of posterior lesions which may not be well evaluated on mammograms and galactograms. It is also less invasive compared to central duct excision. Papillomas and nipple adenomas are benign breast masses that can cause PND and are well visualized on MRI. Ductal ectasia, and infectious etiologies such as mastitis, abscess, and fistulas are additional benign causes of PND that are well evaluated with MRI. MRI is also excellent for evaluation of malignant causes of PND including Paget's disease, ductal carcinoma in-situ and invasive carcinoma. MRI's high negative predictive value of 87-98.2% is helpful in excluding malignant etiologies of PND.
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Affiliation(s)
- Naziya Samreen
- New York University Long Island Division, Long Island, NY, USA
| | | | - Celin Chacko
- New York University Long Island Division, Long Island, NY, USA
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Granata V, Fusco R, Avallone A, Cassata A, Palaia R, Delrio P, Grassi R, Tatangelo F, Grazzini G, Izzo F, Petrillo A. Abbreviated MRI protocol for colorectal liver metastases: How the radiologist could work in pre surgical setting. PLoS One 2020; 15:e0241431. [PMID: 33211702 PMCID: PMC7676687 DOI: 10.1371/journal.pone.0241431] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023] Open
Abstract
Background MRI is the most reliable imaging modality that allows to assess liver metastases. Our purpose is to compare the per-lesion and per-patient detection rate of gadoxetic acid-(Gd-EOB) enhanced liver MRI and fast MR protocol including Diffusion Weighted Imaging (DWI) and T2-W Fat Suppression sequence in the detection of liver metastasis in pre surgical setting. Methods One hundred and eight patients with pathologically proven liver metastases (756 liver metastases) underwent Gd-EOBMRI were enrolled in this study. Three radiologist independently graded the presence of liver lesions on a five-point confidence scale assessed only abbreviated protocol (DWI and sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) fat suppressed sequence) and after an interval of more than 2 weeks the conventional study (all acquired sequences). Per-lesion and per-patient detection rate of metastases were calculated. Weighted к values were used to evaluate inter-reader agreement of the confidence scale regarding the presence of the lesion. Results MRI detected 732 liver metastases. All lesions were identified both by conventional study as by abbreviated protocol. In terms of per-lesion detection rate of liver metastasis, all three readers had higher detection rate both with abbreviated protocol and with standard protocol with Gd-EOB (96.8% [732 of 756] vs. 96.5% [730 of 756] for reader 1; 95.8% [725 of 756] vs. 95.2% [720 of 756] for reader 2; 96.5% [730 of 756] vs. 96.5% [730 of 756] for reader 3). Inter-reader agreement of lesions detection rate between the three radiologists was excellent (k range, 0.86–0.98) both for Gd-EOB MRI and for Fast protocol (k range, 0.89–0.99). Conclusion Abbreviated protocol showed the same detection rate than conventional study in detection of liver metastases.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Roberta Fusco
- Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
- * E-mail:
| | - Antonio Avallone
- Gastrointestinal Oncology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Antonino Cassata
- Gastrointestinal Oncology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Raffaele Palaia
- Hepatobiliary Surgical Oncology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Paolo Delrio
- Division of Gastrointestinal Surgical Oncology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Fabiana Tatangelo
- Division of Pathology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Giulia Grazzini
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
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