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Kayadibi Y, Saracoglu MS, Kurt SA, Deger E, Boy FNS, Ucar N, Icten GE. Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models. Acad Radiol 2024:S1076-6332(24)00189-2. [PMID: 38641449 DOI: 10.1016/j.acra.2024.03.025] [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: 02/16/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/21/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate the effectiveness of machine learning-based clinical, radiomics, and combined models in differentiating idiopathic granulomatous mastitis (IGM) from malignancy, both presenting as non-mass enhancement (NME) lesions on magnetic resonance imaging (MRI), and to compare these models with radiological evaluation. MATERIAL AND METHODS A total of 178 patients (69 IGM and 109 breast cancer patients) with NME on breast MRI evaluated between March 2018 and April 2022, were included in this two-center study. Age, skin changes, presence of fistula, and abscess were recorded from hospital records. Two experienced radiologists evaluated MRI images according to the breast imaging reporting and data system 2013 lexicon. Lesions were segmented independently on T2-weighted, apparent diffusion coefficient, and post-contrast-T1-weighted sequences. Data were split into training and external testing sets. Machine learning models were built using Light GBM (light gradient-boosting machine). Radiological, clinical, radiomics, and clinical-radiomics models were created and compared. Decision curve analysis was performed. Quality of reporting and that of methodology were evaluated using CLEAR and METRICS tools. RESULTS IGM group was younger (p = 0.014). Abscesses (p < 0.001), fistulas (p < 0.001), and skin changes (p < 0.001) were significantly more common in the IGM group. No significant difference was detected in terms of lesion size (p = 0.213). In the evaluation of NME, the lowest performance belonged to the radiologists' evaluation (AUC for training, 0.740; for testing, 0.737), while the highest AUC was achieved by the model developed by combined clinical and radiomics features (AUC for training, 0.979; for testing, 0.942). CONCLUSION Our study has shown that the machine learning-based clinical-radiomics model might have the potential to accurately discriminate IGM and malignant lesions in evaluating NME areas.
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Affiliation(s)
- Yasemin Kayadibi
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye.
| | - Mehmet Sakıpcan Saracoglu
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye
| | - Seda Aladag Kurt
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye
| | - Enes Deger
- Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye
| | - Fatma Nur Soylu Boy
- Fatih Sultan Mehmet Education and Research Hospital, Department of Radiology, Atasehir, Istanbul, Türkiye
| | - Nese Ucar
- Gaziosmanspasa Education and Research Hospital, Department of Radiology, Gaziosmanpasa, Istanbul, Türkiye
| | - Gul Esen Icten
- Senology Research Institute, Acibadem Mehmet Ali Aydinlar University, Maslak, Istanbul, Türkiye
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2
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Zhu J, Miao X, Li X, Zhang Y, Lou Y, Chen H, Liu X. Granulomatous lobular mastitis co-existing with ductal carcinoma in situ: Report of three cases and review of the literature. Ann Diagn Pathol 2024; 68:152241. [PMID: 38008016 DOI: 10.1016/j.anndiagpath.2023.152241] [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: 10/22/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 11/28/2023]
Abstract
Granulomatous lobular mastitis (GLM) is a benign and infrequent chronic breast ailment. Although this lesion can be clinically and radiographically mistaken for early-onset breast cancer, it is a rare occurrence for the two to coexist. This report describes three such cases. In all three patients, the primary signs and symptoms were related to the formation of diffuse breast masses or abscesses. Breast ultrasound and MRI revealed glandular edema and dilated breast ducts. The biopsies of all lesions exhibited both granulomatous inflammation confined to the lobules of the breast, abundant interstitial inflammatory cell infiltrates, and apparently cancerous cells located in dilated ducts with intact basement membranes. The surgically excised specimens confirmed the diagnosis of GLM and ductal carcinoma in situ (DCIS) in all three patients who underwent breast mass resection. By clinical imaging and clinical manifestations, GLM may obscure a concurrent DCIS, as highlighted by the cases reported herein.
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MESH Headings
- Female
- Humans
- Carcinoma, Intraductal, Noninfiltrating/complications
- Carcinoma, Intraductal, Noninfiltrating/diagnosis
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Breast/pathology
- Granulomatous Mastitis/complications
- Granulomatous Mastitis/diagnosis
- Granulomatous Mastitis/pathology
- Carcinoma, Ductal, Breast/complications
- Carcinoma, Ductal, Breast/diagnosis
- Carcinoma, Ductal, Breast/pathology
- Breast Neoplasms/complications
- Breast Neoplasms/pathology
- Carcinoma, Lobular/pathology
- Carcinoma in Situ/pathology
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Affiliation(s)
- Jianmin Zhu
- Breast and Thyroid Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China
| | - Xiuming Miao
- Department of Pathology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China
| | - Xin Li
- The First Clinical School of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China
| | - Yang Zhang
- Breast and Thyroid Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China
| | - Yuan Lou
- The First Clinical School of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China
| | - Hanhan Chen
- Breast and Thyroid Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China
| | - Xiaofei Liu
- Breast and Thyroid Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong Province, China.
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3
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Ma Q, Lu X, Qin X, Xu X, Fan M, Duan Y, Tu Z, Zhu J, Wang J, Zhang C. A sonogram radiomics model for differentiating granulomatous lobular mastitis from invasive breast cancer: a multicenter study. LA RADIOLOGIA MEDICA 2023; 128:1206-1216. [PMID: 37597127 DOI: 10.1007/s11547-023-01694-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/28/2023] [Indexed: 08/21/2023]
Abstract
PURPOSE To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC). MATERIALS AND METHODS A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set. RESULTS The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05). CONCLUSION Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.
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Affiliation(s)
- Qianqing Ma
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Xiaofeng Lu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Xiachuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University Nan Chong), Sichuan, China
| | - Xiangyi Xu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Min Fan
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yayang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China
| | - Zhengzheng Tu
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Jianhui Zhu
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Junli Wang
- Department of Ultrasound, The Second People's Hospital of Wuhu, No.259 Jiuhuashan Road, Jinghu District, Wuhu, 241001, Anhui, China.
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China.
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4
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Soylu Boy FN, Esen Icten G, Kayadibi Y, Tasdelen I, Alver D. Idiopathic Granulomatous Mastitis or Breast Cancer? A Comparative MRI Study in Patients Presenting with Non-Mass Enhancement. Diagnostics (Basel) 2023; 13:diagnostics13081475. [PMID: 37189576 DOI: 10.3390/diagnostics13081475] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/20/2023] [Accepted: 03/16/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVE To compare and determine discriminative magnetic resonance imaging (MRI) findings of idiopathic granulomatous mastitis (IGM) and breast cancer (BC) that present as non-mass enhancement. MATERIALS AND METHODS This retrospective study includes 68 IGM and 75 BC cases that presented with non-mass enhancement on breast MRI. All patients with a previous history of breast surgery, radiotherapy, or chemotherapy due to BC or a previous history of mastitis were excluded. On MRI images, presence of architectural distortion skin thickening, edema, hyperintense ducts containing protein, dilated fat-containing ducts and axillary adenopathies were noted. Cysts with enhancing walls, lesion size, lesion location, fistulas, distribution, internal enhancement pattern and kinetic features of non-mass enhancement were recorded. Apparent diffusion coefficient (ADC) values were calculated. Pearson chi-square test, Fisher's exact test, independent t test and Mann-Whitney U test were used as needed for statistical analysis and comparison. Multivariate logistic regression model was used to determine the independent predictors. RESULTS IGM patients were significantly younger than BC patients (p < 0.001). Cysts with thin (p < 0.05) or thick walls (p = 0.001), multiple cystic lesions, (p < 0.001), cystic lesions draining to the skin (p < 0.001), and skin fistulas (p < 0.05) were detected more often in IGM. Central (p < 0.05) and periareolar (p < 0.001) location and focal skin thickening (p < 0.05) were significantly more common in IGM. Architectural distortion (p = 0.001) and diffuse skin thickening (p < 0.05) were associated with BC. Multiple regional distribution was more common in IGM, whereas diffuse distribution and clumped enhancement were more common in BC (p < 0.05). In kinetic analysis, persistent enhancement was more common in IGM, whereas plateau and wash-out types were more common in BC (p < 0.001). Independent predictors for BC were age, diffuse skin thickening and kinetic curve types. There was no significant difference in the diffusion characteristics. Based on these findings, MRI had a sensitivity, specificity and accuracy of 88%, 67.65%, and 78.32%, respectively, in differentiating IGM from BC. CONCLUSIONS In conclusion, for non-mass enhancement, MRI can rule out malignancy with a considerably high sensitivity; however, specificity is still low, as many IGM patients have overlapping findings. Final diagnosis should be complemented with histopathology whenever necessary.
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Affiliation(s)
- Fatma Nur Soylu Boy
- Department of Radiology, Fatih Sultan Mehmet Training and Research Hospital, 34758 Istanbul, Turkey
| | - Gul Esen Icten
- Senology Research Institute, Acibadem Mehmet Ali Aydınlar University, 34457 Istanbul, Turkey
- Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydınlar University, 34457 Istanbul, Turkey
| | - Yasemin Kayadibi
- Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, 34320 Istanbul, Turkey
| | - Iksan Tasdelen
- Department of General Surgery, Fatih Sultan Mehmet Training and Research Hospital, 34758 Istanbul, Turkey
| | - Dolunay Alver
- Department of Radiology, Fatih Sultan Mehmet Training and Research Hospital, 34758 Istanbul, Turkey
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5
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Yin L, Agyekum EA, Zhang Q, Pan L, Wu T, Xiao X, Qian XQ. Differentiation Between Granulomatous Lobular Mastitis and Breast Cancer Using Quantitative Parameters on Contrast-Enhanced Ultrasound. Front Oncol 2022; 12:876487. [PMID: 35912226 PMCID: PMC9335943 DOI: 10.3389/fonc.2022.876487] [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: 02/15/2022] [Accepted: 06/20/2022] [Indexed: 12/03/2022] Open
Abstract
Objective To investigate the Contrast-enhanced ultrasound (CEUS) imaging characteristics of granulomatous lobular mastitis (GLM) and the value of differentiating GLM from breast cancer. Materials and methods The study included 30 women with GLM (mean age 36.7 ± 5 years [SD]) and 58 women with breast cancer (mean age 48. ± 8 years [SD]) who were scheduled for ultrasound-guided tissue biopsy. All patients were evaluated with conventional US and CEUS prior to the biopsy. In both groups, the parameters of the quantitative and qualitative analysis of the CEUS were recorded and compared. The receiver-operating-characteristics curves (ROC) were created. Sensitivity, specificity, cut-off, and area under the curve (AUC) values were calculated. Results TTP values in GLM were statistically higher than in breast cancer (mean, 27.63 ± 7.29 vs. 20.10 ± 6.11), but WIS values were lower (mean, 0.16 ± 0.05 vs. 0.28 ± 0.17). Rich vascularity was discovered in 54.45% of breast cancer patients, but only 30.00% of GLM patients had rich vascularity. The AUC for the ROC test was 0.791 and 0.807, respectively. The optimal cut-off value for TTP was 24.5s, and the WIS cut-off value was 0.185dB/s, yielding 73.33% sensitivity, 84.48% specificity, and 86.21% sensitivity, 70% specificity respectively in the diagnosis of GLM. The lesion scores reduced from 4 to 3 with the addition of CEUS for the patients with GLM. However, the scores did not change for the patients with breast cancer. Conclusion CEUS could help distinguish GLM from breast cancer by detecting higher TTP and WIS values, potentially influencing clinical decision-making for additional biopsies.
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Affiliation(s)
- Liang Yin
- Department of Breast Surgery, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
- *Correspondence: Liang Yin,
| | - Enock Adjei Agyekum
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Qing Zhang
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Lei Pan
- Department of Breast Surgery, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Ting Wu
- Department of Pathology, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Xiudi Xiao
- Department of Breast Surgery, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Xiao-qin Qian
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
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6
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Lin Q, Fei C, Wu X, Wu Q, Chen Q, Yan Y. Imaging manifestations of idiopathic granulomatous lobular mastitis on cone-beam breast computed tomography. Eur J Radiol 2022; 154:110389. [DOI: 10.1016/j.ejrad.2022.110389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
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7
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Yuan QQ, Xiao SX, Farouk O, Du YT, Sheybani F, Tan QT, Akbulut S, Cetin K, Alikhassi A, Yaghan RJ, Durur-Subasi I, Altintoprak F, Eom TI, Alper F, Hasbahceci M, Martínez-Ramos D, Oztekin PS, Kwong A, Pluguez-Turull CW, Brownson KE, Chandanwale S, Habibi M, Lan LY, Zhou R, Zeng XT, Bai J, Bai JW, Chen QR, Chen X, Zha XM, Dai WJ, Dai ZJ, Feng QY, Gao QJ, Gao RF, Han BS, Hou JX, Hou W, Liao HY, Luo H, Liu ZR, Lu JH, Luo B, Ma XP, Qian J, Qin JY, Wei W, Wei G, Xu LY, Xue HC, Yang HW, Yang WG, Zhang CJ, Zhang F, Zhang GX, Zhang SK, Zhang SQ, Zhang YQ, Zhang YP, Zhang SC, Zhao DW, Zheng XM, Zheng LW, Xu GR, Zhou WB, Wu GS. Management of granulomatous lobular mastitis: an international multidisciplinary consensus (2021 edition). Mil Med Res 2022; 9:20. [PMID: 35473758 PMCID: PMC9040252 DOI: 10.1186/s40779-022-00380-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/07/2022] [Indexed: 02/07/2023] Open
Abstract
Granulomatous lobular mastitis (GLM) is a rare and chronic benign inflammatory disease of the breast. Difficulties exist in the management of GLM for many front-line surgeons and medical specialists who care for patients with inflammatory disorders of the breast. This consensus is summarized to establish evidence-based recommendations for the management of GLM. Literature was reviewed using PubMed from January 1, 1971 to July 31, 2020. Sixty-six international experienced multidisciplinary experts from 11 countries or regions were invited to review the evidence. Levels of evidence were determined using the American College of Physicians grading system, and recommendations were discussed until consensus. Experts discussed and concluded 30 recommendations on historical definitions, etiology and predisposing factors, diagnosis criteria, treatment, clinical stages, relapse and recurrence of GLM. GLM was recommended as a widely accepted definition. In addition, this consensus introduced a new clinical stages and management algorithm for GLM to provide individual treatment strategies. In conclusion, diagnosis of GLM depends on a combination of history, clinical manifestations, imaging examinations, laboratory examinations and pathology. The approach to treatment of GLM should be applied according to the different clinical stage of GLM. This evidence-based consensus would be valuable to assist front-line surgeons and medical specialists in the optimal management of GLM.
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Affiliation(s)
- Qian-Qian Yuan
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Shu-Xuan Xiao
- grid.170205.10000 0004 1936 7822Department of Pathology, University of Chicago Pritzker School of Medicine, Chicago, IL 60637 USA
| | - Omar Farouk
- grid.10251.370000000103426662Department of Surgical Oncology and Breast Surgery, Oncology Center, Faculty of Medicine, Mansoura University, Mansoura, 35516 Egypt
| | - Yu-Tang Du
- grid.24695.3c0000 0001 1431 9176Department of Breast Surgery, Beijing University of Chinese Medicine, Beijing, 100700 China
| | - Fereshte Sheybani
- grid.411583.a0000 0001 2198 6209Department of Infectious Diseases and Tropical Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, 9177899191 Iran
| | - Qing Ting Tan
- grid.414963.d0000 0000 8958 3388Breast Department, KK Women’s and Children’s Hospital, 100 Bukit Timah Road, Singapore, 229899 Singapore
| | - Sami Akbulut
- grid.411650.70000 0001 0024 1937Department of Surgery, Department of Public Health, Department of Biostatistics, Bioinformatics and Medical Informatics, Inonu University Faculty of Medicine, 44280 Malatya, Turkey
| | - Kenan Cetin
- grid.412364.60000 0001 0680 7807Department of General Surgery, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Afsaneh Alikhassi
- grid.411705.60000 0001 0166 0922Department of Radiology, Cancer Institute, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, 1419733141 Iran
| | - Rami Jalal Yaghan
- grid.411424.60000 0001 0440 9653Department of Surgery, College of Medicine and Medical Sciences, Arabian Gulf University-Bahrain, Manama, 26671 Bahrain
| | - Irmak Durur-Subasi
- grid.411781.a0000 0004 0471 9346Department of Radiology, International Faculty of Medicine, Istanbul Medipol University, 34810 Istanbul, Turkey
| | - Fatih Altintoprak
- grid.49746.380000 0001 0682 3030Department of General Surgery, Faculty of Medicine, Sakarya University, 54050 Sakarya, Turkey
| | - Tae Ik Eom
- Department of Surgery, HiU Clinic, 170, Gwongwang-ro, Paldal-gu, Suwon, 16488 Korea
| | - Fatih Alper
- grid.411445.10000 0001 0775 759XDepartment of Radiology, Faculty of Medicine, Ataturk University, 25240 Erzurum, Turkey
| | - Mustafa Hasbahceci
- Academic Support and Education Center, Hırkai Serif District, Kececi Cesmesi Str, Doktorlar Building, B/7, 34091 Istanbul, Turkey
| | - David Martínez-Ramos
- grid.470634.2Department of General and Digestive Surgery, Hospital General Castellon, Avda Benicassim S/N, 12812004 Castellón, Spain
| | - Pelin Seher Oztekin
- grid.413783.a0000 0004 0642 6432Radiology Department, Ankara Training and Research Hospital, 305018 Ankara, Turkey
| | - Ava Kwong
- grid.440671.00000 0004 5373 5131Department of Surgery, The University of Hong Kong, China; The University of Hong Kong-Shenzhen Hospital, Shenzhen, 518053 China
| | - Cedric W. Pluguez-Turull
- grid.418456.a0000 0004 0414 313XUniversity of Miami Health System and Miller School of Medicine, 1475 NW 12th Avenue, Miami, FL 33136 USA
| | - Kirstyn E. Brownson
- grid.223827.e0000 0001 2193 0096Department of Surgery, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT 84112 USA
| | - Shirish Chandanwale
- grid.464654.10000 0004 1764 8110Department of Pathology, Dr D Y Patil Medical College Hospital and Research Centre, Pimpri, Pune, 603203 India
| | - Mehran Habibi
- Department of Surgery, Johns Hopkins Breast Center at Bayview Campus, 4940 Eastern Avenue, Rm. A-562, Baltimore, MD 21224 USA
| | - Liu-Yi Lan
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Rui Zhou
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Xian-Tao Zeng
- grid.413247.70000 0004 1808 0969Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Jiao Bai
- grid.413247.70000 0004 1808 0969Department of Diagnostic Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Jun-Wen Bai
- grid.413375.70000 0004 1757 7666Department of Surgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010110 China
| | - Qiong-Rong Chen
- grid.49470.3e0000 0001 2331 6153Center for Pathology and Molecular Diagnostics, Wuhan University, Wuhan, 430071 China
| | - Xing Chen
- grid.415108.90000 0004 1757 9178Department of General Surgery, Fujian Provincial Hospital, Fuzhou, 350001 China
| | - Xiao-Ming Zha
- grid.412676.00000 0004 1799 0784The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210029 China
| | - Wen-Jie Dai
- grid.412596.d0000 0004 1797 9737Key Laboratory of Hepatosplenic Surgery and the First Department of General Surgery, First Affiliated Hospital of Harbin Medical University, Harbin, 150007 China
| | - Zhi-Jun Dai
- grid.13402.340000 0004 1759 700XDepartment of Breast Surgery, Zhejiang University School of Medicine First Affiliated Hospital, Hangzhou, 310003 China
| | - Qin-Yu Feng
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Qing-Jun Gao
- grid.452244.1Department of General Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004 China
| | - Run-Fang Gao
- grid.464423.3Department of General Surgery, Shanxi Provincial People’s Hospital, Taiyuan, 030012 China
| | - Bao-San Han
- grid.412987.10000 0004 0630 1330Department of Breast Surgery, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200092 China
| | - Jin-Xuan Hou
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Wei Hou
- Department of Cardiothoracic Surgery, Zaoyang People’s Hospital, Zaoyang, 441299 Hubei China
| | - Hai-Ying Liao
- grid.452702.60000 0004 1804 3009Department of Thyroid and Breast Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, 050004 China
| | - Hong Luo
- grid.411634.50000 0004 0632 4559Department of General Surgery, Guangshan County People’s Hospital, Guangshan County, Xinxiang, 465499 Henan China
| | - Zheng-Ren Liu
- grid.412604.50000 0004 1758 4073Department of Breast Surgery, First Affiliated Hospital of Nanchang University, Nanchang, 330006 China
| | - Jing-Hua Lu
- grid.9227.e0000000119573309Chinese Academy of Sciences, Beijing, 100045 China
| | - Bin Luo
- grid.12527.330000 0001 0662 3178Department of General Surgery, School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing, 102218 China
| | - Xiao-Peng Ma
- grid.411395.b0000 0004 1757 0085Department of Breast and Thyroid Surgery, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Hospital, Hefei, 230001 China
| | - Jun Qian
- grid.414902.a0000 0004 1771 3912Department of Thyroid Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, 650032 China
| | - Jian-Yong Qin
- Department of Oncology, Liwan Central Hospital of Guangzhou, Guangzhou, 510150 China
| | - Wei Wei
- grid.440601.70000 0004 1798 0578Department of Breast Surgery, Peking University Shenzhen Hospital, Shenzhen, 518036 Guangdong China
| | - Gang Wei
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Li-Ying Xu
- grid.413247.70000 0004 1808 0969Department of Computed Tomography, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Hui-Chao Xue
- grid.412990.70000 0004 1808 322XDepartment of General Surgery, Xinxiang Medical University First Affiliated Hospital, Xinxiang, 453100 Henan China
| | - Hua-Wei Yang
- grid.256607.00000 0004 1798 2653Department of Breast Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021 China
| | - Wei-Ge Yang
- grid.413087.90000 0004 1755 3939Department of General Surgery, Zhongshan Hospital Fudan University, Shanghai, 200032 China
| | - Chao-Jie Zhang
- grid.477407.70000 0004 1806 9292Department of Breast and Thyroid Surgery, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Fan Zhang
- grid.410726.60000 0004 1797 8419Department of Breast and Thyroid Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing, 400013 China
| | - Guan-Xin Zhang
- Department of General Surgery, Qinghai Province People’s Hospital, Xining, 810007 China
| | - Shao-Kun Zhang
- grid.508137.80000 0004 4914 6107Department of Thyroid and Breast Surgery, Qingdao Women and Children’s Hospital, Qingdao, 266000 Shandong China
| | - Shu-Qun Zhang
- grid.43169.390000 0001 0599 1243Department of Oncology, Xi’an Jiaotong University Second Affiliated Hospital, Xi’an, 710004 China
| | - Ye-Qiang Zhang
- Department of Cardiothoracic Surgery, Zaoyang First People’s Hospital, Zaoyang, 441299 Hubei China
| | - Yue-Peng Zhang
- grid.413247.70000 0004 1808 0969Department of Diagnostic Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Sheng-Chu Zhang
- grid.508285.20000 0004 1757 7463Department of Thyroid and Breast Surgery, Yichang Central People’s Hospital, Yichang, 443003 Hubei China
| | - Dai-Wei Zhao
- grid.413458.f0000 0000 9330 9891Department of Thyroid Surgery, The Second Affiliated Hospital, Guizhou Medical University, Kaili, 556000 Guizhou China
| | - Xiang-Min Zheng
- grid.413810.fDepartment of General Surgery, Shanghai Changzheng Hospital, Shanghai, 200003 China
| | - Le-Wei Zheng
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Gao-Ran Xu
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
| | - Wen-Bo Zhou
- grid.452381.90000 0004 1779 2614Department of Surgery, Dongfeng General Hospital Affiliated with Hubei Medical College, Shiyan, 442001 Hubei China
| | - Gao-Song Wu
- grid.413247.70000 0004 1808 0969Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071 China
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Li HH, Sun B, Tan C, Li R, Fu CX, Grimm R, Zhu H, Peng WJ. The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer. Front Oncol 2022; 12:821586. [PMID: 35223503 PMCID: PMC8864172 DOI: 10.3389/fonc.2022.821586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 01/20/2022] [Indexed: 01/02/2023] Open
Abstract
Purpose To determine if whole-tumor histogram and texture analyses using intravoxel incoherent motion (IVIM) parameters values could differentiate the pathologic characteristics of locally advanced gastric cancer. Methods Eighty patients with histologically confirmed locally advanced gastric cancer who received surgery in our institution were retrospectively enrolled into our study between April 2017 and December 2018. Patients were excluded if they had lesions with the smallest diameter < 5 mm and severe image artifacts. MR scanning included IVIM sequences (9 b values, 0, 20, 40, 60, 100, 150,200, 500, and 800 s/mm2) used in all patients before treatment. Whole tumors were segmented by manually drawing the lesion contours on each slice of the diffusion-weighted imaging (DWI) images (with b=800). Histogram and texture metrics for IVIM parameters values and apparent diffusion coefficient (ADC) values were measured based on whole-tumor volume analyses. Then, all 24 extracted metrics were compared between well, moderately, and poorly differentiated tumors, and between different Lauren classifications, signet-ring cell carcinomas, and other poorly cohesive carcinomas using univariate analyses. Multivariate logistic analyses and multicollinear tests were used to identify independent influencing factors from the significant variables of the univariate analyses to distinguish tumor differentiation and Lauren classifications. ROC curve analyses were performed to evaluate the diagnostic performance of these independent influencing factors for determining tumor differentiation and Lauren classifications and identifying signet-ring cell carcinomas. The interobserver agreement was also conducted between the two observers for image quality evaluations and parameter metric measurements. Results For diagnosing tumor differentiation, the ADCmedian, pure diffusion coefficient median (Dslowmedian), and pure diffusion coefficient entropy (Dslowentropy) showed the greatest AUCs: 0.937, 0.948, and 0.850, respectively, and no differences were found between the three metrics, P>0.05). The 95th percentile perfusion factor (FP P95th) was the best metric to distinguish diffuse-type GCs vs. intestinal/mixed (AUC=0.896). The ROC curve to distinguish signet-ring cell carcinomas from other poorly cohesive carcinomas showed that the Dslowmedian had AUC of 0.738. For interobserver reliability, image quality evaluations showed excellent agreement (interclass correlation coefficient [ICC]=0.85); metrics measurements of all parameters indicated good to excellent agreement (ICC=0.65-0.89), except for the Dfast metric, which showed moderate agreement (ICC=0.41-0.60). Conclusions The whole-tumor histogram and texture analyses of the IVIM parameters based on the biexponential model provided a non-invasive method to discriminate pathologic tumor subtypes preoperatively in patients with locally advanced gastric cancer. The metric FP P95th derived from IVIM performed better in determining Lauren classifications than the mono-exponential model.
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Affiliation(s)
- Huan-Huan Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bo Sun
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cong Tan
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cai-Xia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Robert Grimm
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
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9
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Rodríguez Pérez A, Rojo Novo S, Gutiérrez Domingo Á, Novo Cabrera J. Mastitis granulomatosa: desafío diagnóstico y terapéutico en paciente joven. CLINICA E INVESTIGACION EN GINECOLOGIA Y OBSTETRICIA 2022. [DOI: 10.1016/j.gine.2021.100714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Li Y, Yang ZL, Lv WZ, Qin YJ, Tang CL, Yan X, Guo YH, Xia LM, Ai T. Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses. Front Oncol 2021; 11:738330. [PMID: 34631572 PMCID: PMC8493069 DOI: 10.3389/fonc.2021.738330] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/07/2021] [Indexed: 12/30/2022] Open
Abstract
Purpose We aimed to assess the additional value of a radiomics-based signature for distinguishing between benign and malignant non-mass enhancement lesions (NMEs) on dynamic contrast-enhanced breast magnetic resonance imaging (breast DCE-MRI). Methods In this retrospective study, 232 patients with 247 histopathologically confirmed NMEs (malignant: 191; benign: 56) were enrolled from December 2017 to October 2020 as a primary cohort to develop the discriminative models. Radiomic features were extracted from one post-contrast phase (around 90s after contrast injection) of breast DCE-MRI images. The least absolute shrinkage and selection operator (LASSO) regression model was adapted to select features and construct the radiomics-based signature. Based on clinical and routine MR features, radiomics features, and combined information, three discriminative models were built using multivariable logistic regression analyses. In addition, an independent cohort of 72 patients with 72 NMEs (malignant: 50; benign: 22) was collected from November 2020 to April 2021 for the validation of the three discriminative models. Finally, the combined model was assessed using nomogram and decision curve analyses. Results The routine MR model with two selected features of the time-intensity curve (TIC) type and MR-reported axillary lymph node (ALN) status showed a high sensitivity of 0.942 (95%CI, 0.906 - 0.974) and low specificity of 0.589 (95%CI, 0.464 - 0.714). The radiomics model with six selected features was significantly correlated with malignancy (P<0.001 for both primary and validation cohorts). Finally, the individual combined model, which contained factors including TIC types and radiomics signatures, showed good discrimination, with an acceptable sensitivity of 0.869 (95%CI, 0.816 to 0.916), improved specificity of 0.839 (95%CI, 0.750 to 0.929). The nomogram was applied to the validation cohort, reaching good discrimination, with a sensitivity of 0.820 (95%CI, 0.700 to 0.920), specificity of 0.864 (95%CI,0.682 to 1.000). The combined model was clinically helpful, as demonstrated by decision curve analysis. Conclusions Our study added radiomics signatures into a conventional clinical model and developed a radiomics nomogram including radiomics signatures and TIC types. This radiomics model could be used to differentiate benign from malignant NMEs in patients with suspicious lesions on breast MRI.
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Affiliation(s)
- Yan Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenlu L Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhi Z Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, China
| | - Yanjin J Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Caili L Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yan
- Scientific Marketing, Siemens Healthcare Ltd., Shanghai, China
| | - Yihao H Guo
- Magnetic Resonance (MR) Collaboration, Siemens Healthcare, Guangzhou, China
| | - Liming M Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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11
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Li S, Liang P, Wang Y, Feng C, Shen Y, Hu X, Hu D, Meng X, Li Z. Combining volumetric apparent diffusion coefficient histogram analysis with vesical imaging reporting and data system to predict the muscle invasion of bladder cancer. Abdom Radiol (NY) 2021; 46:4301-4310. [PMID: 33909091 DOI: 10.1007/s00261-021-03091-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/06/2021] [Accepted: 04/10/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The objective of this study was to explore whether volumetric apparent diffusion coefficient (ADC) histogram analysis can provide additional value to Vesical Imaging Reporting and Data System (VI-RADS) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND METHODS 80 patients were retrospectively reviewed with pathologically proven NMIBC (n = 53) or MIBC (n = 27). All patients underwent MRI including diffusion-weighted imaging (DWI) (b = 0, 800 s/mm2), and the VI-RADS score was evaluated based on DWI. Volumetric ADC histogram parameters were calculated from the volumetric of interest (VOI) on DWI, including the min ADC, mean ADC, median ADC, max ADC, 10th, 25th, 75th, 90th percentiles ADC, skewness, kurtosis, and entropy. The Mann-Whitney U-test was used to compare histogram parameters between NMIBC and MIBC. Receiver operating characteristic analysis was used to evaluate the diagnostic value of each significant parameter. RESULTS Among all parameters, the VI-RADS yield the highest Area Under the Curve (AUC, 0.88; sensitivity, 88.89%; specificity, 83.61%). MIBC had significantly lower min ADC, mean ADC, median ADC, 10th, 25th, 75th, and 90th percentiles ADC than NMIBC (p = 0.002, p < 0.001, p < 0.001, p = 0.003, p = 0.004, p < 0.001, p < 0.001). Skewness and kurtosis of MIBC were significantly higher than those of NMIBC (p < 0.001, p < 0.001). The combination of VI-RADS and skewness showed significantly higher AUC (AUC 0.923; 95% CI 0.847-0.969) than only with VI-RADS (AUC 0.880; 95% CI 0.793-0.940). CONCLUSION Volumetric ADC histogram analysis and VI-RADS are both useful methods in differentiating MIBC from NMIBC, and the volumetric ADC histogram analysis can provide additional value to VI-RADS.
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Affiliation(s)
- Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave., Wuhan, 430030, Hubei, China
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12
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You J, Yin J. Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas. Front Oncol 2021; 11:678441. [PMID: 34414105 PMCID: PMC8369414 DOI: 10.3389/fonc.2021.678441] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/19/2021] [Indexed: 12/29/2022] Open
Abstract
Objective To determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1–2 versus T3–4) in rectal carcinomas. Materials and Methods One hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman’s correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves. Results The wavelet HHH NGTDM strength (RS = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3–4 tumors. The shape maximum 2D diameter column (RS = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (RS = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (RS = -0.229, P = 0.032) from ADC maps were independent predictors. In training cohorts, the classification models from HR-T2WI, ADC maps and the combination of two achieved the area under the ROC curves (AUCs) of 0.877, 0.902 and 0.941, with the accuracy of 79.78%, 89.86% and 89.89%, respectively. In validation cohorts, the three models achieved AUCs of 0.845, 0.881 and 0.910, with the accuracy of 78.46%, 83.08% and 87.69%, respectively. Conclusions Texture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.
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Affiliation(s)
- Jia You
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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13
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Lee EB, Kim SH, Park GE, Lee J, Kang BJ. Risk Stratification of Ductal Carcinoma In Situ and Texture Analysis of Contrast-Enhanced Breast Magnetic Resonance Imaging. J Comput Assist Tomogr 2021; 45:843-848. [PMID: 34347708 DOI: 10.1097/rct.0000000000001205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to investigate whether texture analysis of contrast-enhanced T1 weighted images could predict risk of ductal carcinoma in situ (DCIS). METHODS The study included 185 DCIS lesions that were classified as either low risk or non-low risk using surgical pathology records. All magnetic resonance imaging texture analyses were performed using postprocessing software, and texture-derived parameters were extracted. RESULTS The sphericity, compactness, and spherical disproportion were significantly different in the low-risk and non-low risk groups using the Van Nuys Prognostic Index (mean ± SD, 0.479 ± 0.189 vs 0.414 ± 0.176, 0.161 ± 0.159 vs 0.112 ± 0.134, and 2.569 ± 1.434 vs 2.934 ± 1.374, respectively; P < 0.05). In the univariate analyses, sphericity (odds ratio, 7.091; 95% confidence interval, 1.236-40.666; P = 0.028) and compactness (odds ratio, 9.267; 95% confidence interval, 1.125-76.360; P = 0.039) were significantly associated with a high probability of being low risk according to the Van Nuys Prognostic Index. CONCLUSIONS Whole-lesion texture analysis may be helpful in identifying patients classified as having low-risk DCIS before surgery.
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Affiliation(s)
- Eun Byul Lee
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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14
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Ichinose Y, Kosaka Y, Saeki T, Fujimoto A, Nukui A, Asano A, Shimada H, Matsuura K, Hasebe T, Osaki A. Granuloma after breast conserving surgery-a report of three cases. J Surg Case Rep 2021; 2021:rjab199. [PMID: 34104403 PMCID: PMC8177901 DOI: 10.1093/jscr/rjab199] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
Granulomatous mastitis is a rare breast disease that is categorized as a benign tumor with chronic inflammation. Since the cause of the chronic inflammation is usually unknown, it is sometimes called idiopathic granulomatous mastitis (IGM). Although imaging modalities, such as ultrasound, magnetic resonance imaging and mammography can detect tumors, they are sometimes unable to differentiate between benign and malignant tumors. In such cases, biopsy is needed to make a correct diagnosis. We experienced three cases of IGM after breast conserving surgery in breast cancer patients in whom we needed to rule out recurrence of breast cancer. In our cases, tumorectomy was performed in two cases for pathological diagnosis, since neither biopsy nor cytology was able to reveal a conclusive pathological diagnosis. Our management of these three cases might suggest the appropriate management of granulomatous tumors after breast conserving surgery in breast cancer survivors.
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Affiliation(s)
- Yuki Ichinose
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Yoshimasa Kosaka
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Toshiaki Saeki
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Akihiro Fujimoto
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Asami Nukui
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Aya Asano
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Hiroko Shimada
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Kazuo Matsuura
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Takahiro Hasebe
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Akihiko Osaki
- Department of Breast Oncology, Saitama Medical University International Medical Center, Saitama, Japan
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15
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Tekin L, Dinç Elibol F. Is There any Relationship Between Granulomatous Mastitis and Seasons? An Analysis of Seasonal Frequency, Clinical, and Radiologic Findings. Eur J Breast Health 2020; 16:235-243. [PMID: 33062962 DOI: 10.5152/ejbh.2020.5897] [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: 06/24/2020] [Accepted: 08/18/2020] [Indexed: 11/22/2022]
Abstract
Objective Idiopathic granulomatous mastitis (IGM) is a rare, resistant, and recurrent benign disease of the breast. IGM can be clinically and radiologically confused with breast carcinoma, and core needle biopsy is needed to diagnose. The etiology and pathogenesis of IGM have not been fully explained. This premenopausal disease may be associated with pregnancy, breastfeeding, autoimmune processes, inflammation, and oral contraceptives. However, there is no study on whether there is a seasonal relationship. Materials and Methods From January 2015 to January 2020, the seasonal relationship of IGM was evaluated in 37 females aged between 25-49. Results Although all cases were distributed between September and May, there was no statistically significant result in the relationship with the season. US is the main modality in the diagnosis of this condition which only provides an accurate pre-diagnosis approach with the typical USG appearance features. Some MRI features may help us to distinguish IGM from breast malignities. Conclusion IGM is a rare chronic non-specific inflammatory lesion of the breast, which can be confused with benign and malignant breast diseases in both clinical and radiologic aspects. To understand the etiology of this condition better, the seasonal connection needs to be evaluated in larger patient groups.
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Affiliation(s)
- Leyla Tekin
- Department of Pathology, Muğla Sıtkı Koçman University Faculty of Medicine, Muğla, Turkey
| | - Funda Dinç Elibol
- Department of Radiology, Muğla Sıtkı Koçman University Faculty of Medicine, Muğla, Turkey
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16
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Ai Z, Han Q, Huang Z, Wu J, Xiang Z. The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1128. [PMID: 33240977 PMCID: PMC7576072 DOI: 10.21037/atm-20-5109] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions. Methods In this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0–600 s/mm2), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves. Results There were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29. Conclusions A multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.
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Affiliation(s)
- Zhu Ai
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Zhiwei Huang
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Jiayan Wu
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
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