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Iima M, Kataoka M, Honda M, Le Bihan D. Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer. Korean J Radiol 2024; 25:623-633. [PMID: 38942456 PMCID: PMC11214919 DOI: 10.3348/kjr.2023.1188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 02/28/2024] [Accepted: 04/11/2024] [Indexed: 06/30/2024] Open
Abstract
This study systematically reviewed the role of diffusion-weighted imaging (DWI) in the assessment of molecular prognostic biomarkers in breast cancer, focusing on the correlation of apparent diffusion coefficient (ADC) with hormone receptor status and prognostic biomarkers. Our meta-analysis includes data from 52 studies examining ADC values in relation to estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status. The results indicated significant differences in ADC values among different receptor statuses, with ER-positive, PgR-positive, HER2-negative, and Ki-67-positive tumors having lower ADC values compared to their negative counterparts. This study also highlights the potential of advanced DWI techniques such as intravoxel incoherent motion and non-Gaussian DWI to provide additional insights beyond ADC. Despite these promising findings, the high heterogeneity among the studies underscores the need for standardized DWI protocols to improve their clinical utility in breast cancer management.
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Affiliation(s)
- Mami Iima
- Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan
| | - Denis Le Bihan
- NeuroSpin, Joliot Institute, Department of Fundamental Research, Commissariat à l'Energie Atomique (CEA)-Saclay, Gif-sur-Yvette, France
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Amano M, Fujita S, Takei N, Sano K, Wada A, Sato K, Kikuta J, Kuwatsuru Y, Tachibana R, Sekine T, Horimoto Y, Aoki S. Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast. J Magn Reson Imaging 2023; 58:1752-1759. [PMID: 36951614 DOI: 10.1002/jmri.28683] [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: 01/21/2023] [Revised: 03/05/2023] [Accepted: 03/07/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Two-dimensional synthetic MRI of the breast has limited spatial coverage. Three-dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast. PURPOSE To evaluate the feasibility of 3D synthetic MRI using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki-67 expression in IDC. STUDY TYPE Prospective observational study. POPULATION A total of 33 females with IDC of the breast (mean, 52.3 years). FIELD STRENGTH/SEQUENCE A 3-T, 3D-QALAS gradient-echo and fat-suppressed T1-weighted 3D fast spoiled gradient-echo sequences. ASSESSMENT Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1-weighted images acquired from 3D-QALAS. The mean T1 and T2 were measured for each IDC. STATISTICAL TESTS Intraclass correlation coefficient for assessing interobserver agreement. Mann-Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant. RESULTS The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR-positive and HR-negative lesions, while the mean T2 value differed between HR-positive and HR-negative lesions, between the triple-negative and HR-positive or HER2-positive lesions, and between the Ki-67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR-negative IDC than in HR-positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple-negative status, and Ki-67 level. DATA CONCLUSION 3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Maki Amano
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiology, Nihon University Hospital, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | | | - Katsuhiro Sano
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | - Kanako Sato
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | - Junko Kikuta
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
| | | | - Rina Tachibana
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Towa Sekine
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Yoshiya Horimoto
- Department of Breast Oncology, Juntendo University Hospital, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Hospital, Tokyo, Japan
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Kim HY, Bae MS, Seo BK, Lee JY, Cho KR, Woo OH, Song SE, Cha J. Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study. Bioengineering (Basel) 2023; 10:bioengineering10050504. [PMID: 37237574 DOI: 10.3390/bioengineering10050504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. METHODS This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan-Meier analysis. RESULTS Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). CONCLUSIONS Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients.
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Affiliation(s)
- Hyo-Young Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Min-Sun Bae
- Department of Radiology, Inha University Hospital, Inha University College of Medicine, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Republic of Korea
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Republic of Korea
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jaehyung Cha
- Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
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Liang X, Chen X, Yang Z, Liao Y, Wang M, Li Y, Fan W, Dai Z, Zhang Y. Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer. BMC Cancer 2022; 22:1250. [PMID: 36460972 PMCID: PMC9716688 DOI: 10.1186/s12885-022-10315-x] [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: 05/16/2022] [Accepted: 11/14/2022] [Indexed: 12/04/2022] Open
Abstract
INTRODUCTION Improving the early prediction of neoadjuvant chemotherapy (NAC) efficacy in breast cancer can lead to an improved prediction of the final prognosis of patients, which would be useful for promoting individualized treatment. This study aimed to explore the value of the combination of dynamic contrast-enhanced (DCE)-MRI parameters and apparent diffusion coefficient (ADC) values in the early prediction of pathological complete response (pCR) to NAC for breast cancer. METHODS A total of 119 (range, 28-69 years) patients with biopsy-proven breast cancer who received two cycles of NAC before breast surgery were retrospectively enrolled from our hospital database. Patients were divided into pCR and non pCR groups according to their pathological responses; a total of 24 patients achieved pCR, while 95 did not. The quantitative (Ktrans; Kep; Ve; IAUC) and semiquantitative parameters (W-in; W-out; TTP) of DCE-MRI that were significantly different between groups were combined with ADC values to explore their value in the early prediction of pCR to NAC for breast cancer. The independent T test was performed to compare the differences in DCE-MRI parameters and ADC values between the two groups. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC), sensitivity and specificity were calculated to evaluate the performance of the prediction. RESULTS The Ktrans, Kep, IAUC, ADC, W-in and TTP values were significantly different between the pCR and non pCR groups after NAC. The AUC (0.845) and specificity (95.79%) of the combined Ktrans, Kep, IAUC and ADC values were both higher than those of the individual parameters. The combination of W-in, TTP and ADC values had the highest AUC value (0.886) in predicting pCR, with a sensitivity and specificity of 87.5% and 82.11%, respectively. CONCLUSIONS The results suggested that the combination of ADC values and quantitative and semiquantitative DCE-MRI parameters, especially the combination of W-in, TTP, and ADC values, may improve the early prediction of pCR in breast cancer.
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Affiliation(s)
- Xinhong Liang
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Xiaofeng Chen
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Zhiqi Yang
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | | | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthineers, Guangzhou, 510620 China
| | - Yulin Li
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Weixiong Fan
- grid.459766.fDepartment of Radiology, Meizhou People’s Hospital, Meizhou, 514031 China
| | - Zhuozhi Dai
- grid.452734.3Department of Radiology, Shantou Central Hospital, Guangdong, 515041 China
| | - Yunuo Zhang
- grid.459766.fDepartment of Oncology, Meizhou People’s Hospital, Meizhou, 514031 China
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Murakami W, Won Choi H, Joines MM, Hoyt A, Doepke L, McCann KE, Salamon N, Sayre J, Lee-Felker S. Quantitative Predictors of Response to Neoadjuvant Chemotherapy on Dynamic Contrast-enhanced 3T Breast MRI. JOURNAL OF BREAST IMAGING 2022; 4:168-176. [PMID: 38422427 DOI: 10.1093/jbi/wbab095] [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: 03/23/2021] [Indexed: 03/02/2024]
Abstract
OBJECTIVE To assess whether changes in quantitative parameters on breast MRI better predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer than change in volume. METHODS This IRB-approved retrospective study included women with newly diagnosed breast cancer who underwent 3T MRI before and during NAC from January 2013 to December 2019 and underwent surgery at our institution. Clinical data such as age, histologic diagnosis and grade, biomarker status, clinical stage, maximum index cancer dimension and volume, and surgical pathology (presence or absence of in-breast pCR) were collected. Quantitative parameters were calculated using software. Correlations between clinical features and MRI quantitative measures in pCR and non-pCR groups were assessed using univariate and multivariate logistic regression. RESULTS A total of 182 women with a mean age of 52 years (range, 26-79 years) and 187 cancers were included. Approximately 45% (85/182) of women had pCR at surgery. Stepwise multivariate regression analysis showed statistical significance for changes in quantitative parameters (increase in time to peak and decreases in peak enhancement, wash out, and Kep [efflux rate constant]) for predicting pCR. These variables in combination predicted pCR with 81.2% accuracy and an area under the curve (AUC) of 0.878. The AUCs of change in index cancer volume and maximum dimension were 0.767 and 0.613, respectively. CONCLUSION Absolute changes in quantitative MRI parameters between pre-NAC MRI and intra-NAC MRI could help predict pCR with excellent accuracy, which was greater than changes in index cancer volume and maximum dimension.
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Affiliation(s)
- Wakana Murakami
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
- Showa University Graduate School of Medicine, Department of Radiology, Shinagawa-ku, Tokyo, Japan
| | - Hyung Won Choi
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Melissa M Joines
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Anne Hoyt
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Laura Doepke
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Kelly E McCann
- University of California at Los Angeles David Geffen School of Medicine, Department of Medicine, Los Angeles, CA, USA
| | - Noriko Salamon
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - James Sayre
- University of California at Los Angeles Fielding School of Public Health, Department of Biostatistics, Los Angeles, CA, USA
| | - Stephanie Lee-Felker
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
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Roytman M, Kim S, Glynn S, Thomas C, Lin E, Feltus W, Magge RS, Liechty B, Schwartz TH, Ramakrishna R, Karakatsanis NA, Pannullo SC, Osborne JR, Knisely JPS, Ivanidze J. PET/MR Imaging of Somatostatin Receptor Expression and Tumor Vascularity in Meningioma: Implications for Pathophysiology and Tumor Outcomes. Front Oncol 2022; 11:820287. [PMID: 35155210 PMCID: PMC8832502 DOI: 10.3389/fonc.2021.820287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Meningiomas, the most common primary intracranial tumor, are vascular neoplasms that express somatostatin receptor-2 (SSTR2). The purpose of this investigation was to evaluate if a relationship exists between tumor vascularity and SSTR2 expression, which may play a role in meningioma prognostication and clinical management. Materials and Methods Gallium-68-DOTATATE PET/MRI with dynamic contrast-enhanced (DCE) perfusion was prospectively performed. Clinical and demographic patient characteristics were recorded. Tumor volumes were segmented and superimposed onto parametric DCE maps including flux rate constant (Kep), transfer constant (Ktrans), extravascular volume fraction (Ve), and plasma volume fraction (Vp). Meningioma PET standardized uptake value (SUV) and SUV ratio to superior sagittal sinus (SUVRSSS) were recorded. Pearson correlation analyses were performed. In a random subset, analysis was repeated by a second investigator, and intraclass correlation coefficients (ICCs) were determined. Results Thirty-six patients with 60 meningiomas (20 WHO-1, 27 WHO-2, and 13 WHO-3) were included. Mean Kep demonstrated a strong significant positive correlation with SUV (r = 0.84, p < 0.0001) and SUVRSSS (r = 0.81, p < 0.0001). When stratifying by WHO grade, this correlation persisted in WHO-2 (r = 0.91, p < 0.0001) and WHO-3 (r = 0.92, p = 0.0029) but not WHO-1 (r = 0.26, p = 0.4, SUVRSSS). ICC was excellent (0.97–0.99). Conclusion DOTATATE PET/MRI demonstrated a strong significant correlation between tumor vascularity and SSTR2 expression in WHO-2 and WHO-3, but not WHO-1 meningiomas, suggesting biological differences in the relationship between tumor vascularity and SSTR2 expression in higher-grade meningiomas, the predictive value of which will be tested in future work.
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Affiliation(s)
- Michelle Roytman
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Sean Kim
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Shannon Glynn
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Charlene Thomas
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Eaton Lin
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Whitney Feltus
- Departments of Radiology, New York-Presbyterian Hospital/Columbia University Medical Center, New York, NY, United States
| | - Rajiv S. Magge
- Department of Neurology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Benjamin Liechty
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Theodore H. Schwartz
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Rohan Ramakrishna
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Nicolas A. Karakatsanis
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Susan C. Pannullo
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Joseph R. Osborne
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Jonathan P. S. Knisely
- Department of Radiation Oncology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Jana Ivanidze
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
- *Correspondence: Jana Ivanidze,
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Romeo V. Standardization of Quantitative DCE-MRI Parameters Measurement: An Urgent Need for Breast Cancer Imaging. Acad Radiol 2022; 29 Suppl 1:S87-S88. [PMID: 34991941 DOI: 10.1016/j.acra.2021.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 12/05/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, Naples, 80138, Italy (V.R.).
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Yan J, Xue X, Gao C, Guo Y, Wu L, Zhou C, Chen F, Xu M. Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images. Quant Imaging Med Surg 2022; 12:642-652. [PMID: 34993108 DOI: 10.21037/qims-20-1385] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 07/29/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND The Ki-67 proliferation index (PI) reflects the proliferation of cells. However, the conventional methods for the acquisition of the Ki-67 PI, such as surgery and biopsy, are generally invasive. This study investigated a potential noninvasive method of predicting the Ki-67 PI in patients with lung adenocarcinoma presenting with subsolid nodules. METHODS This retrospective study enrolled 153 patients who presented with pulmonary adenocarcinoma appearing as subsolid nodules (SSNs) on computed tomography (CT) images between January 2015 and December 2018. Presence of LUAD with SSNs was confirmed by histopathology. Of these participants, 107 patients were from institution 1 and were divided into a training cohort and an internal validation cohort in a 7:3 ratio. The other 46 patients were from institution 2 and were enrolled as an external validation cohort. All patients underwent conventional CT scans with thin-slice (≤1.25 mm) reconstruction, and 1,316 quantitative radiomic features were extracted from the CT images for each nodule. The minimum redundancy maximum relevance and the least absolute shrinkage and selection operator were used for feature selection, and the radiomics signature was constructed based on these selected features. Clinical features were examined using univariate logistic regression analysis. The nomogram was developed based on the radiomics signature and the independent clinical risk factors. The Delong test and t test were employed for statistical analysis. The performance of different models was assessed by the receiver operating characteristic (ROC) curve. RESULTS The diameter of the nodules [odds ratio (OR) =1.17; P=0.003] was identified as an independent predictive parameter. Both the radiomics signature and the nomogram suggested a good predictive probability for Ki-67 expression. For the radiomics signature, the area under the ROC curve (AUC) for the training cohort, the internal validation cohort, and the external validation cohort was 0.86 [95% confidence interval (CI): 0.77 to 0.95], 0.81 (95% CI: 0.64 to 0.98), and 0.77 (95% CI: 0.62 to 0.91), respectively. For the nomogram, the AUC for the training cohort, the internal validation cohort, and the external validation cohort was 0.86 (95% CI: 0.77 to 0.95), 0.80 (95% CI: 0.64 to 0.97), and 0.79 (95% CI: 0.65 to 0.94), respectively. There were no statistical differences in the AUCs between the radiomics signature and the radiomic nomogram in the training cohort or the validation cohorts (all P>0.05). CONCLUSIONS The nomogram provides a novel strategy for determining the Ki-67 PI in predicting the proliferation of subsolid nodules, which may be beneficial for the management of patients with SSNs.
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Affiliation(s)
- Jing Yan
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xing Xue
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chen Gao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Yifan Guo
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Linyu Wu
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Changyu Zhou
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Feng Chen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Maosheng Xu
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Ya G, Wen F, Xing-ru L, Zhuan-zhuan G, Jun-qiang L. Difference of DCE-MRI Parameters at Different Time Points and Their Predictive Value for Axillary Lymph Node Metastasis of Breast Cancer. Acad Radiol 2022; 29 Suppl 1:S79-S86. [PMID: 33504446 DOI: 10.1016/j.acra.2021.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/09/2021] [Accepted: 01/11/2021] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES To assess differences of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) parameters at different postcontrast time points (TPs), and to explore the predictive value of DCE-MRI parameters for axillary lymph node (ALN) metastasis of breast cancer. MATERIALS AND METHODS A total of 107 breast cancer patients were included retrospectively, and 50 phases were collected on DCE-MRI for each patient. DCE-MRI parameters Ktrans, Kep, Ve, TTP, Peak, Washin, Washout, and AUC were extracted from the images at 67.8 seconds, 128.5 seconds, 189.2 seconds, 249.9 seconds, and 310.5 seconds (regard as TP1, 2, 3, 4, and 5). Wilcoxon signed rank test was used to compare DCE-MRI parameters at different postcontrast TPs. Logistic regression was performed to analyze the predictive value of DCE-MRI parameters for ALN metastasis of breast cancer, and receiver operating characteristic (ROC) curve was constructed to evaluate the predictive performance. RESULTS The difference of DCE-MRI parameters between TP1, 2, 3, 4, and 5 was statistically significant (p < 0.01) in breast cancer. The TPs are considered as the optimal TPs when DCE-MRI parameters values reach the maximum. The optimal TPs of Ktrans, Kep, and Ve were respectively at TP2, TP2, and TP4 (Ktrans2, Kep2, and Ve4). The optimal TPs of TTP, Peak, and AUC were at TP5 (TTP5, Peak5, and AUC5). AUC5 showed the ability to predict ALN metastasis of breast cancer (area under ROC curve = 0.656, p < 0.05). CONCLUSIONS DCE-MRI parameters values were different at different postcontrast TPs. AUC5 may be an independent predictor of ALN metastasis in breast cancer.
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Tang W, Zhou H, Quan T, Chen X, Zhang H, Lin Y, Wu R. XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses. Front Oncol 2022; 12:833680. [PMID: 35372060 PMCID: PMC8968064 DOI: 10.3389/fonc.2022.833680] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/21/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The malignant probability of MRI BiRADS 4 breast lesions ranges from 2% to 95%, leading to unnecessary biopsies. The purpose of this study was to construct an optimal XGboost prediction model through a combination of DKI independently or jointly with other MR imaging features and clinical characterization, which was expected to reduce false positive rate of MRI BiRADS 4 masses and improve the diagnosis efficiency of breast cancer. METHODS 120 patients with 158 breast lesions were enrolled. DKI, Diffusion-weighted Imaging (DWI), Proton Magnetic Resonance Spectroscopy (1H-MRS) and Dynamic Contrast-Enhanced MRI (DCE-MRI) were performed on a 3.0-T scanner. Wilcoxon signed-rank test and χ2 test were used to compare patient's clinical characteristics, mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), total choline (tCho) peak, extravascular extracellular volume fraction (Ve), flux rate constant (Kep) and volume transfer constant (Ktrans). ROC curve analysis was used to analyze the diagnostic performances of the imaging parameters. Spearman correlation analysis was performed to evaluate the associations of imaging parameters with prognostic factors and breast cancer molecular subtypes. The Least Absolute Shrinkage and Selectionator operator (lasso) and the area under the curve (AUC) of imaging parameters were used to select discriminative features for differentiating the breast benign lesions from malignant ones. Finally, an XGboost prediction model was constructed based on the discriminative features and its diagnostic efficiency was verified in BiRADS 4 masses. RESULTS MK derived from DKI performed better for differentiating between malignant and benign lesions than ADC, MD, tCho, Kep and Ktrans (p < 0.05). Also, MK was shown to be more strongly correlated with histological grade, Ki-67 expression and lymph node status. MD, MK, age, shape and menstrual status were selected to be the optimized feature subsets to construct an XGboost model, which exhibited superior diagnostic ability for breast cancer characterization and an improved evaluation of suspicious breast tumors in MRI BiRADS 4. CONCLUSIONS DKI is promising for breast cancer diagnosis and prognostic factor assessment. An optimized XGboost model that included DKI, age, shape and menstrual status is effective in improving the diagnostic accuracy of BiRADS 4 masses.
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Affiliation(s)
- Wan Tang
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Institute of Health Monitoring, Inspection and Protection, Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
| | - Han Zhou
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Tianhong Quan
- Department of Electronic and information Engineering, College of Engineering, Shantou University, Shantou, China
| | - Xiaoyan Chen
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Huanian Zhang
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yan Lin
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Renhua Wu
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
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11
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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: 10] [Impact Index Per Article: 3.3] [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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12
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Lai HW, Chang YL, Chen ST, Chang YJ, Wu WP, Chen DR, Kuo SJ, Liao CY, Wu HK. Revisit the practice of lymph node biopsy in patients diagnosed as ductal carcinoma in situ before operation: a retrospective analysis of 682 cases and evaluation of the role of breast MRI. World J Surg Oncol 2021; 19:263. [PMID: 34470633 PMCID: PMC8411510 DOI: 10.1186/s12957-021-02336-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/19/2021] [Indexed: 11/25/2022] Open
Abstract
Background The optimal axillary lymph node (ALN) management strategy in patients diagnosed with ductal carcinoma in situ (DCIS) preoperatively remains controversial. The value of breast magnetic resonance imaging (MRI) to predict ALN metastasis pre-operative DCIS patients was evaluated. Methods Patients with primary DCIS with or without pre-operative breast MRI evaluation and underwent breast surgery were recruited from single institution. The value of breast MRI for ALN evaluation, predictors of breast and ALN surgeries, upgrade from DCIS to invasive cancer, and ALN metastasis were analyzed. Results A total of 682 cases with pre-operative diagnosis of DCIS were enrolled in current study. The rate of upgrade to invasive cancer were found in 34.2% of specimen, and this upgrade rate is 23% for patients who received breast conserving surgery and 40.7% for mastectomy (p < 0.01). Large pre-operative imaging tumor size and post-operative invasive component were risk factors to ALN metastasis. Breast MRI had 53.8% sensitivity, 77.8% specificity, 14.9% positive predictive value, 95.9% negative predictive value (NPV), and 76.2% accuracy to predict ALN metastasis in pre-OP DCIS patients. In MRI node-negative breast cancer patients with MRI tumor size < 3 cm, the NPV was 96.4%, and all these false-negative cases were N1. Pre-OP diagnosed DCIS patients with MRI tumor size < 3 cm and node negative suitable for BCS could safely omit SLNB if whole breast radiotherapy is to be performed. Conclusion Breast MRI had high NPV to predict ALN metastasis in pre-OP DCIS patients, which is useful and could be provided as shared decision-making reference.
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Affiliation(s)
- Hung-Wen Lai
- Endoscopic & Oncoplastic Breast Surgery Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan. .,Division of General Surgery, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan. .,Comprehensive Breast Cancer Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan. .,Minimal Invasive Surgery Research Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan. .,Tumor Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan. .,Department of Surgery, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Division of Breast Surgery, Yuanlin Christian Hospital, Yuanlin, Taiwan. .,School of Medicine, Chung Shan Medical University, Taichung, Taiwan. .,Department of Biomedical Imaging and Radiological Sciences, Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. .,Chang Gung University College of Medicine, Taoyuan City, Taiwan. .,Division of General Surgery, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
| | - Yi-Lin Chang
- Division of General Surgery, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Shou-Tung Chen
- Division of General Surgery, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.,Comprehensive Breast Cancer Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Yu-Jun Chang
- Center for Research and Epidemiology, Big Data Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Wen-Pei Wu
- Department of Surgery, Kaohsiung Medical University, Kaohsiung, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Radiology, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Dar-Ren Chen
- Endoscopic & Oncoplastic Breast Surgery Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.,Division of General Surgery, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.,Comprehensive Breast Cancer Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.,School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Shou-Jen Kuo
- Division of General Surgery, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.,Comprehensive Breast Cancer Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Chiung-Ying Liao
- Department of Radiology, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Hwa-Koon Wu
- Department of Radiology, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
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13
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Lee JY, Lee KS, Seo BK, Cho KR, Woo OH, Song SE, Kim EK, Lee HY, Kim JS, Cha J. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI. Eur Radiol 2021; 32:650-660. [PMID: 34226990 DOI: 10.1007/s00330-021-08146-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/01/2021] [Accepted: 06/09/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). METHODS This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. RESULTS Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (Ve). CONCLUSIONS Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer. KEY POINTS • Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer. • The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network). • The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (Ve) among perfusion parameters.
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Affiliation(s)
- Ji Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang, Gyeonggi-do, 10380, Republic of Korea
| | - Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do, 15355, Republic of Korea.
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Yongin Severance Hospital, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea
| | - Hye Yoon Lee
- Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Jung Sun Kim
- Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Jaehyung Cha
- Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
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14
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Wijsman PJM, Voskens FJ, Molenaar L, van 't Hullenaar CDP, Consten ECJ, Draaisma WA, Broeders IAMJ. Efficiency in image-guided robotic and conventional camera steering: a prospective randomized controlled trial. Surg Endosc 2021; 36:2334-2340. [PMID: 33977377 DOI: 10.1007/s00464-021-08508-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 04/07/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Robotic camera steering systems have been developed to facilitate endoscopic surgery. In this study, a randomized controlled trial was conducted to compare conventional human camera control with the AutoLap™ robotic camera holder in terms of efficiency and user experience when performing routine laparoscopic procedures. Novelty of this system relates to the steering method, which is image based. METHODS Patients undergoing an elective laparoscopic hemicolectomy, sigmoid resection, fundoplication and cholecystectomy between September 2016 and January 2018 were included. Stratified block randomization was used for group allocation. The primary aim of this study was to compare the efficiency of robotic and human camera control, measured with surgical team size and total operating time. Secondary outcome parameters were number of cleaning moments of the laparoscope and the post-study system usability questionnaire. RESULTS A total of 100 patients were randomized to have robotic (50) versus human (50) camera control. Baseline characteristics did not differ significantly between groups. In the robotic group, 49/50 (98%) of procedures were carried out without human camera control, reducing the surgical team size from four to three individuals. The median total operative time (60.0 versus 53.0 min, robotic vs. control) was not significantly different, p = 0.122. The questionnaire showed a positive user satisfaction and easy control of the robotic camera holder. CONCLUSION Image-based robotic camera control can reduce surgical team size and does not result in significant difference in operative time compared to human camera control. Moreover, robotic image-guided camera control was associated with positive user experience.
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Affiliation(s)
- P J M Wijsman
- Department of Surgery, Meander Medical Center, Maatweg 3, Amersfoort, The Netherlands
- Department of Surgery, Jeroen Bosch Hospital, 's Hertogenbosch, The Netherlands
- Robotics and Mechatronics, University of Twente, Enschede, The Netherlands
| | - F J Voskens
- Department of Surgery, Meander Medical Center, Maatweg 3, Amersfoort, The Netherlands
| | - L Molenaar
- Department of Surgery, Meander Medical Center, Maatweg 3, Amersfoort, The Netherlands
- Magnetic Detection & Imaging, University of Twente, Enschede, The Netherlands
| | | | - E C J Consten
- Department of Surgery, Meander Medical Center, Maatweg 3, Amersfoort, The Netherlands
| | - W A Draaisma
- Department of Surgery, Jeroen Bosch Hospital, 's Hertogenbosch, The Netherlands
| | - I A M J Broeders
- Department of Surgery, Meander Medical Center, Maatweg 3, Amersfoort, The Netherlands.
- Robotics and Mechatronics, University of Twente, Enschede, The Netherlands.
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15
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Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization. Cancers (Basel) 2020; 12:cancers12123763. [PMID: 33327532 PMCID: PMC7765071 DOI: 10.3390/cancers12123763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Confirming whether a breast lesion is benign or malignant usually involves an invasive tissue sample with an image-guided breast biopsy, which may cause substantial inconvenience to the patient. The purpose of this study was to investigate whether imaging biomarkers obtained from noninvasive dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can help differentiate benign from malignant lesions and characterize breast cancers to the same extent as a biopsy. In a sample of 37 patients with suspicious findings on mammography or ultrasound, we found that the radiologists’ diagnostic accuracy was improved when subjective Breast Imaging-Reporting and Data System (BI-RADS) evaluation was augmented with the use of pharmacokinetic markers. This study serves as a starting point for future collaborative research with the potential of providing valuable noninvasive tools for improved breast cancer diagnosis. Abstract The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.
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16
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Du S, Gao S, Zhang L, Yang X, Qi X, Li S. Improved discrimination of molecular subtypes in invasive breast cancer: Comparison of multiple quantitative parameters from breast MRI. Magn Reson Imaging 2020; 77:148-158. [PMID: 33309922 DOI: 10.1016/j.mri.2020.12.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 11/21/2020] [Accepted: 12/06/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To compare multiple quantitative parameters from breast magnetic resonance imaging (MRI) with the synthetic MRI sequence included for discrimination of molecular subtypes of invasive breast cancer. MATERIALS AND METHODS Between March 2019 and September 2020, two hundred breast cancer patients underwent preoperative breast multiparametric MRI examinations including synthetic MRI, diffusion weighted imaging (DWI) and dynamic contrast enhancement (DCE)-MRI sequences. MRI morphological features, T1 and T2 relaxation times (T1, T2) and proton density (PD) values from synthetic MRI, Ktrans, Kep, and Ve from DCE-MRI, mean apparent diffusion coefficient (ADC) from DWI and tumor volume were measured. Quantitative parameters were compared according to molecular markers and subtypes. Logistic regression were performed to find the related MRI parameters and establish combined parameters. The comparison between single and combined quantitative parameters by using DeLong tests. RESULTS T1, T2 values were significantly higher in hormone receptor (HR)- negative and Ki67 > 14% tumors (p < 0.05). Human epidermal growth factor receptor 2 (HER2)-positive tumors demonstrated significantly higher Ktrans and Kep (p < 0.01). Mean ADC values were significantly decreased in HR-positive and Ki67 > 14% tumors (p < 0.01). Tumor volumes were significantly higher in HER2-positive and Ki67 > 14% tumors (p < 0.05). Independent influencing factors were lower T2 values (p < 0.001), smaller tumor volume (p = 0.031) and higher mean ADC (p = 0.002) associated with luminal A subtype, while T1 values (p = 0.007) was the only quantitative parameter associated with triple-negative subtype. The diagnostic efficiency of combined parameters (T2 + mean ADC + volume) (AUC = 0.765) was significantly higher than that of mean ADC (AUC = 0.666, p = 0.031 by DeLong test) and volume (AUC = 0.650, p = 0.008 by DeLong test) for separating luminal A subtype. CONCLUSIONS MRI quantitative parameters could help distinguish molecular markers and subtypes. The emerging synthetic MRI parameters - T1 values were associated with the TN subtype, and combined parameters with added T2 values might improve the discrimination of the luminal A subtype. Application of synthetic MRI can enrich quantitative descriptors from breast MRI.
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Affiliation(s)
- Siyao Du
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Si Gao
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Lina Zhang
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China.
| | - Xiaoping Yang
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Xixun Qi
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Shu Li
- Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
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17
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Incoronato M, Mirabelli P, Grimaldi AM, Soricelli A, Salvatore M. Correlating imaging parameters with molecular data: An integrated approach to improve the management of breast cancer patients. Int J Biol Markers 2020; 35:47-50. [PMID: 32079469 DOI: 10.1177/1724600819899665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The goal of this review is to provide an overview of the studies aimed at integrating imaging parameters with molecular biomarkers for improving breast cancer patient's diagnosis and prognosis. The use of diagnostic imaging to extract quantitative parameters related to the morphology, metabolism, and functionality of tumors, as well as their correlation with cancer tissue biomarkers is an emerging research topic. Thanks to the development of imaging biobanks and the technological tools required for extraction of imaging parameters including radiomic features, it is possible to integrate imaging markers with genetic data. This new field of study represents the evolution of radiology-pathology correlation from an anatomic-histologic level to a genetic level, which paves new interesting perspectives for breast cancer management.
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Affiliation(s)
| | | | | | - Andrea Soricelli
- IRCCS SDN, Naples, Italy.,Department of Motor Sciences & Healthiness, University of Naples Parthenope, Naples, Italy
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18
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Iima M. Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends. Magn Reson Med Sci 2020; 20:125-138. [PMID: 32536681 PMCID: PMC8203481 DOI: 10.2463/mrms.rev.2019-0124] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital
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19
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Iima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K. Diffusion MRI of the breast: Current status and future directions. J Magn Reson Imaging 2019; 52:70-90. [PMID: 31520518 DOI: 10.1002/jmri.26908] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/12/2019] [Indexed: 12/30/2022] Open
Abstract
Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, New York, New York, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York, New York, USA
| | - Ayami Ohno Kishimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Prognostic value of preoperative dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian cancer. Eur J Radiol 2019; 115:66-73. [DOI: 10.1016/j.ejrad.2019.03.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/20/2019] [Accepted: 03/29/2019] [Indexed: 01/24/2023]
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