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  • 1
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    Online Resource
    Differentiation Between Benign and Metastatic Breast Lymph Nodes Using Apparent Diffusion Coefficients
    Frontiers Media SA ; 2022
    In:  Frontiers in Oncology Vol. 12 ( 2022-2-23)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 12 ( 2022-2-23)
    Abstract: The aim of this study was to determine the range of apparent diffusion coefficient (ADC) values for benign axillary lymph nodes in contrast to malignant axillary lymph nodes, and to define the optimal ADC thresholds for three different ADC parameters (minimum, maximum, and mean ADC) in differentiating between benign and malignant lymph nodes. This retrospective study included consecutive patients who underwent breast MRI from January 2017–December 2020. Two-year follow-up breast imaging or histopathology served as the reference standard for axillary lymph node status. Area under the receiver operating characteristic curve (AUC) values for minimum, maximum, and mean ADC (min ADC, max ADC, and mean ADC) for benign vs malignant axillary lymph nodes were determined using the Wilcoxon rank sum test, and optimal ADC thresholds were determined using Youden’s Index. The final study sample consisted of 217 patients (100% female, median age of 52 years (range, 22–81), 110 with benign axillary lymph nodes and 107 with malignant axillary lymph nodes. For benign axillary lymph nodes, ADC values (×10 −3 mm 2 /s) ranged from 0.522–2.712 for mean ADC, 0.774–3.382 for max ADC, and 0.071–2.409 for min ADC; for malignant axillary lymph nodes, ADC values (×10 −3 mm 2 /s) ranged from 0.796–1.080 for mean ADC, 1.168–1.592 for max ADC, and 0.351–0.688 for min ADC for malignant axillary lymph nodes. While there was a statistically difference in all ADC parameters (p & lt;0.001) between benign and malignant axillary lymph nodes, boxplots illustrate overlaps in ADC values, with the least overlap occurring with mean ADC, suggesting that this is the most useful ADC parameter for differentiating between benign and malignant axillary lymph nodes. The mean ADC threshold that resulted in the highest diagnostic accuracy for differentiating between benign and malignant lymph nodes was 1.004×10 −3 mm 2 /s, yielding an accuracy of 75%, sensitivity of 71%, specificity of 79%, positive predictive value of 77%, and negative predictive value of 74%. This mean ADC threshold is lower than the European Society of Breast Imaging (EUSOBI) mean ADC threshold of 1.300×10 −3 mm 2 /s, therefore suggesting that the EUSOBI threshold which was recently recommended for breast tumors should not be extrapolated to evaluate the axillary lymph nodes.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2022.795265
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
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  • 2
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    Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists’ Performance
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 7 ( 2022-03-29), p. 1743-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 7 ( 2022-03-29), p. 1743-
    Abstract: This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14071743
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 3
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    A phase I, open label, perioperative study of AG-120 and AG-881 in recurrent IDH1 mutant, low-grade glioma: Results from cohort 1.
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. 2003-2003
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. 2003-2003
    Abstract: 2003 Background: AG-120 (ivosidenib [IVO]) is a first-in-class oral inhibitor of mutant isocitrate dehydrogenase 1 (mIDH1) evaluated in 66 glioma patients (pts) in an ongoing phase 1 study. AG-881 (vorasidenib [VOR] ) is an oral, potent, brain-penetrant inhibitor of mIDH1/2 evaluated in 52 glioma pts in an ongoing phase 1 study. In an orthotopic glioma model, IVO and VOR reduced 2-hydroxyglutarate (2-HG) by 85% and 98%, respectively, despite different brain:plasma ratios ( 〈 0.04 vs 1.33). Methods: Primary endpoint: brain tumor 2-HG concentration with IVO or VOR treatment in mIDH1 low-grade glioma. Pts with recurrent non-enhancing WHO-2016 Grade (Gr) 2 or 3 mIDH1-R132H oligodendroglioma or astrocytoma undergoing craniotomy were randomized 2:2:1 to IVO 500mg QD, VOR 50mg QD, or no treatment for 4 wks preoperatively in Cohort 1. Post-operatively, pts continued to receive IVO or VOR and control pts were randomized 1:1 to IVO or VOR. Tumors were assessed for mIDH1 status, cellularity, 2-HG, and drug concentration. Treated samples were compared to control pts and mIDH1 and wild type (WT) banked reference (ref) samples. Plasma and CSF 2-HG were assessed. Pts with non-evaluable tissue were replaced. Results: As of 29 Nov 2018, 26 pts (17M, 9F; 25 Gr 2, 1 Gr 3) were randomized preoperatively (IVO 10, VOR 11, control 5), 25 received drug (IVO 12, VOR 13). At the data cut, 19 tumors were analyzed with 16 evaluable. Common ( 〉 10%) TEAEs (all grade 1/2): diarrhea (36%), hypocalcemia and constipation (each 20%), anemia, hyperglycemia, pruritus, headache and nausea (each 16%), and hypokalemia and fatigue (each 12%). Mean brain:plasma ratio: 0.16 for IVO, 2.4 for VOR. Tumor 2-HG results are shown in Table. Updated data from Cohort 1 will be presented. Conclusions: In Cohort 1 of this phase 1 perioperative study, IVO and VOR were CNS penetrant and lowered 2-HG compared to untreated samples. Cohort 2 is open and will evaluate IVO 250mg BID and VOR 10mg QD. Brain tumor 2-HG concentration. Clinical trial information: NCT03343197. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2019.37.15_suppl.2003
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
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  • 4
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    Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods
    Oxford University Press (OUP) ; 2022
    In:  BJR|Open Vol. 4, No. 1 ( 2022-01)
    Details
    In: BJR|Open, Oxford University Press (OUP), Vol. 4, No. 1 ( 2022-01)
    Abstract: Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.
    Type of Medium: Online Resource
    ISSN: 2513-9878
    URL: Article
    DOI: 10.1259/bjro.20210072
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 5
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    Diagnostic usefulness of water‐to‐fat ratio and choline concentration in malignant and benign breast lesions and normal breast parenchyma: An in vivo 1 H MRS study
    Wiley ; 2011
    In:  Journal of Magnetic Resonance Imaging Vol. 33, No. 4 ( 2011-04), p. 855-863
    Details
    In: Journal of Magnetic Resonance Imaging, Wiley, Vol. 33, No. 4 ( 2011-04), p. 855-863
    Abstract: To compare total choline concentrations ([Cho]) and water‐to‐fat (W/F) ratios of subtypes of malignant lesions, benign lesions, and normal breast parenchyma and determine their usefulness in breast cancer diagnosis. Reference standard was histology. Materials and Methods: In this HIPPA compliant study, proton MRS was performed on 93 patients with suspicious lesions ( 〉 1 cm) who underwent MRI‐guided interventional procedures, and on 27 prospectively accrued women enrolled for screening MRI. (W/F) and [Cho] values were calculated using MRS data. Results: Among 88 MRS‐evaluable histologically‐confirmed lesions, 40 invasive ductal carcinoma (IDC); 10 invasive lobular carcinoma (ILC); 4 ductal carcinoma in situ (DCIS); 3 invasive mammary carcinoma (IMC); 31 benign. No significant difference observed in (W/F) between benign lesions and normal breast tissue. The area under curve (AUC) of receiver operating characteristic (ROC) curves for discriminating the malignant group from the benign group were 0.97, 0.72, and 0.99 using [Cho], (W/F) and their combination as biomarkers, respectively. (W/F) performs significantly ( P 〈 0.0001;AUC = 0.96) better than [Cho] (AUC = 0.52) in differentiating IDC and ILC lesions. Conclusion: Although [Cho] and (W/F) are good biomarkers for differentiating malignancy, [Cho] is a better marker. Combining both can further improve diagnostic accuracy. IDC and ILC lesions have similar [Cho] levels but are discriminated using (W/F) values. J. Magn. Reson. Imaging 2011;33:855–863. © 2011 Wiley‐Liss, Inc.
    Type of Medium: Online Resource
    ISSN: 1053-1807 , 1522-2586
    URL: Issue
    URL: Article
    DOI: 10.1002/jmri.v33.4
    DOI: 10.1002/jmri.22493
    Language: English
    Publisher: Wiley
    Publication Date: 2011
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  • 6
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    Intravoxel incoherent motion diffusion‐weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma
    Wiley ; 2014
    In:  Journal of Magnetic Resonance Imaging Vol. 40, No. 4 ( 2014-10), p. 813-823
    Details
    In: Journal of Magnetic Resonance Imaging, Wiley, Vol. 40, No. 4 ( 2014-10), p. 813-823
    Abstract: To study the differentiation of malignant breast lesions from benign lesions and fibroglandular tissue (FGT) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters. Materials and Methods This retrospective study included 26 malignant and 14 benign breast lesions in 35 patients who underwent diffusion‐weighted MRI at 3.0T and nine b‐values (0–1000 s/mm 2 ). ADC and IVIM parameters (perfusion fraction f p , pseudodiffusion coefficient D p , and true diffusion coefficient D d ) were determined in lesions and FGT. For comparison, IVIM was also measured in 16 high‐risk normal patients. A predictive model was constructed using linear discriminant analysis. Lesion discrimination based on ADC and IVIM parameters was assessed using receiver operating characteristic (ROC) and area under the ROC curve (AUC). Results In FGT of normal subjects, f p was 1.1 ± 1.1%. In malignant lesions, f p (6.4 ± 3.1%) was significantly higher than in benign lesions (3.1 ± 3.3%, P = 0.0025) or FGT (1.5 ± 1.2%, P 〈 0.001), and D d ((1.29 ± 0.28) × 10 −3 mm 2 /s) was lower than in benign lesions ((1.56 ± 0.28) × 10 −3 mm 2 /s, P = 0.011) or FGT ((1.86 ± 0.34) × 10 −3 mm 2 /s, P 〈 0.001). A combination of D d and f p provided higher AUC for discrimination between malignant and benign lesions (0.84) or FGT (0.97) than ADC (0.72 and 0.86, respectively). Conclusion The IVIM parameters provide accurate identification of malignant lesions. J. Magn. Reson. Imaging 2014;40:813–823 . © 2013 Wiley Periodicals, Inc .
    Type of Medium: Online Resource
    ISSN: 1053-1807 , 1522-2586
    URL: Issue
    URL: Article
    DOI: 10.1002/jmri.v40.4
    DOI: 10.1002/jmri.24462
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 1146614-5
    detail.hit.zdb_id: 1497154-9
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  • 7
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    Spectrum separation resolves partial‐volume effect of MRSI as demonstrated on brain tumor scans
    Wiley ; 2008
    In:  NMR in Biomedicine Vol. 21, No. 10 ( 2008-12), p. 1030-1042
    Details
    In: NMR in Biomedicine, Wiley, Vol. 21, No. 10 ( 2008-12), p. 1030-1042
    Abstract: Magnetic resonance spectroscopic imaging (MRSI) is currently used clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and to evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability because of partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity, and measurement noise. We address these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. This ‘spectrum separation’ method uses the non‐negative matrix factorization algorithm, which simultaneously decomposes the observed spectra of multiple voxels into abundance distributions and constituent spectra. The accuracy of the estimated abundances is validated on phantom data. The presented results on 20 clinical cases of brain tumor show reduced cross‐subject variability. This is reflected in improved discrimination between high‐grade and low‐grade gliomas, which demonstrates the physiological relevance of the extracted spectra. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool. Copyright © 2008 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0952-3480 , 1099-1492
    URL: Issue
    URL: Article
    DOI: 10.1002/nbm.v21:10
    DOI: 10.1002/nbm.1271
    Language: English
    Publisher: Wiley
    Publication Date: 2008
    detail.hit.zdb_id: 2002003-X
    detail.hit.zdb_id: 1000976-0
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  • 8
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    Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers
    Springer Science and Business Media LLC ; 2020
    In:  European Radiology Vol. 30, No. 12 ( 2020-12), p. 6721-6731
    Details
    In: European Radiology, Springer Science and Business Media LLC, Vol. 30, No. 12 ( 2020-12), p. 6721-6731
    Abstract: To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.
    Type of Medium: Online Resource
    ISSN: 0938-7994 , 1432-1084
    URL: Article
    DOI: 10.1007/s00330-020-06991-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 1085366-2
    detail.hit.zdb_id: 1472718-3
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  • 9
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    Apparent diffusion coefficient mapping using diffusion-weighted MRI: impact of background parenchymal enhancement, amount of fibroglandular tissue and menopausal status on breast cancer diagnosis
    Springer Science and Business Media LLC ; 2018
    In:  European Radiology Vol. 28, No. 6 ( 2018-6), p. 2516-2524
    Details
    In: European Radiology, Springer Science and Business Media LLC, Vol. 28, No. 6 ( 2018-6), p. 2516-2524
    Type of Medium: Online Resource
    ISSN: 0938-7994 , 1432-1084
    URL: Article
    DOI: 10.1007/s00330-017-5202-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 1085366-2
    detail.hit.zdb_id: 1472718-3
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  • 10
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    Quantitative apparent diffusion coefficient measurement obtained by 3.0Tesla MRI as a potential noninvasive marker of tumor aggressiveness in breast cancer
    Elsevier BV ; 2016
    In:  European Journal of Radiology Vol. 85, No. 9 ( 2016-09), p. 1651-1658
    Details
    In: European Journal of Radiology, Elsevier BV, Vol. 85, No. 9 ( 2016-09), p. 1651-1658
    Type of Medium: Online Resource
    ISSN: 0720-048X
    URL: Article
    DOI: 10.1016/j.ejrad.2016.06.019
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 138815-0
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