Integrated 18F-FDG PET/MRI in breast cancer: early prediction of response to neoadjuvant chemotherapy

Cho, Nariya; Im, Seock‐Ah; Cheon, Gi Jeong; Park, In-Ae; Lee, Kyung-Hun; Kim, Tae-Yong; Kim, Young Seon; Kwon, Bo Ra; Lee, Jung Min; Suh, Hoon Young; Suh, Koung Jin

doi:10.1007/s00259-017-3849-3

Cited by 45 publications

(35 citation statements)

References 34 publications

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“…Cho et al performed 18 F‐FDG PET/MRI in 26 breast cancer patients before and after the first round of chemotherapy and evaluated qualitative MRI parameters as well as quantitative PET and MRI parameters . While the qualitative MRI parameters were not found to be different between pathologic complete responders and pathologic noncomplete responders, reductions in total lesion glycolysis (TLG) and signal enhancement ratio (SER) were different between the two groups.…”

Section: Neoadjuvant Therapymentioning

confidence: 99%

PET/MRI in Breast Cancer

Pujara

Kim

Axelrod

et al. 2018

Magnetic Resonance Imaging

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Section: Neoadjuvant Therapymentioning

confidence: 99%

PET/MRI in Breast Cancer

Pujara

Kim

Axelrod

et al. 2018

Magnetic Resonance Imaging

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“…Recently, a review focused on the value of FDG PET/CT as a response prediction tool in breast cancer [31]. Although the current study does not present a head-tohead comparison to a pure PET/CT approach, the data suggest that PET/MRI will most likely be superior since the MRI increased significantly the sensitivity as well as the specificity of PET [29].…”

mentioning

confidence: 80%

“…In this issue, a paper by Cho and coworkers reporting a prospective FDG PET/MRI study in breast cancer patients scheduled to receive neo-adjuvant chemotherapy is published [29]. In an earlier study, it was shown that simultaneous PET/ MRI can enable illustration of close interactions between glucose metabolism and pharmacokinetic parameters in breast This Editorial Commentary refers to the article https://doi.org/10.1007/ s00259-017-3849-3 cancer patients and the potential of this for response assessment was discussed [30].…”

mentioning

confidence: 99%

“…In an earlier study, it was shown that simultaneous PET/ MRI can enable illustration of close interactions between glucose metabolism and pharmacokinetic parameters in breast This Editorial Commentary refers to the article https://doi.org/10.1007/ s00259-017-3849-3 cancer patients and the potential of this for response assessment was discussed [30]. In the study by Cho and coworkers, subjects received PET/MRI scans before the start of NAC and after the first cycle of NAC [29]. In total, 26 patients were enrolled, of which 19 were classified as responders based on the histological classification of the surgery specimen and seven as non-responders.…”

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confidence: 99%

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Is the whole larger than the sum of the parts? Integrated PET/MRI as a tool for response prediction

Mottaghy

2017

Eur J Nucl Med Mol Imaging

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“…Positron emission tomography/magnetic resonance imaging (PET/MRI) is a two-in-one hybrid imaging technology that could provide competitive advantages over PET/X-ray computed tomography (PET/CT), including superior soft tissue contrast, advanced options for multiparametric MRI and MR spectroscopy, superior motion-tracking capabilities, and reduced radiation exposure [1]. A number of studies reported that 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) PET/MRI has the potential to qualitatively and quantitatively improve tumor characterization in breast cancer [2,3]. Xin et al [4] also reported that PET/MRI could provide good image quality suitable for the detection of abdominal and pelvic tumors.…”

Section: Introductionmentioning

confidence: 99%

Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

et al. 2019

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Purpose: The aim of this work is to investigate the impact of tissue classification in magnetic resonance imaging (MRI)-guided positron emission tomography (PET) attenuation correction (AC) for whole-body (WB) Patlak net uptake rate constant (K i ) imaging in PET/MRI studies. Procedures: WB dynamic PET/CT data were acquired for 14 patients. The CT images were utilized to generate attenuation maps (μ-map CTAC ) of continuous attenuation coefficient values (A coeff ). The μ-map CTAC were then segmented into four tissue classes (μ-map 4-classes ), namely background (air), lung, fat, and soft tissue, where a predefined A coeff was assigned to each class. To assess the impact of bone for AC, the bones in the μ-map CTAC were then assigned a predefined soft tissue A coeff (0.1 cm −1 ) to produce an AC μ-map without bones (μ-map no-bones ). Thereafter, both WB static SUV and dynamic PET images were reconstructed using μ-map CTAC , μ-map 4-classes , and μ-map no-bones (PET CTAC, PET 4-classes , and PET no-bones ), respectively. WB indirect and direct parametric K i images were generated using Patlak graphical analysis. Malignant lesions were delineated on PET images with an automatic segmentation method that uses an active contour model (MASAC). Then, the quantitative metrics of the metabolically active tumor volume (MATV), target-to-background (TBR), contrast-to-noise ratio (CNR), peak region-of-interest (ROI peak ), maximum region-of-interest (ROI max ), mean region-of-interest (ROI mean ), and metabolic volume product (MVP) were analyzed. The Wilcoxon test was conducted to assess the difference between PET 4-classes and PET no-bones against PET CTAC for all images. The same test was also adopted to compare the differences between SUV, indirect K i , and direct K i images for each evaluated AC method.Results: No significant differences in MATV, TBR, and CNR were observed between PET 4classes and PET CTAC for either SUV or K i images. PET 4-classes significantly overestimated ROI peak , ROI max , ROI mean , as well as MVP scores compared with PET CTAC in both SUV and K i images. SUV images exhibited the highest median relative errors for PET 4-classes with respect to PET CTAC (RE 4-classes ): 6.91 %, 6.55 %, 5.90 %, and 6.56 % for ROI peak , ROI max , ROI mean , and MVP, respectively. On the contrary, K i images showed slightly reduced RE 4-classes (indirect 5.52 %, 5.95 %, 4.43 %, and 5.70 %, direct 6.61 %, 6.33 %, 5.53 %, and 4.96 %) for ROI peak , ROI max , ROI mean , and MVP, respectively. A higher TBR was observed on indirect and direct K i images relative to SUV, while direct K i images demonstrated the highest CNR. Conclusions: Four-tissue class AC may impact SUV and K i parameter estimation but only to a limited extent, thereby suggesting that WB Patlak K i imaging for dynamic WB PET/MRI studies is feasible. Patlak K i imaging can enhance TBR, thereby facilitating lesion segmentation and quantification. However, patient-specific A coeff for each tissue class should be used when possible to address the high inter...

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Integrated 18F-FDG PET/MRI in breast cancer: early prediction of response to neoadjuvant chemotherapy

Abstract: F-FDG PET/MRI can be used to predict non-pCR after the first cycle of NAC in patients with breast cancer and has the potential to improve sensitivity by the addition of MRI parameters to the PET parameters.

Cited by 45 publications

References 34 publications

PET/MRI in Breast Cancer

PET/MRI in Breast Cancer

Is the whole larger than the sum of the parts? Integrated PET/MRI as a tool for response prediction

Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI

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