Semi-quantitative lymph node assessment of 18F-FDG PET/CT in locally advanced breast cancer: correlation with biological prognostic factors

Vicente, Ana María García; Castrejón, Á. Soriano; Mora, M.A. Cruz; Ageitos, Ana; Sánchez, María del Carmen; Martín, A. León; Aunión, Ruth Espinosa; Calatayud, Fernanda Relea; Madero, Vicente Muñoz; López-Muñíz, Ignacio Chacón; García, J.M. Cordero; Londoño, G.A. Jiménez

doi:10.1007/s00259-012-2244-3

Cited by 21 publications

(16 citation statements)

References 23 publications

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“…Deformations of the specimen relative to the in vivo geometry of the prostate are often non-uniform. Bending and warping energies are particularly high at the lateral peripheral zones of the prostate [ 22 ], which increases registration uncertainties at these locations. While registration errors of each registration step may add up, it is unlikely that they are always to the disadvantage of the lesion representation on in vivo T2W MRI used for tumor segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, when determining the registration errors of BPH nodules from in vivo over ex vivo MRI to histology, the HD and MDA of the final registration (in vivo MRI to histology) increased but was far less than the sum of the individual registration errors. Registration errors due to dehydration of internal prostate tissue components during fixation are unavoidable and will cause shrinkage of the prostate specimen compared to the prostate in the human body [ 22 ]. Schned et al estimated that the in vivo tumor volume was on average 12.4% higher than that measured from prostatectomy pathology [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…The purpose of this study was to determine the precision of tumor boundary detection (segmentation) by visual inspection of mpMRI and by semi-automatic segmentation based on 18 F-choline PET in patients undergoing prostatectomy. Due to prostate deformations, differing slice thicknesses between imaging and histology, and differing spatial orientation between imaging and whole-mount pathology, registration of medical imaging to pathology from prostatectomy specimen is challenging [ 22 ]. To overcome these issues, we first accurately registered in vivo imaging to the prostatectomy specimen using a multi-step approach presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of tumor segmentation from multi-parametric prostate MRI and 18F-choline PET/CT for focal prostate cancer therapy applications

et al. 2018

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BackgroundThe study aims to assess the accuracy of multi-parametric prostate MRI (mpMRI) and 18F-choline PET/CT in tumor segmentation for clinically significant prostate cancer. 18F-choline PET/CT and 3 T mpMRI were performed in 10 prospective subjects prior to prostatectomy. All subjects had a single biopsy-confirmed focus of Gleason ≥ 3+4 cancer. Two radiologists (readers 1 and 2) determined tumor boundaries based on in vivo mpMRI sequences, with clinical and pathologic data available. 18F-choline PET data were co-registered to T2-weighted 3D sequences and a semi-automatic segmentation routine was used to define tumor volumes. Registration of whole-mount surgical pathology to in vivo imaging was conducted utilizing two ex vivo prostate specimen MRIs, followed by gross sectioning of the specimens within a custom-made 3D-printed plastic mold. Overlap and similarity coefficients of manual segmentations (seg1, seg2) and 18F-choline-based segmented lesions (seg3) were compared to the pathologic reference standard.ResultsAll segmentation methods greatly underestimated the true tumor volumes. Human readers (seg1, seg2) and the PET-based segmentation (seg3) underestimated an average of 79, 80, and 58% of the tumor volumes, respectively. Combining segmentation volumes (union of seg1, seg2, seg3 = seg4) decreased the mean underestimated tumor volume to 42% of the true tumor volume. When using the combined segmentation with 5 mm contour expansion, the mean underestimated tumor volume was significantly reduced to 0.03 ± 0.05 mL (2.04 ± 2.84%). Substantial safety margins up to 11–15 mm were needed to include all tumors when the initial segmentation boundaries were drawn by human readers or the semi-automated 18F-choline segmentation tool. Combining MR-based human segmentations with the metabolic information based on 18F-choline PET reduced the necessary safety margin to a maximum of 9 mm to cover all tumors entirely.ConclusionsTo improve the outcome of focal therapies for significant prostate cancer, it is imperative to recognize the full extent of the underestimation of tumor volumes by mpMRI. Combining metabolic information from 18F-choline with MRI-based segmentation can improve tumor coverage. However, this approach requires confirmation in further clinical studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of tumor segmentation from multi-parametric prostate MRI and 18F-choline PET/CT for focal prostate cancer therapy applications

et al. 2018

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“…Meyer et al [139] reviewed the registration methods of 3-D medical images and histopathology of the prostate. They examined the registration process and techniques for registering MRI or PET with whole-mounted prostatectomy specimens.…”

Section: Validationmentioning

confidence: 99%

Computer-aided Detection of Prostate Cancer with MRI

et al. 2016

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One in six men will develop prostate cancer in his life time. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multi-parametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and MR spectroscopy imaging. Due to the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In order to improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

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“…154 Asynchronous postprocessing can present some difficulties which arise from the positioning of the same samples between different imaging modality scans at different times, which could cause difficulties in coregistering images for analysis. 155 Co-registration is especially difficult if data acquisitions are not acquired at the same spatial resolutions; however, advances in computational annotation help to improve image analysis. 156 Often, voxels from the lower resolution modality are combined to form the voxel size of the higher spatial resolution.…”

Section: Multimodal Imaging Systemsmentioning

confidence: 99%

Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights

et al. 2017

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Semi-quantitative lymph node assessment of 18F-FDG PET/CT in locally advanced breast cancer: correlation with biological prognostic factors

Abstract: Biological factors of bad prognosis were correlated with higher semi-quantitative metabolic values in lymph nodes. Therefore these results appear to reveal biological significance of lymph node (18)F-FDG accumulation.

Cited by 21 publications

References 23 publications

Accuracy of tumor segmentation from multi-parametric prostate MRI and 18F-choline PET/CT for focal prostate cancer therapy applications

Accuracy of tumor segmentation from multi-parametric prostate MRI and 18F-choline PET/CT for focal prostate cancer therapy applications

Computer-aided Detection of Prostate Cancer with MRI

Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights

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