Multiples Myelom: Aktuelle Empfehlungen für die Bildgebung

Hillengaß, Jens; Delorme, Stefan

doi:10.1007/s00117-011-2257-0

Cited by 6 publications

(2 citation statements)

References 30 publications

(24 reference statements)

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“…Since low dose WBLDCT is a standardized staging examination for newly diagnosed MM patients, no additional examination needs to be performed for adipose tissue measurements and thus does not display additional exposure to radiation exclusively for these measurements. [ 38 , 39 ] For daily clinical practice, even the measurement of one slice, preferably at the level of L3/L4, is sufficient for valid information. [ 40 ]…”

Section: Discussionmentioning

confidence: 99%

Body fat composition as predictive factor for treatment response in patients with newly diagnosed multiple myeloma - subgroup analysis of the prospective GMMG MM5 trial

et al. 2017

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Introduction/BackgroundObesity is a well-known risk factor for malignant tumors and increased body mass index (BMI) is correlated to the risk of developing multiple myeloma (MM). The correlation of body fat composition with disease activity, adverse events and treatment response of MM patients has not been investigated yet.Patients and MethodsA subgroup of 108 patients from a single institution enrolled in the prospective GMMG-MM5 trial, who received a whole-body low-dose computed tomography (WBLDCT) before induction therapy, were included in this study. Body fat composition was measured in WBLDCT for each patient, divided in the compartments abdomen, pelvis, thigh and further categorized in subcutaneous (SAT) and visceral adipose tissue (VAT). The correlation of these parameters with disease activity (M protein, plasma cell count, LDH, CRAB-criteria), adverse cytogenetics, adverse events and treatment response were evaluated.ResultsSignificant reciprocal correlation was found between adverse cytogenetics and VAT of the abdomen and pelvis, respectively (gain 1q21: p=0.009 and p=0.021; t(4;14): p=0.038 and p=0.042). No correlation of VAT or SAT with adverse events was observed. Significant reciprocal correlation was observed between abdominal (p=0.03) and pelvic (p=0.035) VAT and treatment response. Abdominal VAT remains significant (p=0.034) independently of revised ISS stage and treatment. The BMI did not show a significant correlation with treatment response or investigated cytogenetics.ConclusionBased on the clinically relevant difference in treatment outcome depending on VAT and SAT, excessive body fat of abdomen and pelvis might be a predictive factor for poor treatment response. Further influences in this context should be considered as well, e.g. chemotherapy dosing and body fat metabolism. Further studies are necessary to investigate this hypothesis.

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Section: Discussionmentioning

confidence: 99%

Body fat composition as predictive factor for treatment response in patients with newly diagnosed multiple myeloma - subgroup analysis of the prospective GMMG MM5 trial

et al. 2017

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“…Cancellous bone and bone marrow are less dense, with HU being more similar to those of soft tissue like muscles. Pathological changes in the bone, e.g., caused by multiple myeloma can influence the density and therefore the HU of bone tissue [3].…”

Section: Introductionmentioning

confidence: 99%

Towards Whole-body CT Bone Segmentation

Klein

Warszawski

Hillengaß

et al. 2018

Bildverarbeitung Für Die Medizin 2018

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Bone segmentation from CT images is a task that has been worked on for decades. It is an important ingredient to several diagnostics or treatment planning approaches and relevant to various diseases. As high-quality manual and semi-automatic bone segmentation is very time-consuming, a reliable and fully automatic approach would be of great interest in many scenarios. In this publication, we propose a U-Net inspired architecture to address the task using Deep Learning. We evaluated the approach on whole-body CT scans of patients suffering from multiple myeloma. As the disease decomposes the bone, an accurate segmentation is of utmost importance for the evaluation of bone density, disease staging and localization of focal lesions. The method was evaluated on an in-house data-set of 6000 2D image slices taken from 15 whole-body CT scans, achieving a dice score of 0.96 and an IOU of 0.94.

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Skelettveränderungen bei hämatologischen Systemerkrankungen

Freyschmidt¹

2016

Skeletterkrankungen

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Multiples Myelom: Aktuelle Empfehlungen für die Bildgebung

Cited by 6 publications

References 30 publications

Body fat composition as predictive factor for treatment response in patients with newly diagnosed multiple myeloma - subgroup analysis of the prospective GMMG MM5 trial

Body fat composition as predictive factor for treatment response in patients with newly diagnosed multiple myeloma - subgroup analysis of the prospective GMMG MM5 trial

Towards Whole-body CT Bone Segmentation

Skelettveränderungen bei hämatologischen Systemerkrankungen

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