Evaluation of bone marrow cellularity by magnetic resonance imaging in patients with myelodysplastic syndrome

Depaoli, Lorella; Davini, Ottavio; Foggetti, M D; Ficara, Francesca; Priotto, C; Cirillo, R; Resegotti, L

doi:10.1111/j.1600-0609.1992.tb00039.x

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…55 Although the BM blast percentage, cytogenetics, and mutational analysis would also not be available, these limitations could eventually be overcome by obtaining PB genetic information, 56,57 by flow cytometry, 58 and also by medical imaging. 59,60 A recent study has demonstrated that specific morphologies may be associated with somatic mutations. 61 Perhaps, conversely, specific genetic signatures reflect corresponding morphologic changes.…”

Section: Discussionmentioning

confidence: 99%

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

et al. 2021

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We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

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Section: Discussionmentioning

confidence: 99%

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

et al. 2021

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“…MRI of bone marrow in patients with aplastic anaemia (AA) or myelodysplastic syndrome (MDS) has been reported previously (9)(10)(11)(12)(13). In this study, we examined the signal intensity (SI) patterns on STIR images in patients with AA and MDS (14), and describe follow-up STIR images during the course of those patients treated with recombinant human granulocyte colony stimulating factor (rhG-CSF), recombinant human erythropoietin (rhEPO) or other agents.…”

Section: Introductionmentioning

confidence: 99%

Bone marrow patterns in patients with aplastic anaemia and myelodysplastic syndrome: observations with magnetic resonance imaging

Kusumoto

Jinnai

Matsuda

et al. 1997

European J of Haematology

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Bone marrow magnetic resonance imaging (MRI) was obtained in 48 patients with myelodysplastic syndrome (MDS) (35 cases) or aplastic anaemia (AA) (13 cases). The lower thoracic and lumbar spine were evaluated on sagittal plane using a 1.5 Tesla superconducting MR unit with a surface coil. Pulse sequence of STIRs (TR 2000 msec, TI 160 msec, TE 20 msec) were applied. Four distinct patterns of signal intensity (SI) on the STIR images were classified as follows: pattern 1, homogeneously low SI; 2, marginally high SI; 3, heterogeneously high SI; 4, homogeneously high SI. In all 13 patients with AA, STIR images initially revealed pattern 1. In 25 of 35 cases with MDS patients, the STIR images were initially classified as pattern 3. The STIR images of 6 AA and 5 MDS patients with a clinical response to treatment showed pattern 2 similar to that of normal marrow distribution. The STIR images of MDS patients showed an abnormal distribution of SI. Significant signal changes in the STIR images can be observed in successive examinations of the patients, thus facilitating follow‐up of the disease and treatment. MRI of the bone marrow provides a noninvasive means of grossly examining a large fraction and is a useful technique in patients with aplastic anaemia or myelodysplastic syndrome.

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“…The subjective interpretation of BMC, despite the reported high interobserver agreement,33,34 as well as the changes in quality of the specimens and the focal nature of BM findings, may have limited the accuracy of BMC determination. This limitation will hopefully be addressed by accurate future technologies, such as MRI38 and 18F-PET 39. The translated qualitative BMC information to quantitative values (ie, 30%, 60%, 90%) is somewhat arbitrary, yet it is likely that these numbers, on average, signify a close proximity to the actual BMC.…”

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confidence: 99%

Can bone marrow cellularity help in predicting prognosis in myelodysplastic syndromes?

Joffe

Filanovsky

Oster

et al. 2018

European J of Haematology

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Evaluation of bone marrow cellularity by magnetic resonance imaging in patients with myelodysplastic syndrome

Cited by 13 publications

References 11 publications

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

Bone marrow patterns in patients with aplastic anaemia and myelodysplastic syndrome: observations with magnetic resonance imaging

Can bone marrow cellularity help in predicting prognosis in myelodysplastic syndromes?

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