Literature DB >> 32835853

Mid-term response assessment in multiple myeloma using a texture analysis approach on dual energy-CT-derived bone marrow images - A proof of principle study.

Christian Philipp Reinert1, Eva-Maria Krieg2, Hans Bösmüller3, Marius Horger2.   

Abstract

PURPOSE: To identify textural features on dual-energy CT (DECT)-generated virtual non calcium (VNC) bone marrow images in a small group of patients with multiple myeloma undergoing systemic treatment which could potentially help for mid-term response assessment.
METHODS: 44 patients (59.1 ± 11.2 yr.) with multiple myeloma who underwent unenhanced whole-body reduced-dose DECT before and after systemic therapy were evaluated. All patients had current hematologic laboratory tests including serum levels of immunoglobulins, albumin, and total proteins. Using DECT post-processing, bone marrow images of the axial skeleton were reconstructed. The vertebral bodies T10-L5 were segmented for quantification of 1st order (n = 18) and 2nd order Gray Level Co-occurrence Matrix (GLCM) textural features (n = 23) based on an open-source radiomics library (Pyradiomics), which were then compared with the hematologic response category to treatment. Five patients underwent only active surveillance at intervals after previous successful therapy.
RESULTS: According to hematologic diagnosis, 29 patients were classified as complete response (CR), 10 as partial response (PR) and 5 as stable disease (SD). We observed a significant drop of the 1st order textural features "10th percentile" (p = 0.009), "median" (p = 0.01), and "minimum" (p < 0.0001) after treatment, whereas the 1st order feature "range" (p = 0.0004) and the 2nd order GLCM feature "difference variance" (p = 0.007) significantly increased in patients experiencing CR. A similar trend, however, without statistical significance, could be observed in patients achieving PR after treatment. 2nd order GLCM feature "difference variance" proved to be a significant discriminator (p = 0.01) between patients with CR and PR (sensitivity 0.93, specificity 0.70) for a cut-off value of -0.28. In patients classified CR, both the mean serum protein and the beta-2 microglobulin decreased after treatment, whereas the serum albumin increased (p < 0.01). The same trend without significance could be observed in patients classified PR.
CONCLUSIONS: Changes in textural features applied on VNC bone marrow images in the pre- and posttreatment settings correlate well with myeloma-specific hematologic parameters and provide complementary information for the assessment of the late effects of treatment on the bone marrow.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Bone marrow; Dual-energy CT; Multiple myeloma; Proof of principle; Response assessment; Texture analysis

Mesh:

Substances:

Year:  2020        PMID: 32835853     DOI: 10.1016/j.ejrad.2020.109214

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  10 in total

1.  A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma.

Authors:  Jianfang Liu; Chunjie Wang; Wei Guo; Piaoe Zeng; Yan Liu; Ning Lang; Huishu Yuan
Journal:  Radiol Med       Date:  2021-06-22       Impact factor: 3.469

Review 2.  [Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data].

Authors:  Markus Wennmann; Jacob M Murray
Journal:  Radiologe       Date:  2021-12-10       Impact factor: 0.635

Review 3.  Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Authors:  Alberto Stefano Tagliafico; Alida Dominietto; Liliana Belgioia; Cristina Campi; Daniela Schenone; Michele Piana
Journal:  Medicina (Kaunas)       Date:  2021-01-21       Impact factor: 2.430

4.  Structured reporting of computed tomography in the staging of colon cancer: a Delphi consensus proposal.

Authors:  Vincenza Granata; Lorenzo Faggioni; Roberta Grassi; Roberta Fusco; Alfonso Reginelli; Daniela Rega; Nicola Maggialetti; Duccio Buccicardi; Barbara Frittoli; Marco Rengo; Chandra Bortolotto; Roberto Prost; Giorgia Viola Lacasella; Marco Montella; Eleonora Ciaghi; Francesco Bellifemine; Federica De Muzio; Giulia Grazzini; Massimo De Filippo; Salvatore Cappabianca; Andrea Laghi; Roberto Grassi; Luca Brunese; Emanuele Neri; Vittorio Miele; Francesca Coppola
Journal:  Radiol Med       Date:  2021-11-06       Impact factor: 3.469

5.  Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal.

Authors:  Vincenza Granata; Francesca Coppola; Roberta Grassi; Roberta Fusco; Salvatore Tafuto; Francesco Izzo; Alfonso Reginelli; Nicola Maggialetti; Duccio Buccicardi; Barbara Frittoli; Marco Rengo; Chandra Bortolotto; Roberto Prost; Giorgia Viola Lacasella; Marco Montella; Eleonora Ciaghi; Francesco Bellifemine; Federica De Muzio; Ginevra Danti; Giulia Grazzini; Massimo De Filippo; Salvatore Cappabianca; Carmelo Barresi; Franco Iafrate; Luca Pio Stoppino; Andrea Laghi; Roberto Grassi; Luca Brunese; Emanuele Neri; Vittorio Miele; Lorenzo Faggioni
Journal:  Front Endocrinol (Lausanne)       Date:  2021-11-30       Impact factor: 5.555

6.  Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study.

Authors:  Yi-Yang Liu; Huan Zhang; Lan Wang; Shu-Shen Lin; Hao Lu; He-Jun Liang; Pan Liang; Jun Li; Pei-Jie Lv; Jian-Bo Gao
Journal:  Front Oncol       Date:  2021-09-15       Impact factor: 6.244

7.  Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma.

Authors:  Catharina Silvia Lisson; Christoph Gerhard Lisson; Marc Fabian Mezger; Daniel Wolf; Stefan Andreas Schmidt; Wolfgang M Thaiss; Eugen Tausch; Ambros J Beer; Stephan Stilgenbauer; Meinrad Beer; Michael Goetz
Journal:  Cancers (Basel)       Date:  2022-04-15       Impact factor: 6.575

8.  Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer.

Authors:  Mengying Xu; Song Liu; Lin Li; Xiangmei Qiao; Changfeng Ji; Lingyu Tan; Zhengyang Zhou
Journal:  Sci Rep       Date:  2022-08-19       Impact factor: 4.996

9.  Radiomics Models Based on Magnetic Resonance Imaging for Prediction of the Response to Bortezomib-Based Therapy in Patients with Multiple Myeloma.

Authors:  Yang Li; Ping Yin; Yang Liu; Chuanxi Hao; Lei Chen; Chao Sun; Sicong Wang; Nan Hong
Journal:  Biomed Res Int       Date:  2022-09-05       Impact factor: 3.246

Review 10.  Imaging of treatment response and minimal residual disease in multiple myeloma: state of the art WB-MRI and PET/CT.

Authors:  Frederic E Lecouvet; Marie-Christiane Vekemans; Thomas Van Den Berghe; Koenraad Verstraete; Thomas Kirchgesner; Souad Acid; Jacques Malghem; Joris Wuts; Jens Hillengass; Vincent Vandecaveye; François Jamar; Olivier Gheysens; Bruno C Vande Berg
Journal:  Skeletal Radiol       Date:  2021-08-07       Impact factor: 2.199

  10 in total

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