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Correlation of histopathology and multi-modal magnetic resonance imaging in childhood osteosarcoma: Predicting tumor response to chemotherapy. PLoS One 2022; 17:e0259564. [PMID: 35157711 PMCID: PMC8843228 DOI: 10.1371/journal.pone.0259564] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 10/21/2021] [Indexed: 11/23/2022] Open
Abstract
Background Osteosarcoma, which is the most common malignant pediatric bone cancer, remains dependent on an imprecise systemic treatment largely unchanged in 30 years. In this study, we correlated histopathology with magnetic resonance imaging (MRI), used the correlation to extract MRI-specific features representative of tumor necrosis, and subsequently developed a novel classification model for predicting tumor response to neoadjuvant chemotherapy in pediatric patients with osteosarcoma using multi-modal MRI. The model could ultimately serve as a testable biomarker for a high-risk malignancy without successful precision treatments. Methods Patients with newly diagnosed high-grade appendicular osteosarcoma were enrolled in a single-center observational study, wherein patients underwent pre-surgical evaluation using both conventional MRI (post-contrast T1-weighted with fat saturation, pre-contrast T1-weighted, and short inversion-time inversion recovery (STIR)) and advanced MRI (diffusion weighted (DW) and dynamic contrast enhanced (DCE)). A classification model was established based on a direct correlation between histopathology and MRI, which was achieved through histologic-MR image co-registration and subsequent extraction of MR image features for identifying histologic tumor necrosis. By operating on the MR image features, tumor necrosis was estimated from different combinations of MR images using a multi-feature fuzzy clustering technique together with a weighted majority ruling. Tumor necrosis calculated from MR images, for either an MRI plane of interest or whole tumor volume, was compared to pathologist-estimated necrosis and necrosis quantified from digitized histologic section images using a previously described deep learning classification method. Results 15 patients were enrolled, of whom two withdrew, one became ineligible, and two were subjected to inadequate pre-surgical imaging. MRI sequences of n = 10 patients were subsequently used for classification model development. Different MR image features, depending on the modality of MRI, were shown to be significant in distinguishing necrosis from viable tumor. The scales at which MR image features optimally signified tumor necrosis were different as well depending on the MR image type. Conventional MRI was shown capable of differentiating necrosis from viable tumor with an accuracy averaging above 90%. Conventional MRI was equally effective as DWI in distinguishing necrotic from viable tumor regions. The accuracy of tumor necrosis prediction by conventional MRI improved to above 95% when DCE-MRI was added into consideration. Volume-based tumor necrosis estimations tended to be lower than those evaluated on an MRI plane of interest. Conclusions The study has shown a proof-of-principle model for interpreting chemotherapeutic response using multi-modal MRI for patients with high-grade osteosarcoma. The model will continue to be evaluated as MR image features indicative of tumor response are now computable for the disease prior to surgery.
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Yoshida S, Takahara T, Arita Y, Sakaino S, Katahira K, Fujii Y. Whole‐body diffusion‐weighted magnetic resonance imaging: Diagnosis and follow up of prostate cancer and beyond. Int J Urol 2021; 28:502-513. [DOI: 10.1111/iju.14497] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Soichiro Yoshida
- Department of Urology Tokyo Medical and Dental University TokyoJapan
| | - Taro Takahara
- Department of Biomedical Engineering Tokai University School of Engineering KanagawaJapan
- Department of Radiology Advanced Imaging Center, Yaesu Clinic TokyoJapan
| | - Yuki Arita
- Department of Radiology Keio University School of Medicine TokyoJapan
| | - Shinjiro Sakaino
- Department of Radiation Therapeutics Suzukake Central Hospital ShizuokaJapan
| | | | - Yasuhisa Fujii
- Department of Urology Tokyo Medical and Dental University TokyoJapan
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Fukuda T, Wengler K, de Carvalho R, Boonsri P, Schweitzer ME. MRI biomarkers in osseous tumors. J Magn Reson Imaging 2019; 50:702-718. [PMID: 30701624 DOI: 10.1002/jmri.26672] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 01/15/2019] [Accepted: 01/15/2019] [Indexed: 12/12/2022] Open
Abstract
Although radiography continues to play a critical role in osseous tumor assessment, there have been remarkable advances in cross-sectional imaging. MRI has taken a lead in this assessment due to high tissue contrast and spatial resolution, which are well suited for bone lesion assessment. More recently, although somewhat lagging other organ systems, quantitative parameters have shown promising potential as biomarkers for osseous tumors. Among these sequences are chemical shift imaging (CSI), apparent diffusion coefficient (ADC), and intravoxel incoherent motion (IVIM) from diffusion-weighted imaging (DWI), quantitative dynamic contrast enhanced (DCE)-MRI, and magnetic resonance spectroscopy (MRS). In this article, we review the background and recent roles of these quantitative MRI biomarkers for osseous tumors. Level of Evidence: 3 Technical Efficacy Stage: 3 J. MAGN. RESON. IMAGING 2019. J. Magn. Reson. Imaging 2019;50:702-718.
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Affiliation(s)
- Takeshi Fukuda
- Department of Radiology, Stony Brook University, Stony Brook, New York, USA
| | - Kenneth Wengler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
| | - Ruben de Carvalho
- Department of Radiology, Stony Brook University, Stony Brook, New York, USA
| | - Pattira Boonsri
- Department of Radiology, Stony Brook University, Stony Brook, New York, USA
| | - Mark E Schweitzer
- Department of Radiology, Stony Brook University, Stony Brook, New York, USA
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Reischauer C, Patzwahl R, Koh DM, Froehlich JM, Gutzeit A. Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases-A pilot study. Eur J Radiol 2018; 101:184-190. [PMID: 29571795 DOI: 10.1016/j.ejrad.2018.02.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/16/2018] [Accepted: 02/17/2018] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To evaluate whole-lesion volumetric texture analysis of apparent diffusion coefficient (ADC) maps for assessing treatment response in prostate cancer bone metastases. MATERIALS AND METHODS Texture analysis is performed in 12 treatment-naïve patients with 34 metastases before treatment and at one, two, and three months after the initiation of androgen deprivation therapy. Four first-order and 19 second-order statistical texture features are computed on the ADC maps in each lesion at every time point. Repeatability, inter-patient variability, and changes in the feature values under therapy are investigated. Spearman rank's correlation coefficients are calculated across time to demonstrate the relationship between the texture features and the serum prostate specific antigen (PSA) levels. RESULTS With few exceptions, the texture features exhibited moderate to high precision. At the same time, Friedman's tests revealed that all first-order and second-order statistical texture features changed significantly in response to therapy. Thereby, the majority of texture features showed significant changes in their values at all post-treatment time points relative to baseline. Bivariate analysis detected significant correlations between the great majority of texture features and the serum PSA levels. Thereby, three first-order and six second-order statistical features showed strong correlations with the serum PSA levels across time. CONCLUSION The findings in the present work indicate that whole-tumor volumetric texture analysis may be utilized for response assessment in prostate cancer bone metastases. The approach may be used as a complementary measure for treatment monitoring in conjunction with averaged ADC values.
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Affiliation(s)
- Carolin Reischauer
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland; Institute for Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland.
| | - René Patzwahl
- Department of Radiology, Cantonal Hospital Winterthur, Winterthur, Switzerland
| | - Dow-Mu Koh
- Academic Department of Radiology, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK; CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Sutton, Surrey, UK
| | - Johannes M Froehlich
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland
| | - Andreas Gutzeit
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland; Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland; Department of Radiology, Paracelsus Medical University Salzburg, Salzburg, Austria
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Reischauer C, Patzwahl R, Koh DM, Froehlich JM, Gutzeit A. Non-Mono-Exponential Analysis of Diffusion-Weighted Imaging for Treatment Monitoring in Prostate Cancer Bone Metastases. Sci Rep 2017; 7:5809. [PMID: 28724944 PMCID: PMC5517576 DOI: 10.1038/s41598-017-06246-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 06/27/2017] [Indexed: 01/14/2023] Open
Abstract
Diffusion-weighted imaging quantified using the mono-exponential model has shown great promise for monitoring treatment response in prostate cancer bone metastases. The aim of this prospective study is to evaluate whether non-mono-exponential diffusion models better describe the water diffusion properties and may improve treatment response assessment. Diffusion-weighted imaging data of 12 treatment-naïve patients with 34 metastases acquired before and at one, two, and three months after initiation of antiandrogen treatment are analysed using the mono-exponential, the intravoxel incoherent motion, the stretched exponential, and the statistical model. Repeatability of the fitted parameters and changes under therapy are quantified. Model preference is assessed and correlation coefficients across times are calculated to delineate the relationship between the prostate-specific antigen levels and the diffusion parameters as well as between the diffusion parameters within each model. There is a clear preference for non-mono-exponential diffusion models at all time points. Particularly the stretched exponential is favoured in approximately 60% of the lesions. Its parameters increase significantly in response to treatment and are highly repeatable. Thus, the stretched exponential may be utilized as a potential optimal model for monitoring treatment response. Compared with the mono-exponential model, it may provide complementary information on tissue properties and improve response assessment.
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Affiliation(s)
- Carolin Reischauer
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland.
- Institute for Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland.
| | - René Patzwahl
- Department of Radiology, Cantonal Hospital Winterthur, Winterthur, Switzerland
| | - Dow-Mu Koh
- Academic Department of Radiology, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Sutton, Surrey, UK
| | - Johannes M Froehlich
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland
| | - Andreas Gutzeit
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
- Department of Radiology, Paracelsus Medical University Salzburg, Salzburg, Austria
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Luker GD, Nguyen HM, Hoff BA, Galbán CJ, Hernando D, Chenevert TL, Talpaz M, Ross BD. A Pilot Study of Quantitative MRI Parametric Response Mapping of Bone Marrow Fat for Treatment Assessment in Myelofibrosis. ACTA ACUST UNITED AC 2016; 2:67-78. [PMID: 27213182 PMCID: PMC4872873 DOI: 10.18383/j.tom.2016.00115] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Myelofibrosis (MF) is a hematologic neoplasm arising as a primary disease or secondary to other myeloproliferative neoplasms (MPNs). Both primary and secondary MF are uniquely associated with progressive bone marrow fibrosis, displacing normal hematopoietic cells from the marrow space and disrupting normal production of mature blood cells. Activation of the JAK2 signaling pathway in hematopoietic stem cells commonly causes MF, and ruxolitinib, a drug targeting this pathway, is the treatment of choice for many patients. However, current measures of disease status in MF do not necessarily predict response to treatment with ruxolitinib or other drugs in MF. Bone marrow biopsies are invasive and prone to sampling error, while measurements of spleen volume only indirectly reflect bone marrow status. Toward the goal of developing an imaging biomarker for treatment response in MF, we present preliminary results from a prospective clinical study evaluating parametric response mapping (PRM) of quantitative Dixon MRI bone marrow fat fraction maps in four MF patients treated with ruxolitinib. PRM allows for the voxel-wise identification of significant change in quantitative imaging readouts over time, in this case the bone marrow fat content. We identified heterogeneous response patterns of bone marrow fat among patients and within different bone marrow sites in the same patient. We also observed discordance between changes in bone marrow fat fraction and reductions in spleen volume, the standard imaging metric for treatment efficacy. This study provides initial support for PRM analysis of quantitative MRI of bone marrow fat to monitor response to therapy in MF, setting the stage for larger studies to further develop and validate this method as a complementary imaging biomarker for this disease.
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Affiliation(s)
- Gary D Luker
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Huong Marie Nguyen
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Benjamin A Hoff
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Craig J Galbán
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Thomas L Chenevert
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Moshe Talpaz
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Brian D Ross
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
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