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CC BY-NC-ND 4.0 · World J Nucl Med 2019; 18(04): 345-350
DOI: 10.4103/wjnm.WJNM_119_18
Review Article

Advanced modalities of molecular imaging in precision medicine for musculoskeletal malignancies

Narges Jokar
The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr
,
Erik Velez
1   Department of Diagnostic Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
,
Hossein Shooli
The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr
,
Habibollah Dadgar
2   Cancer Research Center, RAZAVI Hospital, Imam Reza International University, Mashhad
,
Seyed Abbas Sadathosseini
3   Department of Medical Ethics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
,
Majid Assadi
4   Department of Molecular Imaging and Radionuclide Therapy (MIRT), The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr Medical University Hospital, Bushehr University of Medical Sciences, Bushehr
,
Ali Gholamrezanezhad
1   Department of Diagnostic Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Abstract

Musculoskeletal malignancies consist of a heterogenous group of mesenchymal tumors, often with high inter- and intratumoral heterogeneity. The early and accurate diagnosis of these malignancies can have a substantial impact on optimal treatment and quality of life for these patients. Several new applications and techniques have emerged in molecular imaging, including advances in multimodality imaging, the development of novel radiotracers, and advances in image analysis with radiomics and artificial intelligence. This review highlights the recent advances in molecular imaging modalities and the role of non-invasive imaging in evaluating tumor biology in the era of precision medicine.

Keywords

Artificial intelligence - heterogeneity - molecular imaging - musculoskeletal - precision medicine - radiomics

Financial support and sponsorship

Nil.




Publication History

Received: 25 December 2018

Accepted: 18 May 2019

Article published online:
22 April 2022

© 2019. Sociedade Brasileira de Neurocirurgia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Forouzanfar MH, Afshin A, Alexander LT, Anderson HR, Bhutta ZA, Biryukov S, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the global burden of disease study 2015. Lancet 2016;388:1659-724.
  • 2 Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: A systematic review. PLoS One 2014;9:e110300.
  • 3 Thakur ML. Genomic biomarkers for molecular imaging: Predicting the future. In: Seminars in nuclear medicine WB Saunders; 2009. p. 236-46.
  • 4 Sutherland KD, Visvader JE. Cellular mechanisms underlying intertumoral heterogeneity. Trends Cancer 2015;1:15-23.
  • 5 Gatenby RA, Grove O, Gillies RJ. Quantitative imaging in cancer evolution and ecology. Radiology 2013;269:8-15.
  • 6 Chowdhury R, Ganeshan B, Irshad S, Lawler K, Eisenblätter M, Milewicz H, et al. The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis. Br J Radiol 2014;87:20140065.
  • 7 Cormier JN, Pollock RE. Soft tissue sarcomas. CA Cancer J Clin 2004;54:94-109.
  • 8 Uchida A, Seto M, Hashimoto N, Araki N. Molecular diagnosis and gene therapy in musculoskeletal tumors. J Orthop Sci 2000;5:418-23.
  • 9 Mankoff DA. A definition of molecular imaging. J Nucl Med 2007;48:18N, 21N.
  • 10 Ghasemi M, Nabipour I, Omrani A, Alipour Z, Assadi M. Precision medicine and molecular imaging: New targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging 2016;6:310-27.
  • 11 Blasberg RG, Tjuvajev JG. Molecular-genetic imaging: Current and future perspectives. J Clin Invest 2003;111:1620-9.
  • 12 Pysz MA, Gambhir SS, Willmann JK. Molecular imaging: Current status and emerging strategies. Clin Radiol 2010;65:500-16.
  • 13 James ML, Gambhir SS. A molecular imaging primer: Modalities, imaging agents, and applications. Physiol Rev 2012;92:897-965.
  • 14 Cai W, Olafsen T, Zhang X, Cao Q, Gambhir SS, Williams LE, et al. PET imaging of colorectal cancer in xenograft-bearing mice by use of an 18F-labeled T84.66 anti-carcinoembryonic antigen diabody. J Nucl Med 2007;48:304-10.
  • 15 Liu K, Wang MW, Lin WY, Phung DL, Girgis MD, Wu AM, et al. Molecular imaging probe development using microfluidics. Curr Org Synth 2011;8:473-87.
  • 16 Catalano OA, Nicolai E, Rosen BR, Luongo A, Catalano M, Iannace C, et al. Comparison of CE-FDG-PET/CT with CE-FDG-PET/MR in the evaluation of osseous metastases in breast cancer patients. Br J Cancer 2015;112:1452-60.
  • 17 Sudoł-Szopińska I, Cwikła JB. Current imaging techniques in rheumatology: MRI, scintigraphy and PET. Pol J Radiol 2013;78:48-56.
  • 18 Palestro CJ. Radionuclide imaging of musculoskeletal infection: A review. J Nucl Med 2016;57:1406-12.
  • 19 Brown ML, O'Connor MK, Hung JC, Hayostek RJ. Technical aspects of bone scintigraphy. Radiol Clin North Am 1993;31:721-30.
  • 20 Palestro CJ. The current role of gallium imaging in infection. In: Seminars in Nuclear Medicine. WB Saunders: Elsevier; 1994.
  • 21 Saha S, Burke C, Desai A, Vijayanathan S, Gnanasegaran G. SPECT-CT: Applications in musculoskeletal radiology. Br J Radiol 2013;86:20120519.
  • 22 Buck AK, Nekolla S, Ziegler S, Beer A, Krause BJ, Herrmann K, et al. SPECT/CT. J Nucl Med 2008;49:1305-19.
  • 23 Hasegawa BH, Wong KH, Iwata K, Barber WC, Hwang AB, Sakdinawat AE, et al. Dual-modality imaging of cancer with SPECT/CT. Technol Cancer Res Treat 2002;1:449-58.
  • 24 Bybel B, Brunken RC, DiFilippo FP, Neumann DR, Wu G, Cerqueira MD, et al. SPECT/CT imaging: Clinical utility of an emerging technology. Radiographics 2008;28:1097-113.
  • 25 Lu SJ, Ul Hassan F, Vijayanathan S, Gnanasegaran G. Radionuclide bone SPECT/CT in the evaluation of knee pain: Comparing two-phase bone scintigraphy, SPECT and SPECT/CT. Br J Radiol 2018;91:20180168.
  • 26 Palestro CJ, Love C, Schneider R. The evolution of nuclear medicine and the musculoskeletal system. Radiol Clin North Am 2009;47:505-32.
  • 27 Pachowicz M, Staśkiewicz G, Florek K, Chrapko BE. The usefulness of SPECT/CT in characterization of skeletal and soft tissue lesions – Report of two cases. Nucl Med Rev Cent East Eur 2014;17:29-34.
  • 28 Upadhyay B, Mo J, Beadsmoore C, Marshall T, Toms A, Buscombe J. Technetium-99m methylene diphosphonate single-photon emission computed tomography/computed tomography of the foot and ankle. World J Nucl Med 2017;16:88-100.
  • 29 Aboagye EO, Kraeber-Bodéré F. Highlights lecture EANM 2016: “Embracing molecular imaging and multi-modal imaging: A smart move for nuclear medicine towards personalized medicine”. Eur J Nucl Med Mol Imaging 2017;44:1559-74.
  • 30 Jadvar, H, Velez E, Desai B, Ji L, Colletti PM, Quinn DI. Prediction of time to hormonal treatment failure in metastatic castrate-sensitive prostate cancer with (18)F-FDG PET/CT. J Nucl Med 2019.
  • 31 Piperkova E, Mikhaeil M, Mousavi A, Libes R, Viejo-Rullan F, Lin H, et al. Impact of PET and CT in PET/CT studies for staging and evaluating treatment response in bone and soft tissue sarcomas. Clin Nucl Med 2009;34:146-50.
  • 32 Feldman F, van Heertum R, Manos C. 18FDG PET scanning of benign and malignant musculoskeletal lesions. Skeletal Radiol 2003;32:201-8.
  • 33 Hawkins DS, Rajendran JG, Conrad EU 3rd, Bruckner JD, Eary JF. Evaluation of chemotherapy response in pediatric bone sarcomas by [F-18]-fluorodeoxy-D-glucose positron emission tomography. Cancer 2002;94:3277-84.
  • 34 Hawkins DS, Conrad EU 3rd, Butrynski JE, Schuetze SM, Eary JF. [F-18]-fluorodeoxy-D-glucose-positron emission tomography response is associated with outcome for extremity osteosarcoma in children and young adults. Cancer 2009;115:3519-25.
  • 35 Treglia G, Salsano M, Stefanelli A, Mattoli MV, Giordano A, Bonomo L, et al. Diagnostic accuracy of18 F-FDG-PET and PET/CT in patients with ewing sarcoma family tumours: A systematic review and a meta-analysis. Skeletal Radiol 2012;41:249-56.
  • 36 Basu S, Kwee TC, Gatenby R, Saboury B, Torigian DA, Alavi A, et al. Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders. Eur J Nucl Med Mol Imaging 2011;38:987-91.
  • 37 O'Sullivan PJ, Rohren EM, Madewell JE. Positron emission tomography-CT imaging in guiding musculoskeletal biopsy. Radiol Clin North Am 2008;46:475-86, v.
  • 38 Thomas L, Balmus C, Ahmadzadehfar H, Essler M, Strunk H, Bundschuh RA, et al. Assessment of bone metastases in patients with prostate cancer-A comparison between 99mTc-bone-scintigraphy and [68Ga]Ga-PSMA PET/CT. Pharmaceuticals (Basel) 2017;10. pii: E68.
  • 39 Hongtao L, Hui Z, Bingshun W, Xiaojin W, Zhiyu W, Shuier Z, et al. 18F-FDG positron emission tomography for the assessment of histological response to neoadjuvant chemotherapy in osteosarcomas: A meta-analysis. Surg Oncol 2012;21:e165-70.
  • 40 Benz MR, Evilevitch V, Allen-Auerbach MS, Eilber FC, Phelps ME, Czernin J, et al. Treatment monitoring by 18F-FDG PET/CT in patients with sarcomas: Interobserver variability of quantitative parameters in treatment-induced changes in histopathologically responding and nonresponding tumors. J Nucl Med 2008;49:1038-46.
  • 41 Bischoff M, Bischoff G, Buck A, von Baer A, Pauls S, Scheffold F, et al. Integrated FDG-PET-CT: Its role in the assessment of bone and soft tissue tumors. Arch Orthop Trauma Surg 2010;130:819-27.
  • 42 Mahajan A, Azad GK, Cook GJ. PET imaging of skeletal metastases and its role in personalizing further management. PET Clin 2016;11:305-18.
  • 43 van Kruchten M, Glaudemans AW, de Vries EF, Beets-Tan RG, Schröder CP, Dierckx RA, et al. PET imaging of estrogen receptors as a diagnostic tool for breast cancer patients presenting with a clinical dilemma. J Nucl Med 2012;53:182-90.
  • 44 Kandathil A, Subramaniam RM. PET/Computed tomography and precision medicine: Musculoskeletal sarcoma. PET Clin 2017;12:475-88.
  • 45 Iagaru A, Mittra E, Yaghoubi SS, Dick DW, Quon A, Goris ML, et al. Novel strategy for a cocktail 18F-fluoride and 18F-FDG PET/CT scan for evaluation of malignancy: Results of the pilot-phase study. J Nucl Med 2009;50:501-5.
  • 46 Samarin A, Burger C, Wollenweber SD, Crook DW, Burger IA, Schmid DT, et al. PET/MR imaging of bone lesions – Implications for PET quantification from imperfect attenuation correction. Eur J Nucl Med Mol Imaging 2012;39:1154-60.
  • 47 Eiber M, Takei T, Souvatzoglou M, Mayerhoefer ME, Fürst S, Gaertner FC, et al. Performance of whole-body integrated 18F-FDG PET/MR in comparison to PET/CT for evaluation of malignant bone lesions. J Nucl Med 2014;55:191-7.
  • 48 Kogan F, Fan AP, Gold GE. Potential of PET-MRI for imaging of non-oncologic musculoskeletal disease. Quant Imaging Med Surg 2016;6:756-71.
  • 49 Lambin P, van Stiphout RG, Starmans MH, Rios-Velazquez E, Nalbantov G, Aerts HJ, et al. Predicting outcomes in radiation oncology – Multifactorial decision support systems. Nat Rev Clin Oncol 2013;10:27-40.
  • 50 Orndal C, Rydholm A, Willén H, Mitelman F, Mandahl N. Cytogenetic intratumor heterogeneity in soft tissue tumors. Cancer Genet Cytogenet 1994;78:127-37.
  • 51 Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, et al. Unravelling tumour heterogeneity using next-generation imaging: Radiomics, radiogenomics, and habitat imaging. Clin Radiol 2017;72:3-10.
  • 52 Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-6.
  • 53 Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med 2017;38:122-39.
  • 54 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016;278:563-77.
  • 55 van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104-7.
  • 56 Choi ER, Lee HY, Jeong JY, Choi YL, Kim J, Bae J, et al. Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma. Oncotarget 2016;7:67302-13.
  • 57 Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.
  • 58 Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 2015;60:5471-96.
  • 59 Corino VD, Montin E, Messina A, Casali PG, Gronchi A, Marchianò A, et al. Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging 2018;47:829-40.
  • 60 Beam AL, Kohane IS. Translating artificial intelligence into clinical care. JAMA 2016;316:2368-9.
  • 61 Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA 2016;315:551-2.
  • 62 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2017;2:230-43.
  • 63 Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011;306:848-55.
  • 64 Crown WH. Potential application of machine learning in health outcomes research and some statistical cautions. Value Health 2015;18:137-40.