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Editorial

What Is the Role of Imaging in Cancers?

1
Nuclear Medicine Unit, Department of Medicine (DIMED), University of Padua, 35128 Padua, Italy
2
Department of Nuclear Medicine, Sant’Orsola-Malpighi Hospital, University of Bologna, 40138 Bologna, Italy
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(6), 1494; https://doi.org/10.3390/cancers12061494
Submission received: 3 June 2020 / Accepted: 4 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Role of Medical Imaging in Cancers)
In the issue entitled “Role of Medical Imaging in Cancers”, 33 papers have been collected (23 original articles, 8 reviews, 1 brief report and 1 perspective). All the papers focus on different topics, mainly on the role of positron emission tomography (PET) imaging in the management of oncological patients.
Table 1 shows a summary of the topics and the papers included for each topic [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]. The majority of papers are focused on prostate cancer (PCa) and radiomics. The first topic has continuously gained interest in last years, in particular after the introduction of prostate specific membrane antigen (PSMA)-based radiopharmaceuticals, as extensively reported by Hoffmann et al. [18] in a cohort of more than 580 patients with recurrent PCa, showing that, after radical prostatectomy, PSMA PET/computed tomography (CT) was able to detect the presence of recurrent disease in more than 50% of patients with a PSA level < 1.24 ng/mL. Moreover, Treglia et al. [24], in a meta-analysis, clearly demonstrated the high detection rate of 18F-labeled PSMA also in patients with a PSA levels < 0.5 ng/mL. Nevertheless, Fanti et al. [20] underlined that, although there is a large amount of data on PSMA PET in PCa, the diagnostic procedures are still underutilized in clinical practice. The role of imaging in PCa was also analyzed by using different radiopharmaceutical agents and specific software. Laudicella et al. [23] tested the utility of 18F-FACBC (or fluciclovine) in recurrent PCa, showing a high diagnostic performance for the detection of local recurrence, mainly in the prostatic fossa. Bauckneht et al. [19] support the role of 18F-Fluorodeoxyglucose (FDG) PET/CT as a tool for patient selection and response assessment in metastatic castrate resistant PCa patients undergoing 223Ra administration. Furthermore, in this latter setting of patients, a segmentation-based tumor load at 99mTc-dysphonate SPECT/CT was linked with clinical outcome [21]. Finally, a radiomic approach with a specific magnetic resonance imaging (MRI) protocol can be useful to appropriately detect and characterize PCa [31]. Radiomics is an emerging field, defined as the extraction of quantitative data from medical images by using specific software. It can be applied to all medical imaging, such as CT, MRI, PET/CT or PET/MRI. In the last years, a large amount of data has been published, such as in the current issue of Cancers, in order to apply this in diverse settings of disease. Castaldo et al. [30] and Schiano et al. [32] evaluated the role of radiomics in breast cancer. The first group of authors concluded the ability of radiomic features by MRI to discriminate major breast cancer molecular subtypes, thus potentially guiding a personalized treatment [30]. The second group by Schiano et al. [32] showed that the combination of radiomic features by FDG PET/MRI and molecular data are able to predict the synchronous metastatic disease more accurately than a single information. Fujima et al. [33] found a correlation between the clinical outcome and machine-learning algorithm using various MRI-derived data in patients with sinonasal squamous cell carcinomas.
Indeed, radiomics can be used as a tool for the prediction of outcomes in terms of response to therapy or prognosis in patients undergoing immune check point inhibitors, as stated by Polverari et al. [29]. The use of imaging as a predictive biomarker of response to immunotherapy has been discussed by some authors [5,6,7], in the present issue. Both in the reviews and in the original articles, nuclear medicine images can be considered as a non-invasive method that is able to predict the response to immunotherapy, mainly in combination with other clinical or biological markers. Moreover, FDG PET/CT can be helpful in predicting the response to therapy in patients with head and neck squamous cell carcinoma, due to the high correlation between micro-vessel density and FDG uptake in terms of SUVmax [28]. This latter semiquantitative parameter is predictive of overall survival in patients with recurrent ovarian cancer [25]. The same author [27] reported that PET/CT can predict more accurately than MRI the response to concurrent chemoradiation treatment in T2 cervical cancer patients. Finally, from a German experience in a small cohort of patients (n = 16) affected by high-risk soft tissue sarcoma, the authors of [26] found that FDG kinetic analysis can be considered as a marker of response to pazopanib in soft-tissue sarcoma, and therefore it could guide anti-angiogenetic therapy.
The residual papers focused on different topics by analyzing diverse imaging modalities from preclinical [17], to gold nanoparticles for CT imaging [3,4], to specific pathologies (i.e., breast cancer [1,2] or pancreas [13,14] or meningioma [10]), to multicenter trials [8,22]. The common conclusion of these contributions is the current and future support that medical imaging can add to clinical information. The continuous developments of new radiopharmaceutical agents or the production of new technologies (i.e., scanners or software) will lead to increasingly personalized medicine.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Salvatore, B.; Caprio, M.G.; Hill, B.S.; Sarnella, A.; Roviello, G.N.; Zannetti, A. Recent Advances in Nuclear Imaging of Receptor Expression to Guide Targeted Therapies in Breast Cancer. Cancers 2019, 11, 1614. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Hildebrandt, M.G.; Lauridsen, J.F.; Vogsen, M.; Holm, J.; Vilstrup, M.H.; Braad, P.-E.; Gerke, O.; Thomassen, M.; Ewertz, M.; Høilund-Carlsen, P.F.; et al. FDG-PET/CT for Response Monitoring in Metastatic Breast Cancer: Today, Tomorrow, and Beyond. Cancers 2019, 11, 1190. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Kimm, M.A.; Shevtsov, M.; Werner, C.; Sievert, W.; Zhiyuan, W.; Schoppe, O.; Menze, B.H.; Rummeny, E.J.; Proksa, R.; Bystrova, O.; et al. Gold Nanoparticle Mediated Multi-Modal CT Imaging of Hsp70 Membrane-Positive Tumors. Cancers 2020, 12, 1331. [Google Scholar] [CrossRef]
  4. Lee, J.W.; Kim, S.Y.; Lee, H.J.; Han, S.W.; Lee, J.E.; Lee, S.M. Prognostic Significance of CT-Attenuation of Tumor-Adjacent Breast Adipose Tissue in Breast Cancer Patients with Surgical Resection. Cancers 2019, 11, 1135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Castello, A.; Carbone, F.G.; Rossi, S.; Monterisi, S.; Federico, D.; Toschi, L.; Lopci, E. Circulating Tumor Cells and Metabolic Parameters in NSCLC Patients Treated with Checkpoint Inhibitors. Cancers 2020, 12, 487. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Frega, S.; Dal Maso, A.; Pasello, G.; Cuppari, L.; Bonanno, L.; Conte, P.; Evangelista, L. Novel Nuclear Medicine Imaging Applications in Immuno-Oncology. Cancers 2020, 12, 1303. [Google Scholar] [CrossRef] [PubMed]
  7. Decazes, P.; Bohn, P. Immunotherapy by Immune Checkpoint Inhibitors and Nuclear Medicine Imaging: Current and Future Applications. Cancers 2020, 12, 371. [Google Scholar] [CrossRef] [Green Version]
  8. Albano, D.; Laudicella, R.; Ferro, P.; Allocca, M.; Abenavoli, E.; Buschiazzo, A.; Castellino, A.; Chiaravalloti, A.; Cuccaro, A.; Cuppari, L.; et al. The Role of 18F-FDG PET/CT in Staging and Prognostication of Mantle Cell Lymphoma: An Italian Multicentric Study. Cancers 2019, 11, 1831. [Google Scholar] [CrossRef] [Green Version]
  9. Voltin, C.-A.; Mettler, J.; Grosse, J.; Dietlein, M.; Baues, C.; Schmitz, C.; Borchmann, P.; Kobe, C.; Hellwig, D. FDG-PET Imaging for Hodgkin and Diffuse Large B-Cell Lymphoma—An Updated Overview. Cancers 2020, 12, 601. [Google Scholar] [CrossRef] [Green Version]
  10. Laudicella, R.; Albano, D.; Annunziata, S.; Calabrò, D.; Argiroffi, G.; Abenavoli, E.; Linguanti, F.; Albano, D.; Vento, A.; Bruno, A.; et al. Theragnostic Use of Radiolabelled Dota-Peptides in Meningioma: From Clinical Demand to Future Applications. Cancers 2019, 11, 1412. [Google Scholar] [CrossRef] [Green Version]
  11. Jin, Y.; Randall, J.W.; Elhalawani, H.; Al Feghali, K.A.; Elliott, A.M.; Anderson, B.M.; Lacerda, L.; Tran, B.L.; Mohamed, A.S.; Brock, K.K.; et al. Detection of Glioblastoma Subclinical Recurrence Using Serial Diffusion Tensor Imaging. Cancers 2020, 12, 568. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Usuda, K.; Iwai, S.; Funasaki, A.; Sekimura, A.; Motono, N.; Matoba, M.; Doai, M.; Yamada, S.; Ueda, Y.; Uramoto, H. Diffusion-Weighted Imaging Can Differentiate between Malignant and Benign Pleural Diseases. Cancers 2019, 11, 811. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Serafini, S.; Sperti, C.; Brazzale, A.R.; Cecchin, D.; Zucchetta, P.; Pierobon, E.S.; Ponzoni, A.; Valmasoni, M.; Moletta, L. The Role of Positron Emission Tomography in Clinical Management of Intraductal Papillary Mucinous Neoplasms of the Pancreas. Cancers 2020, 12, 807. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Montemagno, C.; Cassim, S.; Trichanh, D.; Savary, C.; Pouyssegur, J.; Pagès, G.; Fagret, D.; Broisat, A.; Ghezzi, C. 99mTc-A1 as a Novel Imaging Agent Targeting Mesothelin-Expressing Pancreatic Ductal Adenocarcinoma. Cancers 2019, 11, 1531. [Google Scholar] [CrossRef] [Green Version]
  15. Samolyk-Kogaczewska, N.; Sierko, E.; Dziemianczyk-Pakiela, D.; Nowaszewska, K.B.; Lukasik, M.; Reszec, J. Usefulness of Hybrid PET/MRI in Clinical Evaluation of Head and Neck Cancer Patients. Cancers 2020, 12, 511. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Incoronato, M.; Grimaldi, A.M.; Mirabelli, P.; Cavaliere, C.; Parente, C.A.; Franzese, M.; Staibano, S.; Ilardi, G.; Russo, D.; Soricelli, A.; et al. Circulating miRNAs in Untreated Breast Cancer: An Exploratory Multimodality Morpho-Functional Study. Cancers 2019, 11, 876. [Google Scholar] [CrossRef] [Green Version]
  17. Montemagno, C.; Dumas, L.; Cavaillès, P.; Ahmadi, M.; Bacot, S.; Debiossat, M.; Soubies, A.; Djaïleb, L.; Leenhardt, J.; De Leiris, N.; et al. In Vivo Assessment of VCAM-1 Expression by SPECT/CT Imaging in Mice Models of Human Triple Negative Breast Cancer. Cancers 2019, 11, 1039. [Google Scholar] [CrossRef] [Green Version]
  18. Hoffmann, M.A.; Buchholz, H.-G.; Wieler, H.J.; Miederer, M.; Rosar, F.; Fischer, N.; Müller-Hübenthal, J.; Trampert, L.; Pektor, S.; Schreckenberger, M. PSA and PSA Kinetics Thresholds for the Presence of 68Ga-PSMA-11 PET/CT-Detectable Lesions in Patients with Biochemical Recurrent Prostate Cancer. Cancers 2020, 12, 398. [Google Scholar] [CrossRef] [Green Version]
  19. Bauckneht, M.; Capitanio, S.; Donegani, M.I.; Zanardi, E.; Miceli, A.; Murialdo, R.; Raffa, S.; Tomasello, L.; Vitti, M.; Cavo, A.; et al. Role of Baseline and Post-Therapy 18F-FDG PET in the Prognostic Stratification of Metastatic Castration-Resistant Prostate Cancer (mCRPC) Patients Treated with Radium-223. Cancers 2020, 12, 31. [Google Scholar] [CrossRef] [Green Version]
  20. Fanti, S.; Oyen, W.; Lalumera, E. Consensus Procedures in Oncological Imaging: The Case of Prostate Cancer. Cancers 2019, 11, 1788. [Google Scholar] [CrossRef] [Green Version]
  21. Fiz, F.; Dittmann, H.; Campi, C.; Weissinger, M.; Sahbai, S.; Reimold, M.; Stenzl, A.; Piana, M.; Sambuceti, G.; la Fougère, C. Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: A 3D Analysis of SPECT/CT Images. Cancers 2019, 11, 869. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Zattoni, F.; Incerti, E.; Dal Moro, F.; Moschini, M.; Castellucci, P.; Panareo, S.; Picchio, M.; Fallanca, F.; Briganti, A.; Gallina, A.; et al. 18F-FDG PET/CT and Urothelial Carcinoma: Impact on Management and Prognosis—A Multicenter Retrospective Study. Cancers 2019, 11, 700. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Laudicella, R.; Albano, D.; Alongi, P.; Argiroffi, G.; Bauckneht, M.; Baldari, S.; Bertagna, F.; Boero, M.; Vincentis, G.D.; Sole, A.D.; et al. 18F-Facbc in Prostate Cancer: A Systematic Review and Meta-Analysis. Cancers 2019, 11, 1348. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Treglia, G.; Annunziata, S.; Pizzuto, D.A.; Giovanella, L.; Prior, J.O.; Ceriani, L. Detection Rate of 18F-Labeled PSMA PET/CT in Biochemical Recurrent Prostate Cancer: A Systematic Review and a Meta-Analysis. Cancers 2019, 11, 710. [Google Scholar] [CrossRef] [Green Version]
  25. Perrone, A.M.; Dondi, G.; Lima, G.M.; Castellucci, P.; Tesei, M.; Coluccelli, S.; Gasparre, G.; Porcelli, A.M.; Nanni, C.; Fanti, S.; et al. Potential Prognostic Role of 18F-FDG PET/CT in Invasive Epithelial Ovarian Cancer Relapse. A Preliminary Study. Cancers 2019, 11, 713. [Google Scholar] [CrossRef] [Green Version]
  26. Sachpekidis, C.; Karampinis, I.; Jakob, J.; Kasper, B.; Nowak, K.; Pilz, L.; Attenberger, U.; Gaiser, T.; Derigs, H.-G.; Schwarzbach, M.; et al. Neoadjuvant Pazopanib Treatment in High-Risk Soft Tissue Sarcoma: A Quantitative Dynamic 18F-FDG PET/CT Study of the German Interdisciplinary Sarcoma Group. Cancers 2019, 11, 790. [Google Scholar] [CrossRef] [Green Version]
  27. Perrone, A.M.; Dondi, G.; Coe, M.; Ferioli, M.; Telo, S.; Galuppi, A.; De Crescenzo, E.; Tesei, M.; Castellucci, P.; Nanni, C.; et al. Predictive Role of MRI and 18F FDG PET Response to Concurrent Chemoradiation in T2b Cervical Cancer on Clinical Outcome: A Retrospective Single Center Study. Cancers 2020, 12, 659. [Google Scholar] [CrossRef] [Green Version]
  28. Surov, A.; Meyer, H.J.; Höhn, A.-K.; Wienke, A.; Sabri, O.; Purz, S. 18F-FDG-PET Can Predict Microvessel Density in Head and Neck Squamous Cell Carcinoma. Cancers 2019, 11, 543. [Google Scholar] [CrossRef] [Green Version]
  29. Polverari, G.; Ceci, F.; Bertaglia, V.; Reale, M.L.; Rampado, O.; Gallio, E.; Passera, R.; Liberini, V.; Scapoli, P.; Arena, V.; et al. 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival. Cancers 2020, 12, 1163. [Google Scholar] [CrossRef]
  30. Castaldo, R.; Pane, K.; Nicolai, E.; Salvatore, M.; Franzese, M. The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status. Cancers 2020, 12, 518. [Google Scholar] [CrossRef] [Green Version]
  31. Monti, S.; Brancato, V.; Di Costanzo, G.; Basso, L.; Puglia, M.; Ragozzino, A.; Salvatore, M.; Cavaliere, C. Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol. Cancers 2020, 12, 390. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Schiano, C.; Franzese, M.; Pane, K.; Garbino, N.; Soricelli, A.; Salvatore, M.; de Nigris, F.; Napoli, C. Hybrid 18F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study. Cancers 2019, 11, 1444. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Fujima, N.; Shimizu, Y.; Yoshida, D.; Kano, S.; Mizumachi, T.; Homma, A.; Yasuda, K.; Onimaru, R.; Sakai, O.; Kudo, K.; et al. Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study. Cancers 2019, 11, 800. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table 1. Summary of articles in the special issue.
Table 1. Summary of articles in the special issue.
Topic
(in Alphabetic Order)
Type of Paper
Original Articles (ref)Reviews or Brief Article or Perspectives (ref)
Breast cancerNoneSalvatore et al. [1], Hildebrandt et al. [2]
CT imagingKimm et al. [3], Lee et al. [4]None
ImmunotherapyCastello et al. [5]Frega et al. [6], Decarez et al. [7]
LymphomaAlbano et al. [8]Voltin et al. [9]
MeningiomaNoneLaudicella et al. [10]
MRIJin et al. [11], Usuda et al. [12]None
PancreasNoneSerafini et al. [13], Montemagno et al. [14]
PET/MRISamolyk-Kogaczewska et al. [15], Incoronato et al. [16]None
PreclinicalMontemagno et al. [17]None
Prostate and genito-urinary Hoffmann et al. [18], Bauckneht et al. [19], Fanti et al. [20], Fiz et al. [21], Zattoni et al. [22]Laudicella et al. [23], Treglia et al. [24]
Response to therapy or predictors Perrone et al. [25], Sachpekidis et al. [26], Perrone et al. [27], Surov et al. [28]None
RadiomicsPolverari et al. [29], Castaldo et al. [30], Monti et al. [31], Schiano et al. [32], Fujima et al. [33]None

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Evangelista, L.; Fanti, S. What Is the Role of Imaging in Cancers? Cancers 2020, 12, 1494. https://doi.org/10.3390/cancers12061494

AMA Style

Evangelista L, Fanti S. What Is the Role of Imaging in Cancers? Cancers. 2020; 12(6):1494. https://doi.org/10.3390/cancers12061494

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Evangelista, Laura, and Stefano Fanti. 2020. "What Is the Role of Imaging in Cancers?" Cancers 12, no. 6: 1494. https://doi.org/10.3390/cancers12061494

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