Next Article in Journal
Image Quality and Radiation Dose of Contrast-Enhanced Chest-CT Acquired on a Clinical Photon-Counting Detector CT vs. Second-Generation Dual-Source CT in an Oncologic Cohort: Preliminary Results
Next Article in Special Issue
Prostate Cancer Detection with mpMRI According to PI-RADS v2 Compared with Systematic MRI/TRUS-Fusion Biopsy: A Prospective Study
Previous Article in Journal
Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT)
Previous Article in Special Issue
Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Tumor Diagnosis and Treatment: Imaging Assessment

Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
Author to whom correspondence should be addressed.
Tomography 2022, 8(3), 1463-1465;
Submission received: 22 May 2022 / Accepted: 26 May 2022 / Published: 30 May 2022
(This article belongs to the Special Issue Tumor Diagnosis and Treatment: Imaging Assessment)
At present, oncologic imaging is crucial for clinical decision-making. Imaging is used for cancer screening, diagnosis, staging, restaging and monitoring recurrence [1].
This Special Issue includes full research articles, case reports and reviews focused on oncological imaging to detect tumors and assess response after chemo-, radio-, or immuno-therapy.
Scientific societies are promoting standardized reporting systems for diagnosis and screening to reduce the variability in the reports and improve the detection of small tumors. The standardized reports aimed to collect information about present and past illness of the patient, symptoms and risk factors, previous diagnostic investigations, together with a classification of the pathology of interest and imaging data of the detected lesions. In this way, the reports not only increase physicians’ ability to reach an accurate and standardized diagnosis for appropriate clinical management, but have become a tool of utmost importance for collecting data to be used in statistical analysis for research purposes [2]. In recent years, different Reporting and Data Systems (RADSs) have been published to standardize the reports of different tumors (BI-RADS, LI-RADS, PI-RADS, My-RADS, etc.) [3,4,5,6]. These criteria are continuously evolving and improving, since new imaging techniques have been developed, and it is necessary to increase the accuracy of the reporting systems as much as possible.
Multiple criteria have been proposed to evaluate the tumor response and metastases to neoadjuvant or adjuvant therapies (chemo-, radio- or immune-therapies), starting from the well-known RECIST criteria [7] and ranging to volumetric analyses and the evaluation of the image texture. Conventionally, imaging is qualitatively assessed, leaving much information that remains unexploited. A particularly difficult parameter to qualitatively assess is the tumors’ intralesional heterogeneity, which has been demonstrated to correlate with a worse outcome for the patients and more aggressive tumors [8]. Using textural analysis (TA), it is possible to elaborate the raw data that are provided by any technique (ultra-sound (US), computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET)), to obtain quantitative parameters that are not normally discernible to radiologists by eye, but that can provide a detailed characterization of any lesion [9]. TA can assess tumor heterogeneity by analyzing the relationship and distribution of pixel or voxel gray levels in the lesion area [10]. The TA is a non-invasive imaging tool with great potential to predict pathologic features, response to therapy and prognosis for many tumors [11]. In specific settings, the automatic or semiautomatic extraction of the textural features has shown similar or even stronger accuracy when detecting cancer compared to the qualitative evaluation of expert radiologists [12,13]. This technique provides new diagnostics pathways and, by means of integration with artificial intelligence algorithms in the elaboration of such large amount of data, could become a routinary application in the near feature. The correlation between radiomics data and clinical reports, pathology, histology and genetic information could provide a global view of the tumor biology [14].
Another field of research that has shown impressive results in oncological diagnostics is hybrid imaging, which is mainly driven by the recent introduction of the PET/MRI scanners. These imaging modalities have three main advantages compared to PET/CT [15]. First of all, compared to CT, MRI shows a higher contrast resolution for tissue characterization, which is particularly valuable for soft tissue, using specific multiparametric information techniques (e.g., mapping, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)). Secondly, it is possible to obtain a better anatomical localization of the radio-tracers activity inside the solid organs in the fused PET/MR images, and profit by the tissue characterization. Finally, there is a significant reduction in exposure to ionizing radiation in PET/MRI compared to PET/CT, making PET/MRI more suitable for pediatric patients and young women. The combination of morphological, functional and metabolic information in the same test has raised this new technique as a possible “one-stop-shop” tool in oncological imaging that can aid in the diagnosis and clinical management of oncologic patients with a lower biological cost. In the clinical setting, PET/MRI was primarily used for prostate cancer, neuro-oncological diseases, soft-tissue sarcomas, colorectal cancer, pediatric malignancies and in cases of persistent clinical suspicion of abdominal malignancy with negative imaging findings [15,16].
Thus, standardized reports, TA and PET/MRI represent three of the main research fields in which oncological imaging has been developing in recent years. Still, the continuous improvements in radiological techniques are broadening the horizon for the applications of medical imaging in clinical routine.
This Special Issue collects papers that show how imaging can improve the early detection of tumors to hasten patients’ access to treatments and better evaluate their response. In the continuous improvement in chemotherapy and organ-saving approaches, it is of paramount importance to accurately assess the size of the residual tumor to determine the most appropriate treatment.

Author Contributions

Data curation, F.C., F.V., G.C., C.Z. and A.P.; writing—original draft preparation, F.C.; writing—review and editing, F.V., G.C., C.Z., A.P. and E.Q.; supervision, E.Q. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Fass, L. Imaging and cancer: A review. Mol. Oncol. 2008, 2, 115–152. [Google Scholar] [CrossRef] [PubMed]
  2. Il Referto Strutturato. Available online: (accessed on 18 May 2022).
  3. Turkbey, B.; Rosenkrantz, A.B.; Haider, M.A.; Padhani, A.R.; Villeirs, G.; Macura, K.J.; Tempany, C.M.; Choyke, P.L.; Cornud, F.; Margolis, D.J.; et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur. Urol. 2019, 76, 340–351. [Google Scholar] [CrossRef]
  4. CT-MRI-LI-RADS-v2018. Available online: (accessed on 18 May 2022).
  5. BI-RADS. Available online: (accessed on 18 May 2022).
  6. Messiou, C.; Hillengass, J.; Delorme, S.; Lecouvet, F.E.; Moulopoulos, L.A.; Collins, D.J.; Blackledge, M.D.; Abildgaard, N.; Østergaard, B.; Schlemmer, H.P.; et al. Guidelines for Acquisition, Interpretation, and Reporting of Whole-Body MRI in Myeloma: Myeloma Response Assessment and Diagnosis System (MY-RADS). Radiology 2019, 291, 5–13. [Google Scholar] [CrossRef] [Green Version]
  7. Eisenhauer, E.A.; Therasse, P.; Bogaerts, J.; Schwartz, L.H.; Sargent, D.; Ford, R.; Dancey, J.; Arbuck, S.; Gwyther, S.; Mooney, M.; et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1·1). Eur. J. Cancer 2009, 45, 228–247. [Google Scholar] [CrossRef] [PubMed]
  8. Varghese, B.A.; Cen, S.Y.; Hwang, D.H.; Duddalwar, V.A. Texture Analysis of Imaging: What Radiologists Need to Know. AJR Am. J. Roentgenol. 2019, 212, 520–528. [Google Scholar] [CrossRef] [PubMed]
  9. Lambin, P.; Leijenaar, R.T.H.; Deist, T.M.; Peerlings, J.; de Jong, E.E.C.; van Timmeren, J.; Sanduleanu, S.; Larue, R.T.H.M.; Even, A.J.G.; Jochems, A.; et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017, 14, 749–762. [Google Scholar] [CrossRef] [PubMed]
  10. Ganeshan, B.; Miles, K.A. Quantifying tumour heterogeneity with CT. Cancer Imaging 2013, 13, 140–149. [Google Scholar] [CrossRef] [Green Version]
  11. Liu, L.; Liu, Y.; Xu, L.; Li, Z.; Lv, H.; Dong, N.; Li, W.; Yang, Z.; Wang, Z.; Jin, E. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J. Magn. Reson. Imaging 2017, 45, 1798–1808. [Google Scholar] [CrossRef]
  12. Perre, S.V.; Duron, L.; Milon, A.; Bekhouche, A.; Balvay, D.; Cornelis, F.H.; Fournier, L.; Thomassin-Naggara, I. Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions. Eur. Radiol. 2021, 31, 4848–4859. [Google Scholar] [CrossRef] [PubMed]
  13. Liu, Y.; Zheng, H.; Liang, Z.; Miao, Q.; Brisbane, W.G.; Marks, L.S.; Raman, S.S.; Reiter, R.E.; Yang, G.; Sung, K. Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification. Diagnostics 2021, 11, 1785. [Google Scholar] [CrossRef] [PubMed]
  14. Gillies, R.J.; Kinahan, P.E.; Hricak, H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016, 278, 563–577. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Seifert, R.; Kersting, D.; Rischpler, C.; Opitz, M.; Kirchner, J.; Pabst, K.M.; Mavroeidi, I.A.; Laschinsky, C.; Grueneisen, J.; Schaarschmidt, B.; et al. Clinical Use of PET/MR in Oncology: An Update. Semin. Nucl. Med. 2022, 52, 356–364. [Google Scholar] [CrossRef] [PubMed]
  16. Crimì, F.; Valeggia, S.; Baffoni, L.; Stramare, R.; Lacognata, C.; Spolverato, G.; Albertoni, L.; Spimpolo, A.; Evangelista, L.; Zucchetta, P.; et al. [18F]FDG PET/MRI in rectal cancer. Ann. Nucl. Med. 2021, 35, 281–290. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Crimì, F.; Vernuccio, F.; Cabrelle, G.; Zanon, C.; Pepe, A.; Quaia, E. Tumor Diagnosis and Treatment: Imaging Assessment. Tomography 2022, 8, 1463-1465.

AMA Style

Crimì F, Vernuccio F, Cabrelle G, Zanon C, Pepe A, Quaia E. Tumor Diagnosis and Treatment: Imaging Assessment. Tomography. 2022; 8(3):1463-1465.

Chicago/Turabian Style

Crimì, Filippo, Federica Vernuccio, Giulio Cabrelle, Chiara Zanon, Alessia Pepe, and Emilio Quaia. 2022. "Tumor Diagnosis and Treatment: Imaging Assessment" Tomography 8, no. 3: 1463-1465.

Article Metrics

Back to TopTop