Special Issue "Cancer Imaging: Current Practice and Future Perspectives"

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 19286

Special Issue Editors

Prof. Dr. Khaled M. Elsayes
E-Mail Website
Guest Editor
Department of Abdominal Imaging, The division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
Interests: hepatobiliary tumors; adrenal tumors; artificial intelligence; treatment response; quantitative analysis
Prof. Dr. Tanya W. Moseley
E-Mail Website
Guest Editor
Department of Breast Imaging, The division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
Interests: breast density in high-risk patients
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Cancers is dedicated to discussing current standard practices and future perspectives in imaging of cancer. The articles included-herein-cover a broad range of topics, with the goal being to provide a comprehensive overview of the state-of-the-art knowledge related to cancer imaging.

Advancements in sciences related to the field of medical imaging have resulted in unprecedented changes in the assessment, management and post-treatment follow-up of cancer. Various modalities including ultrasound, computed tomography, magnetic resonance, and molecular imaging are the primary tools being developed for oncological imaging. Advancements in these modalities, the introduction of artificial intelligence and quantitative, as well as recent management of tumors and new imaging techniques have led to revolutionary changes in diagnosis and treatment.

In this Special Issue, we will publish several manuscripts pertinent to imaging of cancer with emphasis on current practices and future perspectives.

Prof. Dr. Khaled M. Elsayes
Prof. Dr. Tanya Moseley
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cancer imaging
  • screening
  • staging
  • treatment response
  • artificial intelligence

Published Papers (19 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

Article
Diagnostic Performances of the ACR-TIRADS System in Thyroid Nodules Triage: A Prospective Single Center Study
Cancers 2021, 13(9), 2230; https://doi.org/10.3390/cancers13092230 - 06 May 2021
Cited by 3 | Viewed by 805
Abstract
Ultrasound scores are used to determine whether thyroid nodules should undergo Fine Needle Aspiration (FNA) or simple clinical follow-up. Different scores have been proposed for this task, with the American College of Radiology (ACR) TIRADS system being one of the most widely used. [...] Read more.
Ultrasound scores are used to determine whether thyroid nodules should undergo Fine Needle Aspiration (FNA) or simple clinical follow-up. Different scores have been proposed for this task, with the American College of Radiology (ACR) TIRADS system being one of the most widely used. This study evaluates its ability in triaging thyroid nodules deserving FNA on a large prospective monocentric Italian case series of 493 thyroid nodules from 448 subjects. In ACR 1–2, cytology never prompted a surgical indication. In 59% of cases classified as TIR1c-TIR2, the FNA procedure could be ancillary, according to the ACR-TIRADS score. A subset (37.9%) of cases classified as TIR4-5 would not undergo FNA, according to the dimensional thresholds used by the ACR-TIRADS. Applying the ACR score, a total of 46.5% thyroid nodules should be studied with FNA. The ACR system demonstrated a sensitivity and specificity of 58.9% and 59% in the identification of patients with cytology ≥TIR3A, with a particularly high false negative rate for ACR classes ≥3 (44.8%, 43/96), which would dramatically decrease (7.3%, 7/96) if the dimensional criteria were not taken into account. In ACR 3–4–5, a correspondence with the follow-up occurred in 60.3%, 50.2% and 51.9% of cases. The ACR-TIRADS is a useful risk stratification tool for thyroid nodules, although the current dimensional thresholds could lead to an underestimation of malignant lesions. Their update might be considered in future studies to increase the screening performances of the system. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Article
Image-Guided Adaptive Brachytherapy (IGABT) for Primary Vaginal Cancer: Results of the International Multicenter RetroEMBRAVE Cohort Study
Cancers 2021, 13(6), 1459; https://doi.org/10.3390/cancers13061459 - 23 Mar 2021
Cited by 2 | Viewed by 878
Abstract
Purpose: This study assessed outcomes following the nowadays standing treatment for primary vaginal cancer with radio(chemo)therapy and image-guided adaptive brachytherapy (IGABT) in a multicenter patient cohort. Methods: Patients treated with computer tomography (CT)–MRI-assisted-based IGABT were included. Retrospective data collection included patient, tumor and [...] Read more.
Purpose: This study assessed outcomes following the nowadays standing treatment for primary vaginal cancer with radio(chemo)therapy and image-guided adaptive brachytherapy (IGABT) in a multicenter patient cohort. Methods: Patients treated with computer tomography (CT)–MRI-assisted-based IGABT were included. Retrospective data collection included patient, tumor and treatment characteristics. Late morbidity was assessed by using the CTCAE 3.0 scale. Results: Five European centers included 148 consecutive patients, with a median age of 63 years. At a median follow-up of 29 months (IQR 25–57), two- and five-year local control were 86% and 83%; disease-free survival (DFS) was 73% and 66%, and overall survival (OS) was 79% and 68%, respectively. Crude incidences of ≥ grade-three urogenital, gastro-intestinal and vaginal morbidity was 8%, 3% and 8%, respectively. Lymph node metastasis was an independent prognostic factor for disease-free survival (DFS). Univariate analysis showed improved local control in patients with T2–T4 tumors if >80 Gy EQD2α/β10 was delivered to the clinical target volume (CTV) at the time of brachytherapy. Conclusions: In this large retrospective multicenter study, IGABT for primary vaginal cancer resulted in a high local control with acceptable morbidity. These results compared favorably with two-dimensional (2D) radiograph-based brachytherapy and illustrate that IGABT plays an important role in the treatment of vaginal cancer. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Article
Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma
Cancers 2021, 13(4), 600; https://doi.org/10.3390/cancers13040600 - 03 Feb 2021
Cited by 5 | Viewed by 1039
Abstract
We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I–V in 39 patients with [...] Read more.
We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I–V in 39 patients with oral squamous cell carcinoma (OSCC) who underwent preoperative CT and neck dissection. LNs were randomly divided into training (70%), validation (10%), and test (20%) sets. For the validation and test sets, cervical LNs at levels I–II were evaluated. Convolutional neural network analysis was performed using Xception architecture. Two radiologists evaluated the possibility of metastasis to cervical LNs using a 4-point scale. The area under the curve of the DL model and the radiologists’ assessments were calculated and compared at levels I–II, I, and II. In the test set, the area under the curves at levels I–II (0.898) and II (0.967) were significantly higher than those of each reader (both, p < 0.05). DL analysis of pretreatment contrast-enhanced CT can help classify cervical LNs in patients with OSCC with better diagnostic performance than radiologists’ assessments alone. DL may be a valuable diagnostic tool for differentiating between benign and metastatic cervical LNs. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Article
NIR Imaging of the Integrin-Rich Head and Neck Squamous Cell Carcinoma Using Ternary Copper Indium Selenide/Zinc Sulfide-Based Quantum Dots
Cancers 2020, 12(12), 3727; https://doi.org/10.3390/cancers12123727 - 11 Dec 2020
Cited by 4 | Viewed by 1076
Abstract
The efficient intraoperative identification of cancers requires the development of the bright, minimally-toxic, tumor-specific near-infrared (NIR) probes as contrast agents. Luminescent semiconductor quantum dots (QDs) offer several unique advantages for in vivo cellular imaging by providing bright and photostable fluorescent probes. Here, we [...] Read more.
The efficient intraoperative identification of cancers requires the development of the bright, minimally-toxic, tumor-specific near-infrared (NIR) probes as contrast agents. Luminescent semiconductor quantum dots (QDs) offer several unique advantages for in vivo cellular imaging by providing bright and photostable fluorescent probes. Here, we present the synthesis of ZnCuInSe/ZnS core/shell QDs emitting in NIR (~750 nm) conjugated to NAVPNLRGDLQVLAQKVART (A20FMDV2) peptide for targeting αvβ6 integrin-rich head and neck squamous cell carcinoma (HNSCC). Integrin αvβ6 is usually not detectable in nonpathological tissues, but is highly upregulated in HNSCC. QD-A20 showed αvβ6 integrin-specific binding in two-dimension (2D) monolayer and three-dimension (3D) spheroid in vitro HNSCC models. QD-A20 exhibit limited penetration (ca. 50 µm) in stroma-rich 3D spheroids. Finally, we demonstrated the potential of these QDs by time-gated fluorescence imaging of stroma-rich 3D spheroids placed onto mm-thick tissue slices to mimic imaging conditions in tissues. Overall, QD-A20 could be considered as highly promising nanoprobes for NIR bioimaging and imaging-guided surgery. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Graphical abstract

Article
Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study
Cancers 2020, 12(12), 3656; https://doi.org/10.3390/cancers12123656 - 05 Dec 2020
Cited by 5 | Viewed by 998
Abstract
Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative [...] Read more.
Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Article
AGTR1 Is Overexpressed in Neuroendocrine Neoplasms, Regulates Secretion and May Potentially Serve as a Target for Molecular Imaging and Therapy
Cancers 2020, 12(11), 3138; https://doi.org/10.3390/cancers12113138 - 27 Oct 2020
Viewed by 884
Abstract
This study identified and confirmed angiotensin II (ATII) as a strong activator of signaling in neuroendocrine neoplasm (NEN) cells. Expression analyses of the ATII receptor type 1 (AGTR1) revealed an upregulation of mRNA levels (RT-qPCR) and radioligand binding (autoradiography) in small-intestinal (n [...] Read more.
This study identified and confirmed angiotensin II (ATII) as a strong activator of signaling in neuroendocrine neoplasm (NEN) cells. Expression analyses of the ATII receptor type 1 (AGTR1) revealed an upregulation of mRNA levels (RT-qPCR) and radioligand binding (autoradiography) in small-intestinal (n = 71) NEN tissues compared to controls (n = 25). NEN cells with high AGTR1 expression exhibited concentration-dependent calcium mobilization and chromogranin A secretion upon stimulation with ATII, blocked by AGTR1 antagonism and Gαq inhibition. ATII also stimulated serotonin secretion from BON cells. AGTR1 ligand saralasin was coupled to a near-infrared fluorescent (NIRF) dye and tested for its biodistribution in a nude mouse model bearing AGTR1-positive BON and negative QGP-1 xenograft tumors. NIRF imaging showed significantly higher uptake in BON tumors. This proof of concept establishes AGTR1 as a novel target in NEN, paving the way for translational chelator-based probes for diagnostic PET imaging and radioligand therapy. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Graphical abstract

Review

Jump to: Research, Other

Review
An Overview of Selected Rare B-Cell Lymphoproliferative Disorders: Imaging, Histopathologic, and Clinical Features
Cancers 2021, 13(22), 5853; https://doi.org/10.3390/cancers13225853 - 22 Nov 2021
Viewed by 615
Abstract
Lymphoproliferative disorders (LPD) are conditions characterized by the uncontrolled proliferation of B or T-cell lines. They encompass a wide spectrum of abnormalities, which may be broadly classified as reactive processes or malignant diseases, such as lymphoma, based on their cellular clonality and clinical [...] Read more.
Lymphoproliferative disorders (LPD) are conditions characterized by the uncontrolled proliferation of B or T-cell lines. They encompass a wide spectrum of abnormalities, which may be broadly classified as reactive processes or malignant diseases, such as lymphoma, based on their cellular clonality and clinical behavior. While some of these disorders are rare, they may be encountered sporadically in clinical practice, causing diagnostic dilemmas owing to overlap in their clinical and imaging features with more common disorders. The updated 4th edition WHO classification of lymphoid neoplasms was released in 2016 to incorporate the rapid clinical, pathological, molecular biology and cytogenetic advances of some of these disorders. Despite these updates, very little information is presented in the literature from the radiology perspective. The aim of this article is to familiarize radiologists and other physicians with certain rare variants of B-cell lymphoproliferative disorders with a focus on imaging features of these disorders, as well as to provide an overview of some important updates contained within the new WHO classification of lymphoid neoplasms. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Uncommon Variants of Mature T-Cell Lymphomas (MTCLs): Imaging and Histopathologic and Clinical Features with Updates from the Fourth Edition of the World Health Organization (WHO) Classification of Lymphoid Neoplasms
Cancers 2021, 13(20), 5217; https://doi.org/10.3390/cancers13205217 - 18 Oct 2021
Cited by 1 | Viewed by 565
Abstract
Understanding the pathogenesis and molecular biology of malignant lymphomas is challenging, given the complex nature and incongruity of these disorders. The classification of lymphoma is continually evolving to account for advances in clinical, pathological, molecular biology and cytogenetic aspects, which impact our understanding [...] Read more.
Understanding the pathogenesis and molecular biology of malignant lymphomas is challenging, given the complex nature and incongruity of these disorders. The classification of lymphoma is continually evolving to account for advances in clinical, pathological, molecular biology and cytogenetic aspects, which impact our understanding of these disorders. The latest fourth edition of the WHO classification of lymphoid malignancies was released in 2016 to account for these changes. Additionally, unlike B-cell lymphomas (BCL), T-cell lymphomas (TCL) are uncommon, and may be sporadically experienced in clinical practice. These disorders are rare, thus early diagnosis is challenging for both physicians and radiologists, owing to the overlap in clinical and imaging features with other, more common disorders. We aim to discuss some rare variants of T-cell lymphomas, including clinicopathologic and imaging features, as well as to give a glimpse of the updates contained within the new 2016 WHO classification. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Peutz–Jeghers Syndrome and the Role of Imaging: Pathophysiology, Diagnosis, and Associated Cancers
Cancers 2021, 13(20), 5121; https://doi.org/10.3390/cancers13205121 - 13 Oct 2021
Cited by 1 | Viewed by 1303
Abstract
The Peutz-Jeghers Syndrome (PJS) is an autosomal dominant neoplastic syndrome defined by hamartomatous polyps through the gastrointestinal tract, development of characteristic mucocutaneous pigmentations, and an elevated lifetime cancer risk. The majority of cases are due to a mutation in the STK11 gene located [...] Read more.
The Peutz-Jeghers Syndrome (PJS) is an autosomal dominant neoplastic syndrome defined by hamartomatous polyps through the gastrointestinal tract, development of characteristic mucocutaneous pigmentations, and an elevated lifetime cancer risk. The majority of cases are due to a mutation in the STK11 gene located at 19p13.3. The estimated incidence of PJS ranges from 1:50,000 to 1:200,000. PJS carries an elevated risk of malignancies including gastrointestinal, breast, lung, and genitourinary (GU) neoplasms. Patients with PJS are at a 15- to 18-fold increased malignancy risk relative to the general population. Radiologists have an integral role in the diagnosis of these patients. Various imaging modalities are used to screen for malignancies and complications associated with PJS. Awareness of various PJS imaging patterns, associated malignancies, and their complications is crucial for accurate imaging interpretation and patient management. In this manuscript, we provide a comprehensive overview of PJS, associated malignancies, and surveillance protocols. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Mastocytosis—A Review of Disease Spectrum with Imaging Correlation
Cancers 2021, 13(20), 5102; https://doi.org/10.3390/cancers13205102 - 12 Oct 2021
Cited by 1 | Viewed by 589
Abstract
Mastocytosis is a rare disorder due to the abnormal proliferation of clonal mast cells. Mast cells exist in most tissues, mature in situ from hematopoietic stem cells and develop unique characteristics of local effector cells. Mastocytosis develops by activation mutation of the KIT [...] Read more.
Mastocytosis is a rare disorder due to the abnormal proliferation of clonal mast cells. Mast cells exist in most tissues, mature in situ from hematopoietic stem cells and develop unique characteristics of local effector cells. Mastocytosis develops by activation mutation of the KIT surface receptor which is involved in the proliferation of a number of cell lines such as mast cells, germ cells, melanocytes, and hematopoietic cells. It manifests as two main categories: cutaneous mastocytosis and systemic mastocytosis. Imaging can play an important role in detection and characterization of the disease manifestation, not only by radiography and bone scans, but also magnetic resonance imaging and computed tomography, which can be more sensitive in the assessment of distinctive disease patterns. Radiologists should be aware of various appearances of this disease to better facilitate diagnosis and patient management. Accordingly, this review will discuss the clinical presentation, pathophysiology, and role of imaging in detection and extent estimation of the systemic involvement of the disease, in addition to demonstration of appearance on varying imaging modalities. Familiarity with the potential imaging findings associated with mastocytosis can aid in early disease diagnosis and classification and accordingly can lead directing further work up and better management. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Oncologic Imaging of the Lymphatic System: Current Perspective with Multi-Modality Imaging and New Horizon
Cancers 2021, 13(18), 4554; https://doi.org/10.3390/cancers13184554 - 10 Sep 2021
Cited by 1 | Viewed by 755
Abstract
The lymphatic system is an anatomically complex vascular network that is responsible for interstitial fluid homeostasis, transport of large interstitial particles and cells, immunity, and lipid absorption in the gastrointestinal tract. This network of specially adapted vessels and lymphoid tissue provides a major [...] Read more.
The lymphatic system is an anatomically complex vascular network that is responsible for interstitial fluid homeostasis, transport of large interstitial particles and cells, immunity, and lipid absorption in the gastrointestinal tract. This network of specially adapted vessels and lymphoid tissue provides a major pathway for metastatic spread. Many malignancies produce vascular endothelial factors that induce tumoral and peritumoral lymphangiogenesis, increasing the likelihood for lymphatic spread. Radiologic evaluation for disease staging is the cornerstone of oncologic patient treatment and management. Multiple imaging modalities are available to access both local and distant metastasis. In this manuscript, we review the anatomy, physiology, and imaging of the lymphatic system. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives
Cancers 2021, 13(14), 3521; https://doi.org/10.3390/cancers13143521 - 14 Jul 2021
Cited by 7 | Viewed by 1310
Abstract
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving [...] Read more.
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
PTEN Hamartoma Tumor Syndrome/Cowden Syndrome: Genomics, Oncogenesis, and Imaging Review for Associated Lesions and Malignancy
Cancers 2021, 13(13), 3120; https://doi.org/10.3390/cancers13133120 - 22 Jun 2021
Cited by 2 | Viewed by 1276
Abstract
PTEN hamartoma tumor syndrome/Cowden syndrome (CS) is a rare autosomal dominant syndrome containing a germline PTEN mutation that leads to the development of multisystem hamartomas and oncogenesis. Benign tumors such as Lhermitte–Duclos disease and malignant tumors involving the breast, thyroid, kidneys, and uterus [...] Read more.
PTEN hamartoma tumor syndrome/Cowden syndrome (CS) is a rare autosomal dominant syndrome containing a germline PTEN mutation that leads to the development of multisystem hamartomas and oncogenesis. Benign tumors such as Lhermitte–Duclos disease and malignant tumors involving the breast, thyroid, kidneys, and uterus are seen in CS. Radiologists have an integral role in the comanagement of CS patients. We present the associated imaging findings and imaging screening recommendations. Knowledge of the types of cancers commonly seen in CS and their imaging findings can aid in early tumor recognition during cancer screening to help ensure near-normal life spans in CS patients. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications
Cancers 2021, 13(11), 2681; https://doi.org/10.3390/cancers13112681 - 29 May 2021
Cited by 8 | Viewed by 1174
Abstract
Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing [...] Read more.
Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI
Cancers 2021, 13(11), 2522; https://doi.org/10.3390/cancers13112522 - 21 May 2021
Cited by 8 | Viewed by 846
Abstract
Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making [...] Read more.
Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Current Perspectives on Clinical Use of Exosomes as a Personalized Contrast Media and Theranostics
Cancers 2020, 12(11), 3386; https://doi.org/10.3390/cancers12113386 - 16 Nov 2020
Cited by 6 | Viewed by 1348
Abstract
An appropriate combination of biomarkers and imaging technologies will become standard practice in the future. Because the incidence of and mortality from cancers is rising, the further study of new approaches for the early detection and precise characterization of tumors is essential. Extracellular [...] Read more.
An appropriate combination of biomarkers and imaging technologies will become standard practice in the future. Because the incidence of and mortality from cancers is rising, the further study of new approaches for the early detection and precise characterization of tumors is essential. Extracellular vesicles (EVs), including exosomes, prove to have great potential when it comes to diagnosis and targeted therapy. Due to their natural ability to pass through biological barriers, depending on their origin, EVs can accumulate at defined sites, including tumors, preferentially. This manuscript discusses the difficulties and simplicities of processing cell-derived materials, packaging diverse groups of agents in EVs, and activating the biological complex. Developing exosome-based diagnostic techniques to detect disease precisely and early as well as treat disease marks a new era of personalized radiology and nuclear medicine. As circulating drug delivery vehicles for novel therapeutic modalities, EVs offer a new platform for cancer theranostic. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Review
Current Concepts in Multi-Modality Imaging of Solid Tumor Angiogenesis
Cancers 2020, 12(11), 3239; https://doi.org/10.3390/cancers12113239 - 03 Nov 2020
Cited by 2 | Viewed by 804
Abstract
There have been rapid advancements in cancer treatment in recent years, including targeted molecular therapy and the emergence of anti-angiogenic agents, which necessitate the need to quickly and accurately assess treatment response. The ideal tool is robust and non-invasive so that the treatment [...] Read more.
There have been rapid advancements in cancer treatment in recent years, including targeted molecular therapy and the emergence of anti-angiogenic agents, which necessitate the need to quickly and accurately assess treatment response. The ideal tool is robust and non-invasive so that the treatment can be rapidly adjusted or discontinued based on efficacy. Since targeted therapies primarily affect tumor angiogenesis, morphological assessment based on tumor size alone may be insufficient, and other imaging modalities and features may be more helpful in assessing response. This review aims to discuss the biological principles of tumor angiogenesis and the multi-modality imaging evaluation of anti-angiogenic therapeutic responses. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Other

Jump to: Research, Review

Opinion
Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data
Cancers 2021, 13(16), 3944; https://doi.org/10.3390/cancers13163944 - 05 Aug 2021
Cited by 2 | Viewed by 990
Abstract
Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily [...] Read more.
Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Systematic Review
Current Intraoperative Imaging Techniques to Improve Surgical Resection of Laryngeal Cancer: A Systematic Review
Cancers 2021, 13(8), 1895; https://doi.org/10.3390/cancers13081895 - 15 Apr 2021
Cited by 12 | Viewed by 1013
Abstract
Laryngeal cancer is a prevalent head and neck malignancy, with poor prognosis and low survival rates for patients with advanced disease. Treatment consists of unimodal therapy through surgery or radiotherapy in early staged tumors, while advanced stage tumors are generally treated with multimodal [...] Read more.
Laryngeal cancer is a prevalent head and neck malignancy, with poor prognosis and low survival rates for patients with advanced disease. Treatment consists of unimodal therapy through surgery or radiotherapy in early staged tumors, while advanced stage tumors are generally treated with multimodal chemoradiotherapy or (total) laryngectomy followed by radiotherapy. Still, the recurrence rate for advanced laryngeal cancer is between 25 and 50%. In order to improve surgical resection of laryngeal cancer and reduce local recurrence rates, various intraoperative optical imaging techniques have been investigated. In this systematic review, we identify these technologies, evaluating the current state and future directions of optical imaging for this indication. Narrow-band imaging (NBI) and autofluorescence (AF) are established tools for early detection of laryngeal cancer. Nonetheless, their intraoperative utility is limited by an intrinsic inability to image beyond the (sub-)mucosa. Likewise, contact endoscopy (CE) and optical coherence tomography (OCT) are technically cumbersome and only useful for mucosal margin assessment. Research on fluorescence imaging (FLI) for this application is sparse, dealing solely with nonspecific fluorescent agents. Evidently, the imaging modalities that have been investigated thus far are generally unsuitable for deep margin assessment. We discuss two optical imaging techniques that can overcome these limitations and suggest how they can be used to achieve adequate margins in laryngeal cancer at all stages. Full article
(This article belongs to the Special Issue Cancer Imaging: Current Practice and Future Perspectives)
Show Figures

Figure 1

Back to TopTop