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Imaging Biomarkers in Oncology
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Imaging Biomarkers in Oncology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Personalized Therapy and Drug Delivery".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 9228

Special Issue Editors

Dr. Lorenzo Faggioni

E-Mail Website
Guest Editor
Department of Translational Research, University of Pisa, 56126 Pisa, Italy
Interests: computed tomography; magnetic resonance imaging; contrast media; oncologic imaging; cardiac imaging; imaging informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dania Cioni

E-Mail Website
Guest Editor
Department of Surgery, University of Pisa, 56126 Pisa, Italy
Interests: radiology; imaging; CT; MRI; tumor
Dr. Michela Gabelloni

E-Mail Website
Guest Editor
Department of Translational Research, University of Pisa, 56126 Pisa, Italy
Interests: radiology; imaging; cancer

Special Issue Information

Dear Colleagues,

In the last decade, oncology has seen dramatic advancements that have revolutionized cancer care, allowing for a more personalized treatment approach and overall improved patient management in an increasing proportion of clinical scenarios. In this context, medical imaging plays a key role in defining and evaluating specific imaging biomarkers, intended as objective imaging-based indicators that may serve to refine or enhance diagnostic information obtained by conventional image analysis, assess early therapeutic responses, predict individual outcomes, and guide treatment based on patient- and disease-specific features. The purpose of this Special Issue is to collect evidence from the latest research landscape regarding the status of imaging biomarkers in oncology, with a special focus on radiomics and its evolving role in the advanced management of the most common and paradigmatic cancers.

Dr. Lorenzo Faggioni
Prof. Dr. Dania Cioni
Dr. Michela Gabelloni
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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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 2600 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

  • imaging biomarkers
  • radiomics
  • artificial intelligence
  • personalized therapy
  • target therapy
  • radiology
  • oncology
  • multimodality imaging

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Published Papers (4 papers)

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Research

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11 pages, 895 KiB  
Article
Effectiveness and Safety of Immune Checkpoint Inhibitors in Older Cancer Patients
by Damir Vucinic, Iva Skocilic, Marin Golcic, Renata Dobrila-Dintinjana, Maja Kolak, Ivona Jerkovic, Eleonora Cini Tesar, Ani Mihaljevic Ferari, Arnela Redjovic, Jasna Marusic, Doris Kolovrat and Ivana Mikolasevic
J. Pers. Med. 2024, 14(3), 278; https://doi.org/10.3390/jpm14030278 - 1 Mar 2024
Viewed by 1694
Abstract
Background: The development of immunotherapy checkpoint inhibitors (ICIs) has revolutionized cancer care. However, old patients are underrepresented in most clinical trials, although they represent a significant proportion of real-world patients. We aimed to evaluate the effectiveness and safety of ICIs in patients older [...] Read more.
Background: The development of immunotherapy checkpoint inhibitors (ICIs) has revolutionized cancer care. However, old patients are underrepresented in most clinical trials, although they represent a significant proportion of real-world patients. We aimed to evaluate the effectiveness and safety of ICIs in patients older than the age of 70. Methods: We performed a retrospective chart review of 145 patients aged 70 or older treated with ICIs for metastatic or unresectable cancer. Results: Median progression-free survival (PFS) was 10.4 months (95% CI 8.6–13.7), with no differences between octogenarians and septuagenarians (p = 0.41). Female gender (p = 0.04) and first-line treatment setting (p < 0.0001) were associated with a longer median PFS. Median overall survival (OS) was 20.7 months (95% CI 13.5–35.0 months), with no difference based on performance status, cancer site, gender, or between septuagenarians and octogenarians (all p > 0.005). Patients treated with ICIs in the first-line setting reported longer OS compared to treatment in the second-line setting (p < 0.001). Discontinuation of ICIs due to adverse effects was associated with both shorter PFS (p = 0.0005) and OS (p < 0.0001). Conclusion: The effectiveness of ICIs in older cancer patients primarily depends on the line of treatment and treatment discontinuation. Octogenarians experienced similar treatment responses, PFS, OS, and adverse effects compared to septuagenarians. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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11 pages, 437 KiB  
Article
Development and Validation of Artificial-Intelligence-Based Radiomics Model Using Computed Tomography Features for Preoperative Risk Stratification of Gastrointestinal Stromal Tumors
by Marco Rengo, Alessandro Onori, Damiano Caruso, Davide Bellini, Francesco Carbonetti, Domenico De Santis, Simone Vicini, Marta Zerunian, Elsa Iannicelli, Iacopo Carbone and Andrea Laghi
J. Pers. Med. 2023, 13(5), 717; https://doi.org/10.3390/jpm13050717 - 24 Apr 2023
Cited by 3 | Viewed by 1902
Abstract
Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on [...] Read more.
Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST’s prognosis as determined by the Miettinen classification. Methods: patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. Results: 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. Conclusions: the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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10 pages, 1564 KiB  
Article
Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
by Gianluca Carlini, Caterina Gaudiano, Rita Golfieri, Nico Curti, Riccardo Biondi, Lorenzo Bianchi, Riccardo Schiavina, Francesca Giunchi, Lorenzo Faggioni, Enrico Giampieri, Alessandra Merlotti, Daniele Dall’Olio, Claudia Sala, Sara Pandolfi, Daniel Remondini, Arianna Rustici, Luigi Vincenzo Pastore, Leonardo Scarpetti, Barbara Bortolani, Laura Cercenelli, Eugenio Brunocilla, Emanuela Marcelli, Francesca Coppola and Gastone Castellaniadd Show full author list remove Hide full author list
J. Pers. Med. 2023, 13(3), 478; https://doi.org/10.3390/jpm13030478 - 6 Mar 2023
Cited by 3 | Viewed by 2151
Abstract
Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. [...] Read more.
Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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Review

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11 pages, 3930 KiB  
Review
Imaging Assessment of Interval Metastasis from Melanoma
by Igino Simonetti, Piero Trovato, Vincenza Granata, Carmine Picone, Roberta Fusco, Sergio Venanzio Setola, Mauro Mattace Raso, Corrado Caracò, Paolo A. Ascierto, Fabio Sandomenico and Antonella Petrillo
J. Pers. Med. 2022, 12(7), 1033; https://doi.org/10.3390/jpm12071033 - 24 Jun 2022
Cited by 2 | Viewed by 2479
Abstract
Interval metastasis is a particular metastatic category of metastatic localizations in the lymph nodes in patients with melanoma. Interval nodes are generally located at nonregional lymphatic stations placed along the pathway of the spread of melanoma, such as the epitrochlear lymph node station, [...] Read more.
Interval metastasis is a particular metastatic category of metastatic localizations in the lymph nodes in patients with melanoma. Interval nodes are generally located at nonregional lymphatic stations placed along the pathway of the spread of melanoma, such as the epitrochlear lymph node station, the popliteal fossa, and the retroareolar station. Imaging techniques for evaluation of patients with interval metastasis from melanoma diseases include ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), lymphoscintigraphy (LS), and positron emission tomography (PET). A literature review was conducted through a methodical search on the Pubmed and Embase databases. The evaluation of lymph node metastases represents a critical phase in the staging and follow-up of melanoma patients. Therefore, a thorough knowledge of the imaging methods available and the interactions between the clinician and the radiologist are essential for making the correct choice for individual patients, for a better management, and to improve treatment and survival. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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