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: 30 June 2024 | Viewed by 12102

Special Issue Editors


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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

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Guest Editor
Department of Surgery, University of Pisa, 56126 Pisa, Italy
Interests: radiology; imaging; CT; MRI; tumor

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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

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Keywords

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

Published Papers (8 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 922
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, 555 KiB  
Article
Multiple Myeloma from the Perspective of Pro- and Anti-Oxidative Parameters: Potential for Diagnostic and/or Follow-Up Purposes?
by Ayse Nilgun Kul and Bahar Ozturk Kurt
J. Pers. Med. 2024, 14(3), 221; https://doi.org/10.3390/jpm14030221 - 20 Feb 2024
Viewed by 853
Abstract
Background: Multiple myeloma is a hematological malignancy characterized by anemia, antibodies causing kidney damage, and damage to multiple organs, which come together to cause morbidity. Although oxidative stress is not a core pathological aspect of multiple myeloma, reactive oxygen species (ROS) and antioxidant [...] Read more.
Background: Multiple myeloma is a hematological malignancy characterized by anemia, antibodies causing kidney damage, and damage to multiple organs, which come together to cause morbidity. Although oxidative stress is not a core pathological aspect of multiple myeloma, reactive oxygen species (ROS) and antioxidant balance have been shown to play a role in the disease process and are considered in its management. In the presented study, we aim to assess the reliability of specific oxidant and antioxidant variables as potential biomarkers for multiple myeloma and to determine which of these variables might exhibit higher sensitivity in predicting multiple myeloma. Methods: This case-control study was conducted between March 2023 and August 2023. A total of 30 multiple myeloma patients, newly diagnosed according to the multiple myeloma diagnostic criteria revised by the International Myeloma Study Group in 2014, and a total of 30 volunteers without multiple myeloma were included in this study. Serum glutathione peroxidase (GSH-Px), superoxide dismutase (SOD) and catalase (CAT), and malondialdehyde (MDA) and nitric oxide (NO) levels were measured with the first blood samples taken after inclusion. Results: The groups had similar age (p = 0.623) and sex distribution (p = 1.000). MDA (cut-off: >4.35, p < 0.001), GSH-Px (<59.8, p < 0.001), CAT (<67.2, p < 0.001), SOD (<21.2, p = 0.001), and NO (>38.5, p < 0.001) could significantly detect multiple myeloma. Multivariate logistic regression revealed that increased MDA (p = 0.003) and NO (p = 0.001) levels and decreased GSH-Px (p = 0.001), CAT (p = 0.001), and SOD levels were independently associated with multiple myeloma disease. Conclusions: The presence of increased antioxidant levels and decreased antioxidant levels in patients with multiple myeloma is the clearest indicator of increased oxidative stress. These parameters may help to identify potential therapeutic targets and develop strategies to control disease progression. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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11 pages, 7265 KiB  
Article
Endometrial Cancer Staging: Is There Value in ADC?
by Ana Sofia Linhares Moreira, Vera Ribeiro, Giacomo Aringhieri, Salvatore Claudio Fanni, Lorenzo Tumminello, Lorenzo Faggioni, Dania Cioni and Emanuele Neri
J. Pers. Med. 2023, 13(5), 728; https://doi.org/10.3390/jpm13050728 - 25 Apr 2023
Cited by 3 | Viewed by 1345
Abstract
Purpose: To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement. Methods: Patients with endometrial cancers diagnosed between [...] Read more.
Purpose: To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement. Methods: Patients with endometrial cancers diagnosed between 2018–2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification. Results: The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery. Conclusions. ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center. 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 1 | Viewed by 1369
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 2 | Viewed by 1568
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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11 pages, 919 KiB  
Article
Are Semiquantitative Methods Superior to Deauville Scoring in the Monitoring Therapy Response for Pediatric Hodgkin Lymphoma?
by Firuz Ibrahim, Michela Gabelloni, Lorenzo Faggioni, Subramanyam Padma, Arun R. Visakh, Dania Cioni and Emanuele Neri
J. Pers. Med. 2023, 13(3), 445; https://doi.org/10.3390/jpm13030445 - 28 Feb 2023
Cited by 1 | Viewed by 1060
Abstract
Tailoring treatment in patients with Hodgkin lymphoma (HL) is paramount to maximize outcomes while avoiding unnecessary toxicity. We aimed to compare the performance of SUVmax reduction (ΔSUVmax%) and the PET ratio (rPET) versus the Deauville score (DS) for assessing the [...] Read more.
Tailoring treatment in patients with Hodgkin lymphoma (HL) is paramount to maximize outcomes while avoiding unnecessary toxicity. We aimed to compare the performance of SUVmax reduction (ΔSUVmax%) and the PET ratio (rPET) versus the Deauville score (DS) for assessing the chemotherapy response in pediatric HL patients undergoing 18F-FDG PET-CT. Fifty-two patients with biopsy-proven HL (aged 8–16 years) were enrolled at baseline, interim (after the second or third chemotherapy round) and post-therapy (on completion of first-line chemotherapy). Interim and post-therapy DS, ΔSUVmax% and rPET were compared as response predictors. Patients were classified as responders or non-responders based on a 24-month clinical follow-up. Interim DS showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 100%, 80.4%, 100%, 40% and 82.7%, respectively, in predicting the therapy response. Post-therapy DS showed a sensitivity, specificity, PPV, NPV and accuracy of 66.7%, 97.8%, 95.7%, 80% and 94.2%, repsectively. Interim ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 82.6%, 97.4%, 38.5% and 82.7%, respectively, with a 56.3% cutoff. Post-therapy ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 84.8%, 97.5%, 41.7% and 84.6%, respectively, with a 76.8% cutoff. Compared to ΔSUVmax%, DS showed a significantly higher sensitivity, specificity (p < 0.05) and NPV (p < 0.01). The sensitivity, specificity, PPV, NPV and accuracy of rPET in predicting the therapy response at 24 months were 76.1%, 100%, 100%, 35.3% and 78.8%, respectively, with a cut-off of 1.31. DS and rPET showed comparable predictive performance (p > 0.58). In conclusion, DS is an easier method with better performance than ΔSUVmax% and rPET in predicting the chemotherapy response in pediatric HL patients. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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Review

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17 pages, 579 KiB  
Review
Radiomics in Lung Metastases: A Systematic Review
by Michela Gabelloni, Lorenzo Faggioni, Roberta Fusco, Igino Simonetti, Federica De Muzio, Giuliana Giacobbe, Alessandra Borgheresi, Federico Bruno, Diletta Cozzi, Francesca Grassi, Mariano Scaglione, Andrea Giovagnoni, Antonio Barile, Vittorio Miele, Nicoletta Gandolfo and Vincenza Granata
J. Pers. Med. 2023, 13(2), 225; https://doi.org/10.3390/jpm13020225 - 27 Jan 2023
Cited by 6 | Viewed by 2248
Abstract
Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful [...] Read more.
Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature. Full article
(This article belongs to the Special Issue Imaging Biomarkers in Oncology)
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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 1965
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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