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Imaging of Melanoma and Non-melanoma Skin Cancer: New Challenges in the Era of Immunotherapy and Precision Oncology

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 4957

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Special Issue Editors

Prof. Dr. Luca Filippi

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Guest Editor

Nuclear Medicine Unit, Santa Maria Goretti Hospital, viale Canova snc, 04100 Latina, Italy
Interests: FDG PET/CT; oncology; therapy
Special Issues, Collections and Topics in MDPI journals

Dr. Barbara Palumbo

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Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Perugia, Italy
Interests: nuclear medicine; image-based diagnostics; artificial intelligence; PET/CT; SPECT; SPECT/CT; radiomics; oncology; neurodegenerative disorders
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Prof. Dr. Orazio Schillaci

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Division of Nuclear Medicine, Universita degli Studi di Roma Tor Vergata, Rome, Italy
Interests: molecular imaging; precision oncology; targeted therapy; theranostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Skin cancer, encompassing malignant melanoma (MM) and non-melanoma skin cancer (NMSC), represents the most common malignancy in the Caucasian population, with an upwards trend in recent years. In cases of advanced disease, therapeutic management remains challenging. In case of MM harboring the BRAF mutation, the combination of BRAF and MEK inhibitors has been widely applied in clinical practice.

Immunotherapy with immune checkpoint inhibitors has been recently introduced as an effective therapeutic option both for MM and NMSC. Since only 20–40% of patients respond to immunotherapy, it is of utmost importance to find ways to promptly identify responders and non-responders. In this scenario, several imaging techniques have been applied, such as CT and MRI, with promising results. Furthermore, PET/CT and PET/MRI with ¹⁸F-FDG have been utilized to study both tumor biology and metabolism in patients affected by skin cancer undergoing targeted and immunotherapies.

In recent years, several efforts have been made to develop novel radiopharmaceuticals suitable for the in vivo PET imaging of biomarkers associated with immunotherapy response, such as PD-1 and PDL-1 (i.e., immuno-PET), in order to help with patients’ pre-treatment selection and toward early recognition of resistance to therapy.

In this Special Issue, we encourage researchers to submit original papers, review articles, or interesting images on morphological and functional imaging approaches in patients affected by skin cancer undergoing immunotherapy and targeted therapy, with a particular emphasis on more innovative and emerging applications (i.e., radiomics, immuno-PET).

Prof. Dr. Luca Filippi
Dr. Barbara Palumbo
Prof. Dr. Orazio Schillaci
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. Diagnostics 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 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

precision medicine
oncology
immunotherapy
targeted therapy
malignant melanoma
non-melanoma skin cancer

Published Papers (2 papers)

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Research

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9 pages, 257 KiB

Open AccessArticle

Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment

by Anthime Flaus, Vincent Habouzit, Nicolas de Leiris, Jean-Philippe Vuillez, Marie-Thérèse Leccia, Mathilde Simonson, Jean-Luc Perrot, Florent Cachin and Nathalie Prevot

Diagnostics 2022, 12(2), 388; https://doi.org/10.3390/diagnostics12020388 - 02 Feb 2022

Cited by 11 | Viewed by 1645

Abstract

(1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients’ levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-treatment 18F-FDG PET; (2) Methods: Fifty-six metastatic melanoma patients—without prior systemic treatment—were retrospectively included. Forty-five 18F-FDG PET-based radiomic features were computed and the top five features associated with the patient’s outcome were selected. The analyzed machine learning classifiers were random forest (RF), neural network, naive Bayes, logistic regression and support vector machine. The receiver operating characteristic curve was used to compare model performances, which were validated by cross-validation; (3) Results: The RF model obtained the best performance after validation to predict OS and PFS and presented AUC, sensitivities and specificities (IC95%) of 0.87 ± 0.1, 0.79 ± 0.11 and 0.95 ± 0.06 for OS and 0.9 ± 0.07, 0.88 ± 0.09 and 0.91 ± 0.08 for PFS, respectively. (4) Conclusion: A RF classifier, based on pretreatment 18F-FDG PET radiomic features may be useful for predicting the survival status for melanoma patients, after one year of a first line systemic treatment by immunotherapy. Full article

(This article belongs to the Special Issue Imaging of Melanoma and Non-melanoma Skin Cancer: New Challenges in the Era of Immunotherapy and Precision Oncology)

Review

Jump to: Research

22 pages, 1591 KiB

Open AccessReview

The Role and Potential of ¹⁸F-FDG PET/CT in Malignant Melanoma: Prognostication, Monitoring Response to Targeted and Immunotherapy, and Radiomics

by Luca Filippi, Francesco Bianconi, Orazio Schillaci, Angela Spanu and Barbara Palumbo

Diagnostics 2022, 12(4), 929; https://doi.org/10.3390/diagnostics12040929 - 08 Apr 2022

Cited by 12 | Viewed by 2764

Abstract

Novel therapeutic approaches, consisting of immune check-point inhibitors (ICIs) and molecularly targeted therapy, have thoroughly changed the clinical management of malignant melanoma (MM), the most frequent and deadly skin cancer. Since only 30–40% of MM patients respond to ICIs, imaging biomarkers suitable for the pre-therapeutic stratification and response assessment are warmly welcome. In this scenario, positron emission computed tomography (PET/CT) with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) has been successfully utilized for advanced MM staging and therapy response evaluation. Furthermore, several PET-derived parameters (SUVmax, MTV, TLG) were particularly impactful for the prognostic evaluation of patients submitted to targeted and immunotherapy. In this review, we performed a web-based and desktop research on the clinical applications of ¹⁸F-FDG PET/CT in MM, with a particular emphasis on the various metabolic criteria developed for interpreting PET/CT scan in patients undergoing immunotherapy or targeted therapy or a combination of both. Furthermore, the emerging role of radiomics, a quantitative approach to medical imaging applying analysis methodology derived by the field of artificial intelligence, was examined in the peculiar context, putting a particular emphasis on the potential of this discipline to support clinicians in the delicate process of building patient-tailored pathways of care. Full article

(This article belongs to the Special Issue Imaging of Melanoma and Non-melanoma Skin Cancer: New Challenges in the Era of Immunotherapy and Precision Oncology)

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