• Special Issue Topic

    Artificial Intelligence for Precision Oncology

    Submission Deadline: March 31, 2023

    Guest Editors

    Dr. Alfonso Reginelli E-Mail

    Associate Professor, Department of Precision Medicine, University of Campania

    Dr. Valerio Nardone E-Mail

    Associate Professor, Department of Precision Medicine, University of Campania

    About the Special Issue

    Recently, precision oncology has seen raised interest, thanks to the huge advances in technologies and knowledge of both the human body and cancer disease.

    Precision Oncology requires the molecular profiling of tumors to identify targetable alterations, and is rapidly developing and has entered the mainstream of clinical practice.

    In this context, precision oncology represents an opportunity to provide far more tailored treatments, taking into consideration that particular attributes and characteristics are unique for patients.

    In the fields of imaging, that involve both radiology and radiation oncology, the corresponding concept is represented by the image-guided precision medicine, defined as the use of any form of medical imaging to plan, perform, and evaluate procedures and interventions.

    The cross-sectional digital imaging modalities magnetic resonance imaging (MRI) and computed tomography (CT) are the most commonly used modalities of image-guided therapy. These procedures are also supported by ultrasound, angiography, surgical navigation equipment, tracking tools, and integration software.

    At the same time, recent developments in radiotherapy with the incorporation of intensity-modulated radiotherapy, molecular imaging-guided radiotherapy, adaptive radiotherapy, and proton therapy have always included image-guided approaches in the clinical workflow.

    Last (but not least), artificial intelligence can also be included in this context, as a method that is reshaping the existing scenario of precision oncology, aiming at integrating the large amount of data derived from multi-omics analyses with current advances in high-performance computing and groundbreaking deep-learning strategies. 

    For this Special Issue, we welcome basic translational and clinical research papers, cancer biomarkers, professional opinions, and reviews in the broad field of Artificial Intelligence for Precision Oncology in the following categories:


    Head and neck



    Upper GI (oesophagus, stomach, pancreas, liver)


    Gynaecological (endometrium, cervix, vagina, vulva)

    Lower GI (colon, rectum, anus)

    Non-prostate urology



    Skin cancer/malignant melanoma


    Pediatric tumours

    Elderly oncology

    Keywords: Precision oncology; artificial intelligence; radiomics; radiotherapy; radiology; precision medicine

    Call for Papers

    Published Articles

    Open Access
    Role of artificial intelligence in oncologic emergencies: a narrative review
    Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathol [...] Read more.
    Salvatore Claudio Fanni ... Emanuele Neri
    Published: April 28, 2023 Explor Target Antitumor Ther. 2023;4:344–354
    DOI: https://doi.org/10.37349/etat.2023.00138
    Times Cited: 0
    Open Access
    Original Article
    Development and validation of an infrared-artificial intelligence software for breast cancer detection
    Aim: In countries where access to mammography equipment and skilled personnel is limited, most breast cancer (BC) cases are detected in locally advanced stages. Infrared breast thermography is reco [...] Read more.
    Enrique Martín-Del-Campo-Mena ... Yessica González-Mejía
    Published: April 27, 2023 Explor Target Antitumor Ther. 2023;4:294–306
    DOI: https://doi.org/10.37349/etat.2023.00135
    Times Cited: 0
    Open Access
    Artificial intelligence applications in pediatric oncology diagnosis
    Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subse [...] Read more.
    Yuhan Yang ... Yuan Li
    Published: February 28, 2023 Explor Target Antitumor Ther. 2023;4:157–169
    DOI: https://doi.org/10.37349/etat.2023.00127
    Times Cited: 0
    Open Access
    Original Article
    Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy
    Aim: The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are i [...] Read more.
    Tuan D. Pham ... Xiao-Feng Sun
    Published: February 07, 2023 Explor Target Antitumor Ther. 2023;4:1–16
    DOI: https://doi.org/10.37349/etat.2023.00119
    Times Cited: 0
    Open Access
    Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis—a narrative review
    The advent of artificial intelligence (AI) represents a real game changer in today’s landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to acceler [...] Read more.
    Maurizio Cè ... Michaela Cellina
    Published: December 27, 2022 Explor Target Antitumor Ther. 2022;3:795–816
    DOI: https://doi.org/10.37349/etat.2022.00113
    Times Cited: 0
    Open Access
    Systematic Review
    Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review
    Aim: Head and neck squamous cell cancer (HNSCC) is the ninth most common tumor worldwide. Neck lymph node (LN) status is the major indicator of prognosis in all head and neck cancers, and the early detection of LN involvement is c [...] Read more.
    Maria Paola Belfiore ... Salvatore Cappabianca
    Published: December 13, 2022 Explor Target Antitumor Ther. 2022;3:734–745
    DOI: https://doi.org/10.37349/etat.2022.00110
    Times Cited: 0