• 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 "L. Vanvitelli", Naples, Italy


    Dr. Valerio Nardone E-Mail

    Associate Professor, Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy


    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:

    CNS

    Head and neck

    Breast

    Hematology

    Upper GI (oesophagus, stomach, pancreas, liver)

    Lung

    Gynaecological (endometrium, cervix, vagina, vulva)

    Lower GI (colon, rectum, anus)

    Non-prostate urology

    Prostate

    Sarcoma

    Skin cancer/malignant melanoma

    Palliation

    Pediatric tumours

    Elderly oncology

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

    Call for Papers

    Published Articles

    Open Access
    Original Article
    Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer
    Aim: To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). Methods: Fr [...] Read more.
    Azadeh Tabari ... Dania Daye
    Published: February 19, 2024 Explor Target Antitumor Ther. 2024;5:74–84
    DOI: https://doi.org/10.37349/etat.2024.00205
    View:405
    Download:16
    Times Cited: 0
    Open Access
    Review
    Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer
    Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond f [...] Read more.
    Kriti Das ... Chakresh Kumar Jain
    Published: December 27, 2023 Explor Target Antitumor Ther. 2023;4:1286–1300
    DOI: https://doi.org/10.37349/etat.2023.00197
    Open Access
    Systematic Review
    Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature
    Aim: Artificial intelligence (AI) is a new field of science in which computers will provide decisions-supporting tools to help doctors make difficult clinical choices. Recent AI applications in o [...] Read more.
    Antonella Loperfido ... Gianluca Bellocchi
    Published: October 24, 2023 Explor Target Antitumor Ther. 2023;4:933–940
    DOI: https://doi.org/10.37349/etat.2023.00174
    View:777
    Download:17
    Times Cited: 0
    Open Access
    Perspective
    Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy
    Precision oncology is a rapidly evolving field that uses advanced technologies to deliver personalized cancer care based on a patient’s unique genetic and clinical profile. The use of artificial i [...] Read more.
    Bahareh Farasati Far
    Published: August 31, 2023 Explor Target Antitumor Ther. 2023;4:685–689
    DOI: https://doi.org/10.37349/etat.2023.00160
    Open Access
    Review
    Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology
    Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts w [...] Read more.
    Ashish Kumar Jha ... Andre Dekker
    Published: August 24, 2023 Explor Target Antitumor Ther. 2023;4:569–582
    DOI: https://doi.org/10.37349/etat.2023.00153
    Open Access
    Review
    Current role of machine learning and radiogenomics in precision neuro-oncology
    In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI a [...] Read more.
    Teresa Perillo ... Andrea Manto
    Published: July 19, 2023 Explor Target Antitumor Ther. 2023;4:545–555
    DOI: https://doi.org/10.37349/etat.2023.00151
    View:718
    Download:36
    Times Cited: 0
    Open Access
    Review
    Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review
    Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, me [...] Read more.
    Raffaele Natella ... Antonella Santone
    Published: June 30, 2023 Explor Target Antitumor Ther. 2023;4:498–510
    DOI: https://doi.org/10.37349/etat.2023.00147
    View:783
    Download:24
    Times Cited: 0
    Open Access
    Review
    Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review
    Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal ca [...] Read more.
    Giuseppe Di Costanzo ... Enrico Cavaglià
    Published: June 30, 2023 Explor Target Antitumor Ther. 2023;4:406–421
    DOI: https://doi.org/10.37349/etat.2023.00142
    Open Access
    Review
    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
    View:1125
    Download:37
    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
    View:1495
    Download:36
    Times Cited: 0
    Open Access
    Review
    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
    View:1068
    Download:29
    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
    Open Access
    Review
    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
    View:1701
    Download:58
    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
    View:1597
    Download:34