Contents
Special Issue Topic

Artificial Intelligence for Precision Oncology

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 lssue

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

Published Articles

Open Access Perspective
Artificial intelligence and classification of mature lymphoid neoplasms
Hematologists, geneticists, and clinicians came to a multidisciplinary agreement on the classification of lymphoid neoplasms that combines clinical features, histological characteristics, immunophen
Published: April 23, 2024 Explor Target Antitumor Ther. 2024;5:332–348
2327 47 4
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
Published: February 19, 2024 Explor Target Antitumor Ther. 2024;5:74–84
1470 25 1
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
Published: December 27, 2023 Explor Target Antitumor Ther. 2023;4:1286–1300
2176 32 3
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
Published: October 24, 2023 Explor Target Antitumor Ther. 2023;4:933–940
2468 43 9
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
Published: August 31, 2023 Explor Target Antitumor Ther. 2023;4:685–689
2184 46 12
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
Published: August 24, 2023 Explor Target Antitumor Ther. 2023;4:569–582
2707 30 9
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
Published: July 19, 2023 Explor Target Antitumor Ther. 2023;4:545–555
1835 41 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
Published: June 30, 2023 Explor Target Antitumor Ther. 2023;4:498–510
3498 50 5
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
Published: June 30, 2023 Explor Target Antitumor Ther. 2023;4:406–421
2238 42 7
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
Published: April 28, 2023 Explor Target Antitumor Ther. 2023;4:344–354
2302 45 4
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
Published: April 27, 2023 Explor Target Antitumor Ther. 2023;4:294–306
3329 48 1
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 subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.
Published: February 28, 2023 Explor Target Antitumor Ther. 2023;4:157–169
2700 58 10
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
Published: February 07, 2023 Explor Target Antitumor Ther. 2023;4:1–16
1947 40 5
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 accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols.
Published: December 27, 2022 Explor Target Antitumor Ther. 2022;3:795–816
4353 97 17
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 crucial in terms of therapy and prognosis. Diffusion-weighted imaging (DWI) is a non-invasive imaging technique used in magnetic resonance imaging (MRI) to characterize tissues based on the displacement motion of water molecules. This review aims to provide an overview of the current literature concerning quantitative diffusion imaging for LN staging in patients with HNSCC. Methods: This systematic review performed a literature search on the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) for all relevant, peer-reviewed literature on the subject following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria, using the keywords: DWI, MRI, head and neck, staging, lymph node. Results: After excluding reviews, meta-analyses, case reports, and bibliometric studies, 18 relevant papers out of the 567 retrieved were selected for analysis. Conclusions: DWI improves the diagnosis, treatment planning, treatment response evaluation, and overall management of patients affected by HNSCC. More robust data to clarify the role of apparent diffusion coefficient (ADC) and DWI parameters are needed to develop models for prognosis and prediction in HNSCC cancer using MRI.
Published: December 13, 2022 Explor Target Antitumor Ther. 2022;3:734–745
3805 41 4