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A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers

  • Abdominal Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.

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Abbreviations

3D:

Three-dimensional

ADC:

Apparent diffusion coefficient

AI:

Artificial intelligence

ANN:

Artificial neural network

ASD:

Average surface distance

AUC:

Area under the ROC curve

BC:

Breast cancer

BRCA1/2:

Breast cancer gene ½

C-index:

Concordance index

CAD:

Computer-aided detection

CNN:

Convolutional neural network

CRC:

Colorectal cancer

CT:

Computed tomography

CTC:

Computed tomography colonography

DBT:

Digital breast tomosynthesis

DL:

Deep learning

DSC:

Dice similarity coefficient

DWI:

Diffusion weighted imaging

EGFR( +)/EGFR( −):

Epidermal growth factor receptor positive/negative

KRAS:

Kirsten rat sarcoma viral oncogene homolog

HER-2:

Human epidermal growth factor receptor 2

HR:

Hazard ratio

LARC:

Locally advanced rectal cancer

LC:

Lung cancer

ML:

Machine learning

MRI:

Magnetic resonance imaging

nChRT:

Neoadjuvant chemoradiotherapy

NLST:

National lung screening trial

NSCLC:

Non-small cell lung cancer

OR:

Odds ratio

pCR:

Pathological complete response

PD-1:

Programmed cell death protein 1

PD-L1:

Programmed cell death ligand 1

RR:

Recall rate

RT:

Radiation therapy

TKIs:

Tyrosine kinase inhibitors

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Diagnostic Imaging Unit, Department of Medico-Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, ICOT Hospital, Via Franco Faggiana, 166804100, Latina, Italy

    Simone Vicini, Marco Rengo & Davide Bellini

  2. Department of Radiology, Fondazione I.R.C.C.S. Policlinico San Matteo, Viale Camillo Golgi, 19, 27100, Pavia, Italy

    Chandra Bortolotto, Daniela Ballerini & Lorenzo Preda

  3. Diagnostic Imaging Unit, Department of Radiological, Oncological and Pathological Sciences, “Sapienza” University of Rome, ICOT Hospital, Via Franco Faggiana, 1668, 04100, Latina, Italy

    Iacopo Carbone

  4. Diagnostic Imaging Unit Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035, 00189, Rome, Italy

    Andrea Laghi

  5. Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Via Albertoni 15, 40138, Bologna, Italy

    Francesca Coppola

  6. Department of Translational Research, Academic Radiology, University of Pisa, Via Roma, 67, 56126, Pisa, Italy

    Lorenzo Faggioni

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Contributions

MR and CEB conceptualized the study; SB and SP participated in data collection and image processing; NP and DB performed data analysis; AI participated in patient selection; MR supervised image processing, critically interpreted the results and drafted the paper; IC, AL and GS supervised the activities; and all the authors read, commented and approved the manuscript.

Corresponding author

Correspondence to Daniela Ballerini.

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Vicini, S., Bortolotto, C., Rengo, M. et al. A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol med 127, 819–836 (2022). https://doi.org/10.1007/s11547-022-01512-6

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  • DOI: https://doi.org/10.1007/s11547-022-01512-6

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