Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

Author:

Jha Ashish Kumar1ORCID,Mithun Sneha1ORCID,Sherkhane Umeshkumar B.2,Dwivedi Pooj3,Puts Senders4,Osong Biche4,Traverso Alberto4ORCID,Purandare Nilendu5,Wee Leonard4ORCID,Rangarajan Venkatesh5ORCID,Dekker Andre4ORCID

Affiliation:

1. Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India; Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India

2. Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India

3. Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India; Department of Nuclear Medicine, Advance Center for Treatment, Research, Education in Cancer, Kharghar, Navi-Mumbai 410210, Maharashtra, India

4. Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands

5. Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India; Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India

Abstract

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 with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

Publisher

Open Exploration Publishing

Subject

Cancer Research,Oncology

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