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Table of Contents
REVIEW ARTICLE
Year : 2020  |  Volume : 1  |  Issue : 1  |  Page : 27-28

Radiomics: Potential tool to titrate the radiation dose


Mahaveer Cancer Centre, Patna, India

Date of Web Publication22-Oct-2020

Correspondence Address:
Dr. R Chauhan
Department of Radiation Oncology, Mahavir Cancer Centre
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/WKMP-0197.298268

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How to cite this article:
Chauhan R. Radiomics: Potential tool to titrate the radiation dose. J Precis Oncol 2020;1:27-8

How to cite this URL:
Chauhan R. Radiomics: Potential tool to titrate the radiation dose. J Precis Oncol [serial online] 2020 [cited 2020 Nov 25];1:27-8. Available from: https://www.jprecisiononcology.com/text.asp?2020/1/1/27/298268




  Introduction Top


Radiation therapy is an integral part of cancer treatment, and it has been estimated that over 60% of cancer patients require radiation therapy as part of their management protocol (1). As radiation affects both tumour cells and surrounding normal cells, we need to precisely balance the dose delivery to achieve our target of maximal tumour kill with minimum damage to the surrounding tissue. Though both physics and biology are an integral part of radiotherapy, one must admit that medical radiation physics has dominated the field of radiation oncology to date. All the recent developments in clinical radiation oncology have focused on techniques to improve conformity and delivery of radiation beams like 3 Dimensional Conformal Radiotherapy (3DCRT), Intensity Modulated Radiotherapy (IMRT), Image-Guided Radiotherapy (IGRT), Stereotactic Radiotherapy (SRT), Stereotactic Body Radiotherapy (SBRT), Proton therapy etc (2). With these new techniques, we have come a long way to better delivery of planned radiation dose to the normal tissues with excellent sparing of surrounding normal tissues. Unfortunately, there hasn't been a parallel improvement in the overall survival rate in many cancers. One of the probable reasons could be the use of fixed standard radiation protocol for all patients.

Currently, treatment decisions do not take into account individual patient's or tumour's sensitivities to radiation and are treated as a statistically average person. Based on randomized clinical trials and meta-analyses radiation dose is delivered as an average, to bring about a specific effect in the form of tumour kill and with certain known side effects (3). As a result, patients treated with radiation experience a considerable variation in tumour response and normal tissue toxicity. With the development of molecular biology, we now know that every tumour is made up of a genetically heterogeneous group of cancer cells. They have aggressive intratumoural sub-regions, differential levels of oxygenation & blood flow, pH etc. resulting in variations insensitivity or resistance to radiation or drugs (4). Even the mechanism of developing resistance to radiation may be common in some tumours and different in others. Large scale data from genomics and proteomics has now led to the development of molecular staging in many cancers. These markers are reliable predictors of outcome and also acts as a guide for the selection of both chemotherapy drugs and targeted therapy (5). While, on the one hand, there are many prognostic and predictive biomarkers for chemo-selection, there are only a few biomarkers in radiation oncology. Stratification of patients based on radiation sensitivities will allow delivery of the adequate amount of dose, which could be either an escalation or de-escalation for aggressive and sensitive tumours respectively (6).


  Discussion Top


Genomics adjusted radiation dose (GARD) is a recent development in radiotherapy and provides information on the result of certain defined dose of radiation to the given tumour (7). While the genomics and proteomics-based approach towards individualization of radiation therapy are undoubtedly valuable, there is still an unmet need for methods that allow for a more comprehensive disease characterization, and reliable prediction of disease response to a certain amount of radiation dose. Radiomics has recently emerged as a promising tool for discovering new imaging biomarkers. In this technique, various features like shape, texture etc. is extracted from clinical images and then they are combined with bioinformatics tool and models to comprehensively characterize the tumour phenotypes, known as radiomics signature (8). Another advantage of this technique is that it can be applied to various routinely used clinical imaging modalities like Computed Tomography (CT) Scan, Magnetic Resonance Imaging (MRI), or Positron Emission Tomography - Computed Tomography (PET-CT) Scan (9). Easy availability, accessibility, affordability and a better concept of radiology amongst clinicians, genomics may make the approach of using radiomics to guide precision radiotherapy a more feasible and cost-effective option than genomics adjusted radiation dose.

The field of radiomics has made tremendous progress in the past few years. Many studies have identified and proposed radiomics signature for predicting response to radiation/ chemoradiation and overall survival (10). Aertis et al. extracted more than 400 qualitative features from CT images to describe tumour intensity, shape, texture etc. to design radiomics signature for predicting survival in lung cancer patients being treated with radiotherapy. Their radiomics signature captured intratumor heterogeneity and was further validated in head and neck cancer cohort of patients (11). MRI features have also been investigated for radiomics analyses and found to correlate with survival and progression in patients with glioblastoma (12). Wu et al. worked on the imaging features of FDG-PET and CT for predicting distant metastases in early non-small cell lung cancer patients treated with SBRT. Combining the morphological and intratumoural metastatic heterogeneity, they proposed a radiomic signature which had better prognostic value than conventional imaging (13). In a similar study, Cui et al. proposed a radiomics signature for predicting overall survival in 139 locally advanced pancreatic cancer patients treated with SBRT based on their FDG-PET scans (14).

Further, Van Rossum et al. investigated baseline and post chemoradiation FDG-PET scans of oesophagal cancer patients treated with neoadjuvant chemoradiation to predict for complete pathological response (15). Another study by Gatenby et al. showed that they could be used to divide the whole tumour into multiple regional habitats with defined high risks sub-region containing aggressive cancer cells addressed with dose intensification [16]. Taken together, these studies support the need for tumour partitioning to identify aggressive subregions.


  Conclusion Top


Recent advances have brought together several cross-disciplinary areas such as genomics, radiomics to rapidly assess tumour heterogeneity and their subsequent response to radiation. We need individual research thoughts to develop and validate new radiation protocols incorporating the use of radiomics biomarkers to make fine adjustments to radiation dose, improving patient outcome and quality of life.



 
  References Top

1.
Halperin EC, Perez CA, Brady LW. Perez and Brady's principles and practice of radiation oncology, 5th ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2008.  Back to cited text no. 1
    
2.
Garibaldi C, Jereczek-Fossa BA, Marvaso G, et al. Recent advances in radiation oncology. E cancer medicalscience. 2017 Nov 30;11:785.  Back to cited text no. 2
    
3.
Bibault, J., Fumagalli, I., Ferté, C. et al. Personalized radiation therapy and biomarker-driven treatment strategies: a systematic review. Cancer Metastasis.2013;32: 479–492.  Back to cited text no. 3
    
4.
Heppner GH, Shekhar M. Tumour Heterogeneity Is Fundamental to the Tumour Ecosystem. Oncology-Ny. 2014;28:780–1.  Back to cited text no. 4
    
5.
Wang L, Wheeler DA. Genomic sequencing for cancer diagnosis and therapy. Annu Rev Med. 2014;65:33–48.  Back to cited text no. 5
    
6.
Ree AH, Redalen KR. Personalized radiotherapy: concepts, biomarkers and trial design. Br J Radiol. 2015 Jul;88(1051):20150009.  Back to cited text no. 6
    
7.
Scott JG, Berglund A, Schell MJ, et al. A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study. Lancet Oncol. 2017;18:202–211.  Back to cited text no. 7
    
8.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016; 278(2):563-57  Back to cited text no. 8
    
9.
Wu J, Tha KK, Xing L, et al. Radiomics and radiogenomics for precision radiotherapy. Journal of Radiation Research. 2018 March;59(1):25-31  Back to cited text no. 9
    
10.
Avanzo M, Stancanello J, El Naqa I. Beyond imaging: the promise of radiomics. Phys Med. 2017;38:122–39.  Back to cited text no. 10
    
11.
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.  Back to cited text no. 11
    
12.
Grossmann P, Narayan V, Chang K, et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol. 2017;19:1688–97.  Back to cited text no. 12
    
13.
Wu J, Aguilera T, Shultz D, et al. Early-stage non–small cell lung cancer: quantitative imaging characteristics of 18F fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology. 2016;281:270–8.  Back to cited text no. 13
    
14.
Cui Y, Song J, Pollom E, et al. Quantitative analysis of 18 F-fluorodeoxyglucose positron emission tomography identifies novel prognostic imaging biomarkers in locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2016;96:102–9  Back to cited text no. 14
    
15.
van Rossum PS, Fried DV, Zhang L, et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med. 2016;57(5):691–700.  Back to cited text no. 15
    
16.
Zhou M, Hall L, Goldgof D, et al. Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol 2014;7:5–13.  Back to cited text no. 16
    




 

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