Elsevier

Methods

Volume 188, April 2021, Pages 122-132
Methods

PET/CT radiomics in breast cancer: Mind the step

https://doi.org/10.1016/j.ymeth.2020.01.007Get rights and content

Highlights

  • Major limitations affect PET/CT radiomics studies in breast cancer.

  • PET radiomics need collaborative research, standardization and transparency.

  • Clinical need and sound study design should guide breast cancer radiomics research.

  • Clinical, technical and analysis issues need curating within the radiomics workflow.

Abstract

The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms “breast”, “radiomic”, “PET”, “radiomics”, “texture”, and “textural” were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.

Introduction

An estimated risk or probability of the presence of a specific disease or condition (diagnostic setting) or of a specific future outcome (prognostic setting) provided by a prediction model determines treatment and follow-up decisions. However, studies developing or validating multivariable prediction models based on a radiomics approach have been found to be of insufficient quality, since information on all aspects of model development has been incomplete [1]. Such limitations ultimately lead to prediction models that cannot be implemented in clinical practice.

In this context, however, biomarkers of tumor aggressiveness and potential resistance to medical treatments would be of fundamental importance in order to inform treatment decisions. Novel approaches are necessary to avoid potentially toxic and ineffective chemotherapies, to target aggressive cancer foci, and to develop patient-tailored therapeutic approaches.

Imaging, which allows non-invasive, repeatable, whole-body assessment, has the potential to provide image-derived biomarkers. Numerous parameters (features) can be extracted by applying mathematical methods describing the relationships between the intensity of the pixels or voxels and their position within the region of interest. Identification of correlations between these potential biomarkers and relevant outcomes represents the essence of radiomics. This approach has been evaluated on different imaging modalities and appears promising in breast cancer (BC) [2]. BC is the second most common cancer in the global population and the most frequent cancer occurring in women worldwide. Although overall survival rates are improving globally thanks to better screening programs and early stage diagnosis, it still represents the fifth most common cause of female death [3]. Diagnosis and tumor biology characterization, staging, treatment response, and outcome prediction represent the unmet needs in BC patient management. The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in BC, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions.

Section snippets

Search, eligibility criteria, and study selection

Combinations of the terms “breast” AND “radiomic” AND “PET”, “breast” AND “radiomics” AND ”PET“, ”breast“ AND “texture” AND ”PET“, and ”breast“ AND “textural” AND ”PET“ were used for the literature search within the PubMed/MEDLINE database. No start date limit was used, and the search was extended until 8 July 2019. We applied the following inclusion criteria: (a) articles on breast cancer and (b) articles on texture analysis derived from PET/CT. We excluded (a) articles not in English language;

Diagnosis

Currently, definitive diagnosis of suspicious breast nodules relies on core-needle biopsy or surgery. A commonly encountered diagnostic dilemma is nodule classification as benign vs malignant, especially with respect to those lesions that are classified as probably benign (BI-RADS 3) at imaging [4]. Vogl et al. [5] investigated the potential of textural features (TFs) derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and 18

Discussion

Despite encouraging results, the included studies are far from providing definitive conclusions. Radiomics investigations include clinical, technical, and analysis steps, starting with identification of the clinical need, hypothesis generation, and study design, and proceeding to image acquisition and reconstruction, segmentation of the region of interest, processing of the image, actual computation of the texture parameters, collection of other relevant data (clinical parameters, outcome,

Conclusions

We reviewed up-to-date literature on the role of PET radiomics in breast cancer, focusing on methodological aspects of the radiomics workflow and possible pitfalls. Variations in acquisition, reconstruction, segmentation, and radiomics processing were found among studies. Therefore, most of the current evidence on the clinical role is at feasibility level. The shortcomings discussed in the review can be mitigated when corrective strategies are applied. Such strategies lie mainly in the use of

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

MK PhD scholarship was funded by the Italian Association for Research on Cancer (AIRC) grant number IG-2016-18585.

Funding

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

Ethical statement

In view of the design of the present study (i.e., review), ethical approval was considered unnecessary.

Author contributions

MS and MK conceptualized the study; MS and GN participated in data collection and selection; MS, LCozzi, GN, and LCavinato performed data analysis; MK and MS supervised data selection; LA, MS, LCozzi, GN, LCavinato, and MK drafted the paper; AC supervised the activities; all the authors read, commented on, and approved the manuscript.

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