A Role for FDG PET Radiomics in Personalized Medicine?

Abstract

Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.

Figure 1. From [23].

Convolutional neural network architecture for esophageal cancer ¹⁸F-FDG PET data in a vector composed from 4 convolutional (U) and 4 max pooling (V) layers. Different colour arrows in the first convolutional layer represent different learnable weight matrices. Coloured squares in the feature maps represent elements that include local spatial information from the previous layer. h – hidden layer, y_i – responder, y_k – nonresponder.

Figure 2. ¹⁸F-FDG PET/CT of a highly metabolically active NSCLC (adenocarcinoma).

Specific values of several relevant variables are shown in the inset (top). The Hematoxylin slide (bottom left) shows a dense tumor cell population corresponding to the high SUVmean and negative skew of the ¹⁸F-FDG PET image. Binary image (bottom right) thresholded to differentiate high and low mean cell density (MCD) regions shows low pathological lacunarity (path-lac) as smaller sized black regions of low cellularity between white regions of high cellularity.

Figure 3. From [72].

ROC curves for baseline ¹⁸F-FDG PET primary tumor coarseness, contrast, busyness, and complexity for identification of responders vs nonresponders in patients treated with chemoradiotherapy for NSCLC. [This research was originally published in JNM. Cook GJ, Yip C, Siddique M et al. Are Pretreatment ¹⁸F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? J Nucl Med. 2013;54:19-26. © by the Society of Nuclear Medicine and Molecular Imaging, Inc.]