PET/CT in lung cancer analysis assisted with radiomic-based features

the image concerning on PET/CT in lung cancer analysis assisted with radiomics


Improving lung cancer diagnosis from medical image data is a key factor of the screening process or – at a later stage – of monitoring the disease. Although structural tumor information can be observed in structural imaging, such as computed tomography (CT), understanding the physiological activity in functional imaging may be pivotal to perform malignancy of the tumor differentiation. As an example, positron emission tomography (PET) allows us to measure the glucose uptake, which indicates the metabolism of the tissue and may help us identify active lesions (unfortunately, PET images are of poor spatial resolution, and they do not reveal much of the anatomical details). CT scans are used to verify the hot spots based on the human body atlas, but they also can serve as an exciting source of “invisible” information about the lesion. We wanted to quantify the characteristics of the tumor (e.g., its volume), or extract additional information which may not be visible to the naked eye.

What we did

We conducted a study using radiomic features elaborated from the image which could correlate with patient’s clinical parameters. We have shown that textural parameters can be used to predict survival of the patient (p=0.028) – the filtration-histogram technique (TexRAD Ltd,, part of Feedback Plc, Cambridge, UK) was utilized to reveal such image features.

Extracting radiomic-based features from high-uptake lesions

Figure 1: Extracting radiomic-based features from high-uptake lesions requires their accurate delineation – they are clearly visible in b) PET, and once they are segmented there, they may be easily transferred to the a)-c) co-registered CT to extract d) “invisible” textural information from CT.

Our approach, referred to as LUNGCX, required no user intervention and was 100% repeatable – starting from the segmentation of lungs in CT [1], delineating lesions in the lung areas in PET through fairly simple thresholding, and extracting biomarkers from CT using texture analysis.


The results were presented at RSNA 2016 in an Informatics poster discussion as well as published: Jakub Nalepa et al: “PET/CT in Lung Cancer: An Automated Imaging Tool for Decision Support”, Proc. Radiological Society of North America Meeting (RSNA), Chicago, USA, 2016.


[1] Our segmentation approach was described in more detail in the article: Jakub Nalepa, Michal Czardybon, Maksym Walczak: „ Real-time lung segmentation from whole-body CT scans using Adaptive Vision Studio: a visual programming software suite”, Proc. SPIE Real-Time Image and Video Processing 2018 10670, 93-103, 2018.