Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung
cancer diagnoses and is the leading cause of cancer-related death worldwide.
Recent studies indicate that image-based radiomics features from positron
emission tomography-computed tomography (PET/CT) images have predictive power
on NSCLC outcomes. To this end, easily calculated functional features such as
the maximum and the mean of standard uptake value (SUV) and total lesion
glycolysis (TLG) are most commonly used for NSCLC prognostication, but their
prognostic value remains controversial. Meanwhile, convolutional neural
networks (CNN) are rapidly emerging as a new premise for cancer image analysis,
with significantly enhanced predictive power compared to other hand-crafted
radiomics features. Here we show that CNN trained to perform the tumor
segmentation task, with no other information than physician contours, identify
a rich set of survival-related image features with remarkable prognostic value.
In a retrospective study on 96 NSCLC patients before stereotactic-body
radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net)
trained for tumor segmentation in PET/CT images, contained features having
strong correlation with 2- and 5-year overall and disease-specific survivals.
The U-net algorithm has not seen any other clinical information (e.g. survival,
age, smoking history) than the images and the corresponding tumor contours
provided by physicians. Furthermore, through visualization of the U-Net, we
also found convincing evidence that the regions of progression appear to match
with the regions where the U-Net features identified patterns that predicted
higher likelihood of death. We anticipate our findings will be a starting point
for more sophisticated non-intrusive patient specific cancer prognosis
determination
