Deep segmentation networks predict survival of non-small cell lung cancer

Abstract

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 for 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 method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs 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 pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and 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, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Radiomics for Prediction of Radiation-induced Lung Injury After Robotic Stereotactic Body Radiotherapy of Lung Cancer: Results From Two Independent Institutions

“…The strati cation resulted in two groups with highly signi cant different risk for recurrence in both the training and test set [54]. Also in another study where a CNN was applied to both CT and PET images, a highly accurate classi cation of survival probability was achieved in an independent data set [21].…”

“…Radiomics aims at extraction of biomarkers from high-dimensional analysis of digital images and has been extensively studied in lung cancer by using computed tomography (CT) or Fluor-Deoxyglucose Positron Emission Tomography (FDG-PET) of the chest [15][16][17][18][19][20]. Several studies have applied radiomic analysis in SBRT of NSCLC [21][22][23][24][25][26][27][28][29][30][31][32][33][34], but so far, the clinical impact of the developed algorithms has been low due to low reproducibility of the results [35], lack of standardization of the extracted radiomic features and lack of external validation on data from other institutions.…”

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“… FC-Densenet103, Unet, DenseNet, and DenseNet121-FPN. References References [ 137 ] [ 138 ] [ 139 ] [ 140 ] [ 141 ] [ 142 ] [ 143 ] [ 129 ] [ 144 ] [ 145 ] [ 146 ] [ 147 ] [ 148 ] [ 149 ] [ 150 ] [ 151 ] [ 152 ] [ 153 ] [ 154 ] [ 155 ] [ 156 ] [ 157 ] [ 158 ] [ 159 ] [ 160 ] [ 161 ] [ 162 ] [ 163 ] [ 164 ] [ 165 ] Classification Characteristics Characteristics Gray scale feature extraction and ML classifier, and model-based techniques. Resnet-50, CNN, SVM, ResNet101, VGG16, and VGG19.…”

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

“…2OS and 2DS stand for 2-year overall survival and 2year disease-specific survival, respectively. Reprinted with permission from Springer Nature (Baek et al 2019) Arabi and Zaidi European Journal of Hybrid Imaging (2020) 4:17 solutions is still far from full-scale implementation in clinical setting. Currently, AIbased solutions could only assist experts to create a synergy between humans' expertise and machines' capacity.…”