Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction.

Wavelet-based CNN for Predicting PAP Adherence Using Overnight Polysomnography Recordings: a Pilot Study.

Obstructive sleep apnea (OSA) is a common sleep disorder. Positive airway pressure (PAP) therapy is the first-line treatment, while its effectiveness is significantly limited by incomplete adherence in many patients. This work aims to find a predictive association between data from in-laboratory sleep studies during treatment (PAP titration polysomnogram, or PSG) and PAP adherence. Based on a PAP titration PSG database, we present a pipeline to develop a wavelet-based deep learning model and address two challenges. First, to tackle the problem of extremely long overnight PSG signals, it randomly draws segments and extracts features locally. The global representation for the entire signal is achieved by local feature P-norm pooling. Second, to tackle the problem of limited dataset size, the pre-trained EfficienNet-B7 is used as an unsupervised feature extractor to transfer ImageNet knowledge to PSG signals in the wavelet domain. The trained pipeline achieves 78% balanced accuracy and 83% AUC on the test set using airflow and frontal EEG signals, which, we believe, is a compelling result as a pilot study.