Differentiation of glioblastoma from solitary brain metastasis using deep ensembles: Empirical estimation of uncertainty for clinical reliability.

BACKGROUND AND OBJECTIVES: To develop a clinically reliable deep learning model to differentiate glioblastoma (GBM) from solitary brain metastasis (SBM) by providing predictive uncertainty estimates and interpretability. METHODS: A total of 469 patients (300 GBM, 169 SBM) were enrolled in the institutional training set. Deep ensembles based on DenseNet121 were trained on multiparametric MRI. The model performance was validated in the external test set consisting of 143 patients (101 GBM, 42 SBM). Entropy values for each input were evaluated for uncertainty measurement; based on entropy values, the datasets were split to high- and low-uncertainty groups. In addition, entropy values of out-of-distribution (OOD) data from unknown class (257 patients with meningioma) were compared to assess uncertainty estimates of the model. The model interpretability was further evaluated by localization accuracy of the model. RESULTS: On external test set, the area under the curve (AUC), accuracy, sensitivity and specificity of the deep ensembles were 0.83 (95 % confidence interval [CI] 0.76-0.90), 76.2 %, 54.8 % and 85.2 %, respectively. The performance was higher in the low-uncertainty group than in the high-uncertainty group, with AUCs of 0.91 (95 % CI 0.83-0.98) and 0.58 (95 % CI 0.44-0.71), indicating that assessment of uncertainty with entropy values ascertained reliable prediction in the low-uncertainty group. Further, deep ensembles classified a high proportion (90.7 %) of predictions on OOD data to be uncertain, showing robustness in dataset shift. Interpretability evaluated by localization accuracy provided further reliability in the "low-uncertainty and high-localization accuracy" subgroup, with an AUC of 0.98 (95 % CI 0.95-1.00). CONCLUSIONS: Empirical assessment of uncertainty and interpretability in deep ensembles provides evidence for the robustness of prediction, offering a clinically reliable model in differentiating GBM from SBM.

Universal primers for rift valley fever virus whole-genome sequencing.

Rift Valley fever (RVF) is a mosquito-borne zoonotic disease causing acute hemorrhagic fever. Accurate identification of mutations and phylogenetic characterization of RVF virus (RVFV) require whole-genome analysis. Universal primers to amplify the entire RVFV genome from clinical samples with low copy numbers are currently unavailable. Thus, we aimed to develop universal primers applicable for all known RVFV strains. Based on the genome sequences available from public databases, we designed eight pairs of universal PCR primers covering the entire RVFV genome. To evaluate primer universality, four RVFV strains (ZH548, Kenya 56 (IB8), BIME-01, and Lunyo), encompassing viral phylogenetic diversity, were chosen. The nucleic acids of the test strains were chemically synthesized or extracted via cell culture. These RNAs were evaluated using the PCR primers, resulting in successful amplification with expected sizes (0.8-1.7 kb). Sequencing confirmed that the products covered the entire genome of the RVFV strains tested. Primer specificity was confirmed via in silico comparison against all non-redundant nucleotide sequences using the BLASTn alignment tool in the NCBI database. To assess the clinical applicability of the primers, mock clinical specimens containing human and RVFV RNAs were prepared. The entire RVFV genome was successfully amplified and sequenced at a viral concentration of 108 copies/mL. Given the universality, specificity, and clinical applicability of the primers, we anticipate that the RVFV universal primer pairs and the developed method will aid in RVFV phylogenomics and mutation detection.

Chest X-ray-based opportunistic screening of sarcopenia using deep learning.

BACKGROUND: Early detection and management of sarcopenia is of clinical importance. We aimed to develop a chest X-ray-based deep learning model to predict presence of sarcopenia. METHODS: Data of participants who visited osteoporosis clinic at Severance Hospital, Seoul, South Korea, between January 2020 and June 2021 were used as derivation cohort as split to train, validation and test set (65:15:20). A community-based older adults cohort (KURE) was used as external test set. Sarcopenia was defined based on Asian Working Group 2019 guideline. A deep learning model was trained to predict appendicular lean mass (ALM), handgrip strength (HGS) and chair rise test performance from chest X-ray images; then the machine learning model (SARC-CXR score) was built using the age, sex, body mass index and chest X-ray predicted muscle parameters along with estimation uncertainty values. RESULTS: Mean age of the derivation cohort (n = 926; women n = 700, 76%; sarcopenia n = 141, 15%) and the external test (n = 149; women n = 95, 64%; sarcopenia n = 18, 12%) cohort was 61.4 and 71.6 years, respectively. In the internal test set (a hold-out set, n = 189, from the derivation cohort) and the external test set (n = 149), the concordance correlation coefficient for ALM prediction was 0.80 and 0.76, with an average difference of 0.18 ± 2.71 and 0.21 ± 2.28, respectively. Gradient-weight class activation mapping for deep neural network models to predict ALM and HGS commonly showed highly weight pixel values at bilateral lung fields and part of the cardiac contour. SARC-CXR score showed good discriminatory performance for sarcopenia in both internal test set [area under the receiver-operating characteristics curve (AUROC) 0.813, area under the precision-recall curve (AUPRC) 0.380, sensitivity 0.844, specificity 0.739, F1-score 0.540] and external test set (AUROC 0.780, AUPRC 0.440, sensitivity 0.611, specificity 0.855, F1-score 0.458). Among SARC-CXR model features, predicted low ALM from chest X-ray was the most important predictor of sarcopenia based on SHapley Additive exPlanations values. Higher estimation uncertainty of HGS contributed to elevate the predicted risk of sarcopenia. In internal test set, SARC-CXR score showed better discriminatory performance than SARC-F score (AUROC 0.813 vs. 0.691, P = 0.029). CONCLUSIONS: Chest X-ray-based deep leaning model improved detection of sarcopenia, which merits further investigation.