Background Spread through air spaces (STAS) in lung adenocarcinoma means different treatment and worse prognosis. Purpose To construct a radiomics model based on CT scans to predict the presence of STAS in stage IA lung adenocarcinoma, compared with the traditional clinical model. Material and Methods The study included 317 patients (median age = 57.21 years; age range = 45.84–68.61 years) with pathologically confirmed stage IA lung adenocarcinoma. In total, 122 (38.5%) patients were diagnosed with STAS by pathology after the operation. Two experienced radiologists independently segmented the lesions using MITK software and extracted 1791 radiomics features using Python. Single-factor t-test or Mann–Whitney U-test and LASSO were used to screen for radiomics signatures related to STAS. This study constructed a radiomics model, a clinical model, and a combined model, combining radiomics and clinical features. Model performance was evaluated using the area under the curve (AUC). Results By single-factor analysis, four clinical features and 13 radiomics features were significantly associated with STAS. The three models (the clinical, radiomics, and combine models) achieved predictive efficacy, with an AUC of 0.849, 0.867, and 0.939, respectively, in the training set and 0.808, 0.848, and 0.876, respectively, in the testing set. Conclusion The combined model based on the radiomics and clinical features of preoperative chest CT could be used to preoperatively diagnose the presence of STAS in stage IA lung adenocarcinoma and has an excellent diagnostic performance.
A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures.To examine the performance of two versions of deep-learning-based software tools in aiding radiologists in diagnosing rib fractures on chest computed tomography (CT) images.In total, 123 patients (708 rib fractures) were included in this retrospective study. Two groups of radiologists with different experience levels retrospectively reviewed images for rib fractures in the concurrent mode aided with RibFrac-High Sensitivity (HS) and RibFrac-High Precision (HP). We compared their diagnostic performance against the reference standard in terms of sensitivity and positive predictive value (PPV).On a per-patient basis, RibFrac-HS exhibited a higher sensitivity compared with RibFrac-HP (mean difference=0.051, 95% CI=0.012-0.090; P = 0.011), whereas the latter significantly outperformed the former in terms of the PPV (mean difference=0.273, 95% CI=0.238-0.308; P < 0.0001). The use of RibFrac-HP significantly improved the junior and the senior groups' sensitivities respectively by 0.058 (95% CI=0.033-0.083; P < 0.0001) and 0.058 (95% CI=0.034-0.081; P < 0.0001), and decreased the diagnosis time by 206 s (95% CI=191-220; P < 0.0001) and 79 s (95% CI=67-92; P < 0.0001), respectively, when compared to no software assistance.The sensitivity and efficiency of radiologists in identifying rib fractures can be improved by using RibFrac-HS and/or RibFrac-HP. With an added module for false-positive suppression, RibFrac-HP maintains the sensitivity and increases the PPV in fracture detection compared to Rib-Frac-HS.
This study aims to develop a fully automated, CT-based deep learning（DL） model to segment ossified lesions of the posterior longitudinal ligament (OPLL) and to measure the thickness of the ossified material and calculate the cervical spinal cord compression factor.
The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodules (GGNs).
Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.
Abstract To develop a deep learning-based model for detecting rib fractures on chest X-Ray and to evaluate its performance based on a multicenter study. Chest digital radiography (DR) images from 18,631 subjects were used for the training, testing, and validation of the deep learning fracture detection model. We first built a pretrained model, a simple framework for contrastive learning of visual representations (simCLR), using contrastive learning with the training set. Then, simCLR was used as the backbone for a fully convolutional one-stage (FCOS) objective detection network to identify rib fractures from chest X-ray images. The detection performance of the network for four different types of rib fractures was evaluated using the testing set. A total of 127 images from Data-CZ and 109 images from Data-CH with the annotations for four types of rib fractures were used for evaluation. The results showed that for Data-CZ, the sensitivities of the detection model with no pretraining, pretrained ImageNet, and pretrained DR were 0.465, 0.735, and 0.822, respectively, and the average number of false positives per scan was five in all cases. For the Data-CH test set, the sensitivities of three different pretraining methods were 0.403, 0.655, and 0.748. In the identification of four fracture types, the detection model achieved the highest performance for displaced fractures, with sensitivities of 0.873 and 0.774 for the Data-CZ and Data-CH test sets, respectively, with 5 false positives per scan, followed by nondisplaced fractures, buckle fractures, and old fractures. A pretrained model can significantly improve the performance of the deep learning-based rib fracture detection based on X-ray images, which can reduce missed diagnoses and improve the diagnostic efficacy.
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