Xiaoqing: A Q&A model for glaucoma based on LLMs.

Glaucoma is one of the leading cause of blindness worldwide. Individuals affected by glaucoma, including patients and their family members, frequently encounter a deficit in dependable support beyond the confines of clinical environments. Seeking advice via the internet can be a difficult task due to the vast amount of disorganized and unstructured material available on these sites, nevertheless. This research explores how Large Language Models (LLMs) can be leveraged to better serve medical research and benefit glaucoma patients. We introduce Xiaoqing, a Natural Language Processing (NLP) model specifically tailored for the glaucoma field, detailing its development and deployment. To evaluate its effectiveness, we conducted two forms of experiments: comparative and experiential. In the comparative analysis, we presented 22 glaucoma-related questions in simplified Chinese to three medical NLP models (Xiaoqing LLMs, HuaTuo, Ivy GPT) and two general models (ChatGPT-3.5 and ChatGPT-4), covering a range of topics from basic glaucoma knowledge to treatment, surgery, research, management standards, and patient lifestyle. Responses were assessed for informativeness and readability. The experiential experiment involved glaucoma patients and non-patients interacting with Xiaoqing, collecting and analyzing their questions and feedback on the same criteria. The findings demonstrated that Xiaoqing notably outperformed the other models in terms of informativeness and readability, suggesting that Xiaoqing is a significant advancement in the management and treatment of glaucoma in China. We also provide a Web-based version of Xiaoqing, allowing readers to directly experience its functionality. The Web-based Xiaoqing is available at https://qa.glaucoma-assistant.com//qa.

A novel approach to the detection of facial wrinkles: Database, detection algorithm, and evaluation metrics.

Skin wrinkles result from intrinsic aging processes and extrinsic influences, including prolonged exposure to ultraviolet radiation and tobacco smoking. Hence, the identification of wrinkles holds significant importance in skin aging and medical aesthetic investigation. Nevertheless, current methods lack the comprehensiveness to identify facial wrinkles, particularly those that may appear insignificant. Furthermore, the current assessment techniques neglect to consider the blurred boundary of wrinkles and cannot differentiate images with varying resolutions. This research introduces a novel wrinkle detection algorithm and a distance-based loss function to identify full-face wrinkles. Furthermore, we develop a wrinkle detection evaluation metric that assesses outcomes based on curve, location, and gradient similarity. We collected and annotated a dataset for wrinkle detection consisting of 1021 images of Chinese faces. The dataset will be made publicly available to further promote wrinkle detection research. The research demonstrates a substantial enhancement in detecting subtle wrinkles through implementing the proposed method. Furthermore, the suggested evaluation procedure effectively considers the indistinct boundaries of wrinkles and is applicable to images with various resolutions.

Developing and Validating an Intelligent Mouth-Opening Training Device: A New Solution for Restricted Mouth Opening.

Restricted mouth opening (trismus) is one of the most common complications following head and neck cancer treatment. Early initiation of mouth-opening exercises is crucial for preventing or minimizing trismus. Current methods for these exercises predominantly involve finger exercises and traditional mouth-opening training devices. Our research group successfully designed an intelligent mouth-opening training device (IMOTD) that addresses the limitations of traditional home training methods, including the inability to quantify mouth-opening exercises, a lack of guided training resulting in temporomandibular joint injuries, and poor training continuity leading to poor training effect. For this device, an interactive remote guidance mode is introduced to address these concerns. The device was designed with a focus on the safety and effectiveness of medical devices. The accuracy of the training data was verified through piezoelectric sensor calibration. Through mechanical analysis, the stress points of the structure were identified, and finite element analysis of the connecting rod and the occlusal plate connection structure was conducted to ensure the safety of the device. The findings support the effectiveness of the intelligent device in rehabilitation through preclinical experiments when compared with conventional mouth-opening training methods. This intelligent device facilitates the quantification and visualization of mouth-opening training indicators, ensuring both the comfort and safety of the training process. Additionally, it enables remote supervision and guidance for patient training, thereby enhancing patient compliance and ultimately ensuring the effectiveness of mouth-opening exercises.

Prediction model of radiotherapy outcome for Ocular Adnexal Lymphoma using informative features selected by chemometric algorithms.

BACKGROUND: Ocular Adnexal Lymphoma (OAL) is a non-Hodgkin's lymphoma that most often appears in the tissues near the eye, and radiotherapy is the currently preferred treatment. There has been a controversy regarding the prognostic factors for systemic failure of OAL radiotherapy, the thorough evaluation prior to receiving radiotherapy is highly recommended to better the patient's prognosis and minimize the likelihood of any adverse effects. PURPOSE: To investigate the risk factors that contribute to incomplete remission in OAL radiotherapy and to establish a hybrid model for predicting the radiotherapy outcomes in OAL patients. METHODS: A retrospective chart review was performed for 87 consecutive patients with OAL who received radiotherapy between Feb 2011 and August 2022 in our center. Seven image features, derived from MRI sequences, were integrated with 122 clinical features to form comprehensive patient feature sets. Chemometric algorithms were then employed to distill highly informative features from these sets. Based on these refined features, SVM and XGBoost classifiers were performed to classify the effect of radiotherapy. RESULTS: The clinical records of from 87 OAL patients (median age: 60 months, IQR: 52-68 months; 62.1% male) treated with radiotherapy were reviewed. Analysis of Lasso (AUC = 0.75, 95% CI: 0.72-0.77) and Random Forest (AUC = 0.67, 95% CI: 0.62-0.70) algorithms revealed four potential features, resulting in an intersection AUC of 0.80 (95% CI: 0.75-0.82). Logistic Regression (AUC = 0.75, 95% CI: 0.72-0.77) identified two features. Furthermore, the integration of chemometric methods such as CARS (AUC = 0.66, 95% CI: 0.62-0.72), UVE (AUC = 0.71, 95% CI: 0.66-0.75), and GA (AUC = 0.65, 95% CI: 0.60-0.69) highlighted six features in total, with an intersection AUC of 0.82 (95% CI: 0.78-0.83). These features included enophthalmos, diplopia, tenderness, elevated ALT count, HBsAg positivity, and CD43 positivity in immunohistochemical tests. CONCLUSION: The findings suggest the effectiveness of chemometric algorithms in pinpointing OAL risk factors, and the prediction model we proposed shows promise in helping clinicians identify OAL patients likely to achieve complete remission via radiotherapy. Notably, patients with a history of exophthalmos, diplopia, tenderness, elevated ALT levels, HBsAg positivity, and CD43 positivity are less likely to attain complete remission after radiotherapy. These insights offer more targeted management strategies for OAL patients. The developed model is accessible online at: https://lzz.testop.top/.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype.

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Machine learning algorithm based on jaw feature points assist complex maxillary and mandibular reconstruction.

PURPOSES: Large-scale jaw reconstruction can hardly achieve satisfactory results only by relying on doctors' experience. In this study, we assessed a new approach using a machine learning algorithm based on jaw feature points to assist complex jaw reconstruction in patients with maxillary and mandibular defects. METHODS: One hundred and two computed tomography (CT) data on the jaw were collected and 16 skeletal marker points on the jaw were selected. The machine learning algorithm learned the positional relationship between points and built a model, which was used to predict the coordinate position of an unknown point. Then the model was used for a surgical plan in clinical cases. RESULTS: The linear regression model based on machine learning can control the error within 3 mm. In linear models, Lasso has a slight advantage over the others. We used Lasso to predict the missing points for two patients with maxillary and mandibular defect, respectively. The operation was carried out as planned, and the defects were successfully repaired. CONCLUSIONS: The restoration of jaw feature points based on a machine learning algorithm is expected to solve large-scale jaw defects without contralateral reference.

A clinical decision model based on machine learning for ptosis.

BACKGROUND: To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans. METHODS: Data of this retrospective, case-control study was collected from March 2019 to June 2019 at the Department of Ophthalmology, Shanghai Ninth People's Hospital, and then the patients were followed up for 3 months. One hundred fifty-two complete feature eyes from 100 voluntary patients with ptosis and satisfactory surgical results were selected, with 48 eyes excluded due to any severe condition or improper collection and shooting angle. Three experimental schemes were set as follows: use 2D distance alone, use 3D distance alone, and use two distances at the same time. The five most common evaluation indicators used in the binary classification problem to test the decision model were accuracy (ACC), precision, recall, F1-score, and area under the curve (AUC). RESULTS: For diagnostic discrimination, recall of "3D", "2D" and "Both" schemes were 0.875, 0.875 and 0.938 respectively. And precision of the three schemes were 0.8333, 0.7778 and 1.0000 for the surgical procedure classification. Values of "Both" scheme that combined 2D and 3D data were the highest in two classifications. CONCLUSIONS: In this study, 3D eye data are introduced into clinical practice to construct a decision model for ptosis surgery. Our decision model presents exceptional prediction effect, especially when 2D and 3D data employed jointly.

Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs.

PURPOSE: The differentiation of the ameloblastoma and odontogenic keratocyst directly affects the formulation of surgical plans, while the results of differential diagnosis by imaging alone are not satisfactory. This paper aimed to propose an algorithm based on convolutional neural networks (CNN) structure to significantly improve the classification accuracy of these two tumors. METHODS: A total of 420 digital panoramic radiographs provided by 401 patients were acquired from the Shanghai Ninth People's Hospital. Each of them was cropped to a patch as a region of interest by radiologists. Furthermore, inverse logarithm transformation and histogram equalization were employed to increase the contrast of the region of interest (ROI). To alleviate overfitting, random rotation and flip transform as data augmentation algorithms were adopted to the training dataset. We provided a CNN structure based on a transfer learning algorithm, which consists of two branches in parallel. The output of the network is a two-dimensional vector representing the predicted scores of ameloblastoma and odontogenic keratocyst, respectively. RESULTS: The proposed network achieved an accuracy of 90.36% (AUC = 0.946), while sensitivity and specificity were 92.88% and 87.80%, respectively. Two other networks named VGG-19 and ResNet-50 and a network trained from scratch were also used in the experiment, which achieved accuracy of 80.72%, 78.31%, and 69.88%, respectively. CONCLUSIONS: We proposed an algorithm that significantly improves the differential diagnosis accuracy of ameloblastoma and odontogenic keratocyst and has the utility to provide a reliable recommendation to the oral maxillofacial specialists before surgery.

Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways.

OBJECTIVES: To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data. METHODS: In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37). RESULTS: The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort. CONCLUSIONS: CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis. KEY POINTS: • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC.

Automatic orbital computed tomography coordinating method and quantitative error evaluation based on signed distance field.

BACKGROUND: Orbital computed tomography (CT) is commonly used for the diagnosis and digital evaluation of orbital diseases. Yet, this approach requires longer scanning time, increased radiation exposure, and, especially, difficult patient positioning that can affect judgment and data processing. According to high-quality research on orbital imaging, computer-assisted surgery, and artificial intelligent diagnostic development, the correction of a coordinate system is a necessary procedure. Nevertheless, existing manual calibration methods are challenging to reproduce and there is no objective evaluation system for errors. PURPOSE: To establish a method for automatic calibration of orbital CT images and implementation of quantitative error evaluation. MATERIAL AND METHODS: A standard three-dimensional (3D) orbit model was manually adjusted, and optimized orbital models were reconstructed based on the initial registration of the skull-bound directed bounding box and the registration of the mutual information method. The calibration error was calculated based on the signed distance field. Seventeen cases of orbital CT were quantitatively evaluated. RESULTS: A new method for automatic calibration and quantitative error evaluation for orbital CT was established. The calibrated model error with ±2 mm accounted for 81.61% ± 6.91% of the total models, and the error of ±1 mm accounted for 53.49% ± 7.07% of the total models. CONCLUSION: This convenient tool for orbital CT automatic calibration may promote the related quantitative research based on orbital CT. The automated operation and small error are beneficial to the popularization and application of the tool, and the quantitative evaluation facilitates other coordinate systems.

Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions.

PURPOSE: Thyroid-associated ophthalmopathy (TAO) might lead to blindness and orbital deformity. The early diagnosis and treatment are conducive to control disease progression, but currently, there is no effective screening method. The present study aimed to introduce an artificial intelligence (AI) model for screening and testing the model with TAO patients under clinical conditions. METHODS: A total of 1435 computed tomography (CT) scans were obtained from the hospital. These CT scans were preprocessed by resampling and extracting the region of interest. CT from 193 TAO patients and 715 healthy individuals were adopted for three-dimensional (3D)-ResNet model training, and 49 TAO patients and 178 healthy people were adopted for external verification. Data from 150 TAO patients and 150 healthy people were utilized for application tests under clinical conditions, including non-inferiority experiments and diagnostic tests, respectively. RESULTS: In the external verification of the model, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.919, indicating a satisfactory classification effect. The accuracy, sensitivity, and specificity were 0.87, 088, and 0.85, respectively. In non-inferiority experiments: the accuracy was 85.67% in the AI group and 84.33% in the resident group. The model passed both non-inferiority experiments (p = 0.001) and diagnostic test (the AI group sensitivity = 0.87 and specificity = 0.84%). CONCLUSIONS: A promising orbital CT-based TAO screening AI model was established and passed application tests under clinical conditions. This may provide a new TAO screening tool with further validation.

Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer.

PURPOSE: To develop a radiomics signature using diffusion-weighted imaging (DWI) for predicting progression-free survival (PFS) in muscle-invasive bladder cancer (MIBC) patients and to assess its incremental value over traditional staging system. METHOD: 210 MIBC patients undergoing preoperative DWI were enrolled. A radiomics signature was built using LASSO model. A radiomics nomogram was generated to assess the incremental value of the radiomics signature over existing risk factors in PFS estimation in terms of calibration, discrimination, reclassification and clinical usefulness. Kaplan-Meier analysis was used to assess the association of the radiomics signature with PFS. C-index was used as a discrimination measure. Net reclassification improvement (NRI) was calculated to evaluate the usefulness improvement added by the radiomics signature. Decision curve analysis was performed to evaluate the clinical usefulness of the nomograms. RESULTS: The radiomics signature was significantly associated with PFS (log-rank P = 0.0073) and was independent with clinicopathological factors (P = 0.0004). The radiomics nomogram achieved better performance in PFS prediction (C-index: 0.702, 95 % confidence interval [CI]: 0.602, 0.802) than either clinicopathological nomogram (C-index: 0.682, 95 % CI: 0.575, 0.788) or radiomics signature (C-index: 0.612, 95 % CI: 0.493, 0.731), and achieved better calibration and classification (NRI: 0.226, 95 % CI: 0.016, 0.415, P = 0.038). Decision curve analysis demonstrated the better clinical usefulness of the radiomics nomogram. CONCLUSIONS: The DWI-based radiomics signature was an independent predictor of PFS in MIBC patients. Combining the radiomics signature, clinical staging and other clinicopathological factors achieved better performance in individual PFS prediction.

A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR.

Deep learning methods have shown their great capability of extracting high-level features from image and have been used for effective medical imaging classification recently. However, training samples of medical images are restricted by the amount of patients as well as medical ethics issues, making it hard to train the neural networks. In this paper, we propose a novel end-to-end three-dimensional (3D) attention-based residual neural network (ResNet) architecture to classify different subtypes of subcortical vascular cognitive impairment (SVCI) with single-shot T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence. Our aim is to develop a convolutional neural network to provide a convenient and effective way to assist doctors in the diagnosis and early treatment of the different subtypes of SVCI. The experiment data in this paper are collected from 242 patients from the Neurology Department of Renji Hospital, including 78 amnestic mild cognitive impairment (a-MCI), 70 nonamnestic MCI (na-MCI), and 94 no cognitive impairment (NCI). The accuracy of our proposed model has reached 98.6% on a training set and 97.3% on a validation set. The test accuracy on an untrained testing set reaches 93.8% with robustness. Our proposed method can provide a convenient and effective way to assist doctors in the diagnosis and early treatment.

Improving Accuracy of Jaw Reconstruction Based on Gender Differences.

PURPOSE: The aim of the present study was to redetermine the position of the key points (skeletal marker points) in the damaged female and male jaws to improve the accuracy of jaw reconstruction. MATERIALS AND METHODS: To develop a personalized jaw reconstruction guidance program for each patient, we first made 3 statistics to compare the gender differences in the jaw. Next, we proposed and compared 3 methods to use to restore the key skeletal marker points of the damaged jaw according to our statistics. RESULTS: We collected 111 groups of computed tomography data of the jaw from normal people as experimental material. The use of our statistics showed that gender differences are present in the shape of the jaw. In addition, some key angles and distances of the jaw satisfied the Gaussian distribution. The reconstruction results showed that our methods will result in better effects than the widely used method. CONCLUSIONS: To reduce errors, gender differences should be considered when designing a reconstruction approach to the jaw. In addition, our methods can improve the accuracy of reconstruction of the jaw.

How is Gaze Influenced by Image Transformations? Dataset and Model.

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time-consuming and expensive. Most of current studies on human attention and saliency modeling have used high-quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label-preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label-preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial networks (dubbed GazeGAN). A modified U-Net is utilized as the generator of the GazeGAN, which combines classic "skip connection" with a novel "center-surround connection" (CSC) module. Our proposed CSC module mitigates trivial artifacts while emphasizing semantic salient regions, and increases model nonlinearity, thus demonstrating better robustness against transformations. Extensive experiments and comparisons indicate that GazeGAN achieves state-of-the-art performance over multiple datasets. We also provide a comprehensive comparison of 22 saliency models on various transformed scenes, which contributes a new robustness benchmark to saliency community. Our code and dataset are available at.

Classification of Subcortical Vascular Cognitive Impairment Using Single MRI Sequence and Deep Learning Convolutional Neural Networks.

Deep learning has great potential for imaging classification by extracting low to high-level features. Our aim was to train a convolutional neural network (CNN) with single T2-weighted FLAIR sequence to classify different cognitive performances in patients with subcortical ischemic vascular disease (SIVD). In total, 217 patients with SIVD [including 52 with vascular dementia (VaD), 82 with vascular mild cognitive impairment (VaMCI), and 83 with non-cognitive impairment (NCI)] and 46 matched healthy controls (HCs) underwent MRI scans and neuropsychological assessments. 2D and 3D CNNs were trained to classify VaD, VaMCI, NCI, and HCs based on FLAIR data. For 3D-based model, the loss curves of the training set approached 0.017 after about 20 epochs, while the curves of the testing set maintained at about 0.114. The accuracy of training set and testing set reached 99.7 and 96.9% after about 30 and 35 epochs, respectively. However, the accuracy of the 2D-based model was only around 70%, which performed significantly worse than 3D-based model. This experiment suggests that deep learning is a powerful and convenient method to classify different cognitive performances in SIVD by extracting the shift and scale invariant features of neuroimaging data with single FLAIR sequence. 3D-CNN is superior to 2D-CNN which involves clinical evaluation with MRI multiplanar reformation or volume scanning.

Restoring Skeletal Marker Points for Severe Maxillary and Mandibular Jaw Defects Using a Linear Regression Approach.

PURPOSE: For severe mandibular or maxillary defects across the midline, doctors often lack data on the shape of the jaws when designing virtual surgery. This study sought to repair the personalized 3-dimensional shape of the jaw, particularly when the jaw is severely damaged. MATERIALS AND METHODS: Two linear regression methods, denoted method I and method II, were used to reconstruct key points of the severely damaged maxilla or mandible based on the remaining jaw. The predictor variable was the position of key points. Outcome variables were the position of key points and the error between the predicted and actual positions. Another variable was the average error. In the final data analysis, the effect of the method was judged based on the mean error and error probability distribution. RESULTS: Computed tomographic data of jaws from 44 normal adults in East China were collected over 2 years by the Shanghai Jiao Tong University School of Medicine (Shanghai, China). Sixteen 16 key points were extracted for each jaw. Method I showed that 2-dimensional regression can yield the best overall result and that the position error of most points can be decreased to smaller than 5 mm. The result of method II was similar to that of method I but showed cumulative errors. CONCLUSIONS: Linear regression can be used to locate key points. Two-dimensional regression has the best effect, which can be used as a reference to develop a surgical plan and perform surgery.

Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.

OBJECTIVES: To develop a radiomics model with all-relevant imaging features from multiphasic computed tomography (CT) for differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC and to investigate the possible radiogenomics link between the imaging features and a key ccRCC driver gene-the von Hippel-Lindau (VHL) gene mutation. METHODS: In this retrospective two-center study, two radiomics models were built using random forest from a training cohort (170 patients), where one model was built with all-relevant features and the other with minimum redundancy maximum relevance (mRMR) features. A model combining all-relevant features and clinical factors (sex, age) was also built. The radiogenomics association between selected features and VHL mutation was investigated by Wilcoxon rank-sum test. All models were tested on an independent validation cohort (85 patients) with ROC curves analysis. RESULTS: The model with eight all-relevant features from corticomedullary phase CT achieved an AUC of 0.949 and an accuracy of 92.9% in the validation cohort, which significantly outperformed the model with eight mRMR features (seven from nephrographic phase and one from corticomedullary phase) with an AUC of 0.851 and an accuracy of 81.2%. Combining age and sex did not benefit the performance. Five out of eight all-relevant features were significantly associated with VHL mutation, while all eight mRMR features were significantly associated with VHL mutation (false discovery rate-adjusted p < 0.05). CONCLUSIONS: All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC. Most subtype-discriminative imaging features were found to be significantly associated with VHL mutation, which may underlie the molecular basis of the radiomics features. KEY POINTS: • All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC with high accuracy. • Most RCC-subtype-discriminative CT features were associated with the key RCC-driven gene-the VHL gene mutation. • Radiomics model can be more accurate and interpretable when the imaging features could reflect underlying molecular basis of RCC.

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.