TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size.

BACKGROUND AND OBJECTIVES: Spread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past few years. To alleviate the issue of limited STAS data, we propose a method TDASD based on stable diffusion, which is able to generate high-resolution CT images of pulmonary nodules corresponding to specific nodular signs according to the medical professionals. METHODS: First, we apply the stable diffusion method for fine-tuning training on publicly available lung datasets. Subsequently, we extract nodules from our hospital's lung adenocarcinoma data and apply slight rotations to the original nodule CT slices within a reasonable range before undergoing another round of fine-tuning through stable diffusion. Finally, employing DDIM and Ksample sampling methods, we generate lung adenocarcinoma nodule CT images with signs based on prompts provided by doctors. The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics. RESULTS: Our TDASD method has the capability to generate medically meaningful images by optimizing input prompts based on medical descriptions provided by experts. The images generated by our method can improve the model's classification accuracy. Furthermore, Utilizing solely the data generated by our method for model training, the test results on the original real dataset reveal an accuracy rate that closely aligns with the testing accuracy achieved through training on real data. CONCLUSIONS: The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.

MR-guided percutaneous microwave coagulation of small breast tumors.

BACKGROUND: To evaluate the technical success and patient safety of magnetic resonance-guided percutaneous microwave coagulation (MR-guided PMC) for breast malignancies. METHODS: From May 2018 to December 2019, 26 patients with breast tumors measuring 2 cm or less were recruited to participate in a prospective, single-institution clinical study. The primary endpoint of this study was the evaluation of treatment efficacy for each patient. Histochemical staining with α-nicotinamide adenine dinucleotide and reduced (NADH)-diaphorase was used to determine cell viability following and efficacy of PMC. The complications and self-reported sensations from all patients during and after ablation were also assessed. The technical success of the PMC procedure was defined when the area of the NADH-diaphorase negative region fully covered the hematoxylin-eosin (H&E) staining region in the tumor. RESULTS: All patients had a complete response to ablation with no residual carcinoma on histopathological specimen. The mean energy, ablation duration, and procedure duration per tumor were 36.0 ± 4.2 kJ, 252.9 ± 30.9 S, and 104.2 ± 13.5 min, respectively. During the ablation, 14 patients underwent prolonged ablation time, and 1 patient required adjusting of the antenna position. Eleven patients had feelings of subtle heat or swelling, and 3 patients experienced slight pain. After ablation, one patient took two painkillers because of moderate pain, and no patients had postoperative oozing or other complications after PMC. Induration around the ablation area appeared in 16 patients. CONCLUSION: MR-guided PMC of small breast tumors is feasible and could be applied in clinical practice in the future. CRITICAL RELEVANCE STATEMENT: MR-guided PMC of small breast tumors is feasible and could be applied in clinical practice in the future. KEY POINTS: • MR-guided PMC of small breast tumors is feasible. • PMC was successfully performed for all patients. • All patients were satisfied with the final cosmetic result.

An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules.

Background: Computed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework. Methods: Two datasets were used in our experiments: Lung Nodule Analysis 16 (LUNA16), a public dataset for lung nodule classification, and a private dataset of nodule signs. The private dataset contains 641 nodules, 454 of which were annotated with 6 important signs by radiologists. Our classification system consists of 2 main parts: a 3D CNN model and an SSL method called uncertainty-aware self-training framework with consistency regularization (USC). In the system, supervised training is performed with labeled data, and simultaneously, an uncertainty-and-confidence-based strategy is used to select pseudo-labeled samples for unsupervised training, thus jointly realizing the optimization of the model. Results: For the MLC of nodule signs, our proposed 3D CNN achieved satisfactory results with a mean average precision (mAP) of 0.870 and a mean area under the curve (AUC) of 0.782. In semi-supervised experiments, compared with supervised learning, our proposed SSL method could increase the mAP by 7.6% (from 0.730 to 0.806) and the mean AUC by 8.1% (from 0.631 to 0.712); it thus efficiently utilized the unlabeled data and achieved superior performance improvement compared to the recently advanced methods. Conclusions: We realized the optimal MLC of lung nodule signs with our proposed 3D CNN. Our proposed SSL method can also offer an efficient solution for the insufficiency of labeled data that may exist in the MLC tasks of 3D medical images.

Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study.

Background: Broad generalization of radiomics-assisted models may be impeded by concerns about variability. This study aimed to evaluate the merit of combatting batch effect (ComBat) harmonization in reducing the variability of voxel size-related radiomics in both phantom and clinical study in comparison with image resampling correction method. Methods: A pulmonary phantom with 22 different types of nodules was scanned by computed tomography (CT) with different voxel sizes. The variability of voxel size-related radiomics features was evaluated using concordance correlation coefficient (CCC), dynamic range (DR), and intraclass correlation coefficient (ICC). ComBat and image resampling compensation methods were used to reduce variability of voxel size-related radiomics. The percentage of robust radiomics features was compared before and after optimization. Pathologically differential diagnosis of invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) (AIS-MIA group) was used for clinical validation in 134 patients. Results: Before optimization, the number of excellent features in the phantom and clinical data was 26.12% and 32.31%, respectively. The excellent features were increased after image resampling and ComBat correction. For clinical optimization, the effect of the ComBat compensation method was significantly better than that of image resampling, with excellent features reaching 90.96% and poor features only amounting to 4.96%. In addition, the hierarchical clustering analysis showed that the first-order and shape features had better robustness than did texture features. In clinical validation, the area under the curve (AUC) of the testing set was 0.865 after ComBat correction. Conclusions: The ComBat harmonization can optimize voxel size-related CT radiomics variability in pulmonary nodules more efficiently than image resampling harmonization.

The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system.

The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule.

AIM: To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN). MATERIALS AND METHODS: A total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model. RESULTS: A total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings. CONCLUSION: The radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.

Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma.

To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97-1.00] and test set (AUC, 0.99; 95% CI, 0.97-1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.

White matter impairment in type 2 diabetes mellitus with and without microvascular disease.

BACKGROUND AND OBJECTIVE: Type 2 diabetes mellitus (T2DM) is a serious public health problem, and the phenomenon of T2DM occurring in younger people has directed more attention to functional changes in the brain. In this study, the microstructural integrity of white matter (WM) was evaluated in three groups of middle-aged subjects: healthy controls (HCs) and T2DM patients with and without peripheral microvascular complications (T2DM-C and T2DM-NC patients, respectively). METHODS: Diffusion tensor imaging (DTI) and related clinical examinations were performed in 66 subjects, including 20 T2DM-C patients, 20 T2DM-NC patients, 26 age- and sex-matched HCs. Magnetic resonance imaging (MRI) at 3 T was used to perform DTI; then, FSL and tract-based spatial statistics (TBSS) software were used to assess differences in the fractional anisotropy (FA) and mean diffusivity (MD) among the groups. The use of the FA and MD as parameters was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: There were no significant differences in sex or age among the groups, and the clinical data of the groups met the experimental requirements. There was no significant difference in the FA values between the HCs and T2DM-NC groups. Compared with the HCs, the T2DM-C patients showed decreased FA values and increased MD values in the corpus callosum, bilateral anterior limb of the internal capsule, right retrolenticular part of the internal capsule, bilateral posterior thalamic radiation, right superior longitudinal fasciculus, bilateral superior corona radiata and left middle frontal gyrus (P < .01). Compared with the T2DM-NC patients, the T2DM-C patients showed decreased FA values and increased MD values in the corpus callosum, bilateral fornix, right retrolenticular part of the internal capsule, middle cerebral peduncle, right superior longitudinal fasciculus, right posterior thalamic radiation, and left middle frontal gyrus (P < .01). CONCLUSIONS: This study indicates that WM impairment is present in T2DM patients and may be related to microvascular complications. More importantly, this study also shows that such impairment may be diagnosed using the DTI mode of functional MRI before it can be diagnosed clinically.

Magnetic Resonance Imaging of the Human Ferritin Heavy Chain Reporter Gene Carried by Dendrimer-Entrapped Gold Nanoparticles.

This paper aimed to find an effective method to destroy cancer cells by targeting breast cancer cells with natural killer (NK) cells transfected with the human ferritin heavy chain (hFTH1) gene by polyethylene glycol (PEG)-modified dendrimerentrapped gold nanoparticles (Au DENPs). In this study, fifth-generation polyamidoamine (G5 PAMAM) dendrimers modified with PEG were used as templates to entrap gold nanoparticles to transfect hFTH1 into NK cells. Our results revealed that the prepared Au DENPs/FTH1 provided high-quality imaging performance (hypointensity on T2-weighted MR imaging) and efficient transfection efficiency (reaching 80.2%) at a N/P ratio (ratio of the number of surface primary amines on {(Au0)25-G5 · NH2-mPEG17} to the number of phosphate groups in the hFTH1 backbone) of 5:1. Interestingly, the results showed that Au DENPs/FTH1 effectively guided NK-92 cells to concentrate around tumor cells for effective gene therapy without severely impacting their activity. This work will provide a new research platform for immunotherapy based on NK cells and lead to the optimization and even individualization of breast cancer immunotherapy through nanomolecular visualization research, which has a broad scope for future clinical applications.

Use of diffusion kurtosis imaging and quantitative dynamic contrast-enhanced MRI for the differentiation of breast tumors.

BACKGROUND: Breast MRI is a sensitive imaging technique to assess breast cancer but its effectiveness still remains to be improved. PURPOSE: To evaluate the diagnostic performance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and quantitative dynamic contrast-enhanced (DCE)-MRI in differentiating malignant from benign breast lesions independently or jointly and to explore whether correlations exist among these parameters. STUDY TYPE: Retrospective. POPULATION: In all, 106 patients with breast lesions (47 malignant, 59 benign). SEQUENCE: DKI sequence with seven b values and quantitative DCE sequence on 3.0T MRI. ASSESSMENT: Diffusion parameters (mean diffusivity [MD], mean diffusivity [MK], and apparent diffusion coefficient [ADC]) from DKI and DWI and perfusion parameters from DCE (Ktrans , kep , ve , and vp ) were calculated by two experienced radiologists after postprocessing. Disagreement between the two observers was resolved by consensus. STATISTICAL TESTS: The parameters in benign and malignant lesions were compared by Student's t-test. The diagnostic performances of DKI and quantitative DCE, either alone or in combination, were evaluated by receiver operating characteristic (ROC) analysis. The Spearman correlation test was used to evaluate correlations among the diffusion parameters and perfusion parameters. RESULTS: MK, MD, ADC, Ktrans , and kep values were significantly different between breast cancer and benign lesions (P < 0.05). MK from DKI demonstrated the highest AUC of 0.849, which is significantly higher than ADC derived from conventional DWI (z = 3.345, P = 0.0008). The specificity of DCE-MRI-derived parameters was improved when combining diffusion parameters, such as ADC and MK. The highest diagnostic specificity (93.2%) was obtained when kep and ADC were combined. kep was correlated moderately positively with MK (r = 0.516) and moderately negatively with MD (r = -0.527). Ktrans was weakly positively correlated with MK with an r of 0.398 and weakly negatively correlated with MD with an r of -0.450. DATA CONCLUSION: DKI is more valuable than conventional DWI in distinguishing between benign and malignant breast lesions. DKI exhibits promise as a quantitative technique to augment quantitative DCE-MRI. Diffusion parameters derived from DKI were statistically correlated with perfusion parameters from quantitative DCE-MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1358-1366.

Brain atrophy in middle-aged subjects with Type 2 diabetes mellitus, with and without microvascular complications.

BACKGROUND: The rapid rise in Type 2 diabetes mellitus (T2DM) among young adults makes it important to understand structural changes in the brain at a presenile stage. This study examined global and regional brain atrophy in middle-aged adults with T2DM, with a focus on those without clinical evidence of microvascular complications. METHODS: The study recruited 66 dementia-free middle-aged subjects (40 with T2DM, 26 healthy volunteers [HVs]). Patients were grouped according to the presence (T2DM-C; n = 20) or absence (T2DM-NC; n = 20) of diabetic microvascular complications. Global brain volume (including gray matter [GM] and white matter) was calculated based on voxel-based morphometry analysis. Regional GM volumes were further extracted using the anatomical automatic labeling template. RESULTS: There was a significant difference in global brain volume among groups (P = 0.003, anova). Global brain volume was lower in T2DM-C patients than in both T2DM-NC patients and HVs (mean [±SD] 0.720 ± 0.024 vs 0.736 ± 0.021 and 0.743 ± 0.019, respectively; P = 0.032 and P = 0.001, respectively). Regional analysis showed significant GM atrophy in the right Rolandic operculum (t = 3.42, P = 0.001) and right superior temporal gyrus (t = 2.803, P = 0.007) in T2DM-NC patients compared with age- and sex-matched HVs. CONCLUSIONS: Brain atrophy is present in dementia-free middle-aged adults with T2DM. Regional brain atrophy appears to be developing even in those with no clinical evidence of microvascular disturbances. The brain seems to be particularly vulnerable to metabolic disorders prior to peripheral microvascular pathologies associated with other target organs.

Combined resting state functional magnetic resonance imaging and diffusion tensor imaging study in patients with idiopathic restless legs syndrome.

OBJECTIVE: Restless legs syndrome (RLS) is a common neurological disorder characterized by an urge to move the legs along with paraesthesia deep within them. In this study, we aimed to use diffusion tensor imaging (DTI) and regional homogeneity (ReHo) to investigate the changes in regional spontaneous brain activity change for RLS patients against age- and gender-matched normal control (NC) subjects. METHODS: A total of 35 RLS patients and 27 age- and gender-matched NC subjects were recruited for group comparison research that used DTI and ReHo techniques. DTI was analysed by FSL and tract-based spatial statistics (TBSS) software to measure the values of fractional anisotropy (FA) or mean diffusivity (MD) in brain regions. Statistical Parametric Mapping 8 (SPM8) was used for data preprocessing and Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox was used for ReHo calculation. For multiple comparison correction, the AlphaSim program implemented in AFNI was used to control the false-positive rate (corrected p < 0.05). RESULTS: There was no significant difference between the iRLS and NC groups in age or gender. In the one-sample t-test, both the NC and RSL groups showed increased ReHo in the bilateral posterior cingulate/precuneus cortex compared to the groups' global means, indicating that the default mode network was at rest. The RLS group showed a smaller cluster size than the NC group. In the two-sample t-test, the RLS group showed increased ReHo in the bilateral middle frontal gyrus, anterior cingulate cortex, caudate nucleus, insula, thalamus, putamen and left posterior cingulate cortex compared to the NC group. The statistical analysis of DTI images did not show any difference between the two groups. TBSS group comparison did not reveal any difference in FA or mean diffusivity (MD) of any brain region. CONCLUSION: RLS patients showed that greater ReHo within the striatum, thalamus and the limbic system, which implies that the emotional processing, motion control and cognition in the cortico-striatal-thalamic-cortical (CSTC) loop may be the site of dysfunction in RLS patients. This finding may provide imaging evidence to explore the pathophysiology of RLS. On the other hand, we did not see any change in the microstructure in the DTI analysis for RLS patients when compared to the NC group, which suggests a metabolic impairment.