Prognostic implication of consolidation-to-tumor ratio in early lung adenocarcinoma: a retrospective cross-sectional study.

Background: The threshold value of consolidation-to-tumor ratio (CTR) for distinguishing between ground-glass opacity (GGO)-predominant and solid-predominant ground-glass nodules (GGNs) needs to be clarified, as the lack of clarity has caused the prognostic implications to remain ambiguous. This study aimed to determine the threshold value of CTR for distinguishing between GGO-predominant GGNs and solid-predominant GGNs and elucidate the prognostic implications of the solid-predominant GGNs categorized by CTR on c-stage IA lung adenocarcinoma. Methods: Between January 2016 and October 2018, 764 c-stage IA lung adenocarcinoma cases were assembled from the First Affiliated Hospital of Chongqing Medical University. Of the 764 lesions, 515 (67.4%) were nodules with a GGO component, and 249 (32.6%) were solid nodules (SNs) on thin-section computed tomography (CT). We evaluated the correlation of the 3-dimensional (3D) consolidation component volume ratio with CTR based on the coefficient of determination, r. After receiver operating characteristic (ROC) analysis of 515 GGNs, we defined the nodule with CTR >0.750 as solid-predominant GGN and the nodule with CTR ≤0.750 as GGO-predominant GGN. Subsequently, the prognosis of 439 patients who had follow-up registration was evaluated. Survival curves were calculated using the Kaplan-Meier method, and the log-rank test was employed to compare survival rates among different groups. Cox proportional hazard regression models were applied to evaluate the independent risk factors for recurrence-free survival (RFS). Results: Among 764 patients, 515 (67.4%) were nodules with a GGO component, and 249 (32.6%) were SNs on thin-section CT. For 515 GGNs, the 3D consolidation component volume ratio correlated well with CTR (r=0.888). CTR tended to be slightly larger than the 3D consolidation component volume ratio. A 3D consolidation component volume ratio >50% was best predicted by CTR >0.750, followed by CTR >0.549. CTR >0.750 and CTR >0.549 predicted 3D consolidation component volume ratio >50% with 85% and 99.2% sensitivity and 91.6% and 57.2% specificity, respectively. The 5-year RFS and overall survival (OS) of patients with 0.750< CTR <1 were worse than those of patients with 0≤ CTR ≤0.750 (P<0.001 and P<0.001, respectively) but better than those of patients with CTR =1 (P=0.002 and P=0.03, respectively). Carcinoembryonic antigen (CEA) >2.1 [hazard ratio (HR) =12.516, 95% confidence interval (CI): 1.729-90.598], CTR >0.750 (HR =13.934, 95% CI: 3.341-58.123), larger consolidation component size with diameter more than 20 mm (HR =1.855, 95% CI: 1.242-2.770), poorly differentiated (HR =1.622, 95% CI: 1.056-2.491), lymph node metastasis (HR =2.473, 95% CI: 1.601-3.821), and sublobar resection (HR =2.596, 95% CI: 1.701-3.962) could predict the poor prognosis. Patients with 0≤ CTR ≤0.750 receiving sublobar resection had prognoses comparable to those receiving lobar resection, whether the tumor size ≤2 cm or consolidation component size ≤3 cm. Lobar resection was superior to sublobar resection for non-small cell lung cancer (NSCLC) ≤2 cm with CTR >0.750. Conclusions: Compared to CTR =0.5, the 2-dimensional (2D) CTR =0.750 found using the 3D consolidation component volume ratio as the gold standard better differentiated between solid-predominant GGNs and GGO-predominant GGNs. CTR >0.750 was an independent risk factor associated with the poor prognosis of patients with c-stage IA lung adenocarcinoma. Sublobar resection should be cautiously adopted in GGNs with 0.750< CTR ≤1.

The Predictive Effect of Quantitative Analysis of Signal Intensity Heterogeneity on T2-Weighted MR Images for High-intensity Focused Ultrasound Treatment of Uterine Fibroids.

RATIONALE AND OBJECTIVES: To investigate whether the quantitative index of signal intensity (SI) heterogeneity on T2-weighted (T2W) magnetic resonance images can predict the difficulty and efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids. MATERIALS AND METHODS: The standard deviation (SD) of T2W image (T2WI) SI was used to quantify SI heterogeneity. The correlation between SD and the non-perfused volume ratio (NPVR) in 575 patients undergoing HIFU treatment was retrospectively analyzed, and the efficacy of SD in predicting NPVR was discussed. Three classifications were made based on the SD, and the ablation difficulty and ablation effect of different grades were compared. A total of 65 cases from another center were used as an external validation set to verify the classification performance of SD. RESULTS: The SD of SI was negatively correlated with NPVR (r = -0.460, p < 0.001). The predictive efficiency of SD for the ablation effect was higher than that of the scaled signal intensity (0.767 vs. 0.701, p = 0.006). Univariate and multivariate logistic regression analyses showed that SD was an independent predictor of ablation effect. Based on SD, the three classifications were divided into SD I: SD < 101.0, SD II: 101.0 ≤ SD < 138.7, and SD III: SD≥ 138.7. The treatment time, sonication time, treatment intensity, and total energy of SD I were lower than those of SD II and III (p < 0.05). CONCLUSION: The heterogeneity of T2WI SI of uterine fibroids is negatively correlated with NPVR. The SD of SI can be used to predict the ablation difficulty and ablation effect of HIFU.

Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks.

BACKGROUND: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. METHODS: 3D-T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence-based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. RESULTS: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three-classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. CONCLUSION: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm.

RATIONALE AND OBJECTIVES: We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm. MATERIALS AND METHODS: The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out. RESULTS: Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort. CONCLUSION: The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions.

PURPOSE: To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS: From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS: Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION: The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

Differences in Patellofemoral Alignment Between Static and Dynamic Extension Positions in Patients With Patellofemoral Pain.

Background: Considering that patellofemoral pain (PFP) is related to dynamic factors, dynamic extension on 4-dimensional computed tomography (4-DCT) may better reflect the influence of muscles and surrounding soft tissue than static extension. Purpose: To compare the characteristics of patellofemoral alignment between the static and dynamic knee extension position in patients with PFP and controls via 4-DCT. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were 39 knees (25 patients) with PFP and 37 control knees (24 participants). For each knee, an image of the dynamic extension position (a single frame of the knee in full extension [flexion angle of -5° to 0°] selected from 21 frames of continuous images acquired by 4-DCT during active flexion and extension) and an image of the static extension position (acquired using the same equipment with the knee fully extended and the muscles relaxed) were selected. Patellofemoral alignment was evaluated between the dynamic and static extension positions and between the PFP and control groups with the following parameters: patella-patellar tendon angle (P-PTA), Blackburne-Peel ratio, bisect-offset (BO) index, lateral patellar tilt (LPT), and tibial tuberosity-trochlear groove (TT-TG) distance. Results: In both PFP patients and controls, the P-PTA, Blackburne-Peel ratio, and BO index in the static extension position were significantly lower (P < .001 for all), while the LPT and TT-TG distance in the static extension position were significantly higher (P ≤ .034 and P < .001, respectively) compared with values in the dynamic extension position. In the comparison between groups, only P-PTA in the static extension position was significantly different (134.97° ± 4.51° [PFP] vs 137.82° ± 5.63° [control]; P = .027). No difference was found in the rate of change from the static to the dynamic extension position of any parameter between the study groups. Conclusion: The study results revealed significant differences in patellofemoral alignment characteristics between the static and dynamic extension positions of PFP patients and controls. Multiplanar measurements may have a role in subsequent patellofemoral alignment evaluation.

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study.

BACKGROUND: Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. METHODS: We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. FINDINGS: The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). INTERPRETATION: This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. FUNDING: National Natural Science Foundation of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.

PURPOSE: To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC). MATERIALS AND METHODS: From June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC. RESULTS: For model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models. CONCLUSION: The CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression.

Importance: Adolescent major depressive disorder (MDD) is associated with serious adverse implications for brain development and higher rates of self-injury and suicide, raising concerns about its neurobiological mechanisms in clinical neuroscience. However, most previous studies regarding the brain alterations in adolescent MDD focused on single-modal images or analyzed images of different modalities separately, ignoring the potential role of aberrant interactions between brain structure and function in the psychopathology. Objective: To examine alterations of structural and functional connectivity (SC-FC) coupling in adolescent MDD by integrating both diffusion magnetic resonance imaging (MRI) and resting-state functional MRI data. Design, Setting, and Participants: This cross-sectional study recruited participants aged 10 to 18 years from January 2, 2020, to December 28, 2021. Patients with first-episode MDD were recruited from the outpatient psychiatry clinics at The First Affiliated Hospital of Chongqing Medical University. Healthy controls were recruited by local media advertisement from the general population in Chongqing, China. The sample was divided into 5 subgroup pairs according to different environmental stressors and clinical characteristics. Data were analyzed from January 10, 2022, to February 20, 2023. Main Outcomes and Measures: The SC-FC coupling was calculated for each brain region of each participant using whole-brain SC and FC. Primary analyses included the group differences in SC-FC coupling and clinical symptom associations between SC-FC coupling and participants with adolescent MDD and healthy controls. Secondary analyses included differences among 5 types of MDD subgroups: with or without suicide attempt, with or without nonsuicidal self-injury behavior, with or without major life events, with or without childhood trauma, and with or without school bullying. Results: Final analyses examined SC-FC coupling of 168 participants with adolescent MDD (mean [mean absolute deviation (MAD)] age, 16.0 [1.7] years; 124 females [73.8%]) and 101 healthy controls (mean [MAD] age, 15.1 [2.4] years; 61 females [60.4%]). Adolescent MDD showed increased SC-FC coupling in the visual network, default mode network, and insula (Cohen d ranged from 0.365 to 0.581; false discovery rate [FDR]-corrected P < .05). Some subgroup-specific alterations were identified via subgroup analyses, particularly involving parahippocampal coupling decrease in participants with suicide attempt (partial η2 = 0.069; 90% CI, 0.025-0.121; FDR-corrected P = .007) and frontal-limbic coupling increase in participants with major life events (partial η2 ranged from 0.046 to 0.068; FDR-corrected P < .05). Conclusions and Relevance: Results of this cross-sectional study suggest increased SC-FC coupling in adolescent MDD, especially involving hub regions of the default mode network, visual network, and insula. The findings enrich knowledge of the aberrant brain SC-FC coupling in the psychopathology of adolescent MDD, underscoring the vulnerability of frontal-limbic SC-FC coupling to external stressors and the parahippocampal coupling in shaping future-minded behavior.

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer.

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.

Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models.

Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.

Accuracy of solid portion size measured on multiplanar volume rendering images for assessing invasiveness in lung adenocarcinoma manifesting as subsolid nodules.

Background: The solid component of subsolid nodules (SSNs) is closely associated with the invasiveness of lung adenocarcinoma, and its accurate assessment is crucial for selecting treatment method. Therefore, this study aimed to evaluate the accuracy of solid component size within SSNs measured on multiplanar volume rendering (MPVR) and compare it with the dimensions of invasive components on pathology. Methods: A pilot study was conducted using a chest phantom to determine the optimal MPVR threshold for the solid component within SSN, and then clinical validation was carried out by retrospective inclusion of patients with pathologically confirmed solitary SSN from October 2020 to October 2021. The radiological tumor size on MPVR and solid component size on MPVR (RSSm) and on lung window (RSSl) were measured. The size of the tumor and invasion were measured on the pathological section, and the invasion, fibrosis, and inflammation within SSNs were also recorded. The measurement difference between computed tomography (CT) and pathology, inter-observer and inter-measurement agreement were analyzed. Receiver operating characteristic (ROC) analysis and Bland-Altman plot were performed to evaluate the diagnostic efficiency of MPVR. Results: A total of 142 patients (mean age, 54±11 years, 39 men) were retrospectively enrolled in the clinical study, with 26 adenocarcinomas in situ, 92 minimally invasive adenocarcinomas (MIAs), and 24 invasive adenocarcinomas (IAs). The RSSl was significantly smaller than pathological invasion size with fair inter-measurement agreement [intraclass correlation coefficient (ICC) =0.562, P<0.001] and moderate interobserver agreement (ICC =0.761, P<0.001). The RSSm was significantly larger than pathological invasion size with the excellent inter-measurement agreement (ICC =0.829, P<0.001) and excellent (ICC =0.952, P<0.001) interobserver agreement. ROC analysis showed that the cutoff value of RSSm for differentiating adenocarcinoma in situ from MIA and MIA from IA was 1.85 and 6.45 mm (sensitivity: 93.8% and 95.5%, specificity: 85.7% and 88.2%, 95% confidence internal: 0.914-0.993 and 0.900-0.983), respectively. The positive predictive value-and negative predictive value of MPVR in predicting invasiveness were 92.8% and 100%, respectively. Conclusions: Using MPVR to predict the invasive degree of SSN had high accuracy and good inter-observer agreement, which is superior to lung window measurements and helpful for clinical decision-making.

Revolutionizing vascular imaging: trends and future directions of 4D flow MRI based on a 20-year bibliometric analysis.

Background: Four-dimensional flow magnetic resonance imaging (4D flow MRI) is a promising new technology with potential clinical value in hemodynamic quantification. Although an increasing number of articles on 4D flow MRI have been published over the past decades, few studies have statistically analyzed these published articles. In this study, we aimed to perform a systematic and comprehensive bibliometric analysis of 4D flow MRI to explore the current hotspots and potential future directions. Methods: The Web of Science Core Collection searched for literature on 4D flow MRI between 2003 and 2022. CiteSpace was utilized to analyze the literature data, including co-citation, cooperative network, cluster, and burst keyword analysis. Results: A total of 1,069 articles were extracted for this study. The main research hotspots included the following: quantification and visualization of blood flow in different clinical settings, with keywords such as "cerebral aneurysm", "heart", "great vessel", "tetralogy of Fallot", "portal hypertension", and "stiffness"; optimization of image acquisition schemes, such as "resolution" and "reconstruction"; measurement and analysis of flow components and patterns, as indicated by keywords "pattern", "KE", "WSS", and "fluid dynamics". In addition, international consensus for metrics derived from 4D flow MRI and multimodality imaging may also be the future research direction. Conclusions: The global domain of 4D flow MRI has grown over the last 2 decades. In the future, 4D flow MRI will evolve towards becoming a relatively short scan duration with adequate spatiotemporal resolution, expansion into the diagnosis and treatment of vascular disease in other related organs, and a shift in focus from vascular structure to function. In addition, artificial intelligence (AI) will assist in the clinical promotion and application of 4D flow MRI.

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.

BACKGROUND: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. METHODS: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. RESULTS: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. CONCLUSIONS: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

Non-contrast enhanced MRI for efficiency evaluation of high-intensity focused ultrasound in adenomyosis ablation.

OBJECTIVE: To investigate the value of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in evaluating the therapeutic effect of high-intensity focused ultrasound (HIFU) in adenomyosis ablation. MATERIAL AND METHODS: One hundred eighty-nine patients with adenomyosis were treated with HIFU. The ablation areas on T2WI and DWI sequences were classified into different types: type I, relatively ill-defined rim or unrecognizable; subtype IIa, well-defined rim with hyperintensity; subtype IIb, well-defined rim with hypointensity. The volume of ablation areas on T2WI (VT2WI) and DWI (VDWI) was measured and compared with the non-perfused volume (NPV), and linear regression was conducted to analyze their correlation with NPV. RESULTS: The VT2WI of type I and type II (subtype IIa and subtype IIb) were statistically different from the corresponding NPV (p = 0.004 and 0.024, respectively), while no significant difference was found between the VDWI of type I and type II with NPV (p = 0.478 and 0.561, respectively). In the linear regression analysis, both VT2WI and VDWI were positively correlated with NPV, with R2 reaching 0.96 and 0.97, respectively. CONCLUSIONS: Both T2WI and DWI have the potential for efficient evaluation of HIFU treatment in adenomyosis, and DWI can be a replacement for CE-T1WI to some extent.

Factors influencing magnetic resonance imaging changes associated with pelvic bone injury after high-intensity focused ultrasound ablation of uterine fibroids: a retrospective case-control study.

Background: The application of high-intensity focused ultrasound (HIFU) in the treatment of uterine fibroids is becoming increasingly widespread, and postoperative collateral thermal damage to adjacent tissue has become a prominent subject of discussion. However, there is limited research related to bone injury. Therefore, the aim of this study was to investigate the potential factors influencing unintentional pelvic bone injury after HIFU ablation of uterine fibroids with magnetic resonance imaging (MRI). Methods: A total of 635 patients with fibroids treated with HIFU in the First Affiliated Hospital of Chongqing Medical University were enrolled. All patients underwent contrast-enhanced MRI (CE-MRI) pre- and post-HIFU. Based on the post-treatment MRI, the patients were divided into two groups: pelvic bone injury group and non-injury group, while the specific site of pelvic bone injury of each patient was recorded. The univariate and multivariate analyses were used to assess the correlations between the factors of fibroid features and treatment parameters and pelvic bone injury, and to further analyze the factors influencing the site of injury. Results: Signal changes in the pelvis were observed on CE-MRI in 51% (324/635) of patients after HIFU. Among them, 269 (42.4%) patients developed sacral injuries and 135 (21.3%) had pubic bone injuries. Multivariate analyses showed that patients with higher age [P=0.003; odds ratio (OR), 1.692; 95% confidence interval (CI): 1.191-2.404], large anterior side-to-skin distance of fibroid (P<0.001; OR, 2.297; 95% CI: 1.567-3.365), posterior wall fibroid (P=0.006; OR, 1.897; 95% CI: 1.204-2.989), hyperintensity on T2-weighted imaging (T2WI, P=0.003; OR, 2.125; 95% CI: 1.283-3.518), and large therapeutic dose (TD, P<0.001; OR, 3.007; 95% CI: 2.093-4.319) were at higher risk of postoperative pelvic bone injury. Further analysis of the factors influencing the site of the pelvic bone injury showed that some of the fibroid features and treatment parameters were associated with it. Moreover, some postoperative pain-related adverse events were associated with the pelvic bone injury. Conclusions: Post-HIFU treatment, patients may experience pelvic injuries to the sacrum, pubis, or a combination of both, and some of them experienced adverse events. Some fibroid features and treatment parameters are associated with the injury. Taking its influencing factors into full consideration preoperatively, slowing down treatment, and prolonging intraoperative cooling phase can help optimize treatment decisions for HIFU.

Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China.

BACKGROUND: To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system). METHODS: Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021-June 30, 2022), SC Center (September 1, 2021-December 31, 2021), and SX Center (January 1, 2021-December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test. RESULTS: For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P < 0.001; PSN 99.9% vs. 98.4%, P = 0.015), but the specificity (SN 51.2% > 35.1%, PSN 13.3% > 5.7%, all P < 0.001) and agreement rate (SN 81.1% > 74.5%, P < 0.001, PSN 94.6% > 92.7%, all P < 0.05) of PNI-GARS were superior to those of Lung-RADS. For GGN, the sensitivity (96.5%) and agreement rate (88.6%) of PNI-GARS were better than those of Lung-RADS (0, 18.5%, P < 0.001). For the whole sample, the sensitivity (98.5%) and agreement rate (87.0%) of PNI-GARS were better than Lung-RADS (57.5%, 56.5%, all P < 0.001), whereas the specificity was slightly lower (49.8% < 53.4%, P = 0.003). CONCLUSION: PNI-GARS was superior to Lung-RADS in diagnostic performance, especially for GGN.