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Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and validate a machine learning (ML) model for the non-invasive assessment of STA-MCA bypass surgery efficacy in MMD. This study enrolled 118 MMD patients undergoing STA-MCA bypass surgery. Clinical features were screened to construct a clinical model. MRI features were extracted from the middle cerebral artery supply area using 3D Slicer and employed to build five ML models using logistic regression algorithm. The combined model was developed by integrating the radiomics score (Rad-score) with the clinical features. Model performance validation was conducted using ROC curves. Platelet count (PLT) was identified as a significant clinical feature for constructing the clinical model. A total of 3404 features (851 × 4) were extracted, and 15 optimal features were selected from each MRI sequence as predictive factors. Multivariable logistic regression identified PLT and Rad-score as independent parameters used for constructing the combined model. In the testing set, the AUC of the T1WI ML model [0.84 (95% CI, 0.70-0.97)] was higher than that of the clinical model [0.66 (95% CI, 0.46-0.86)] and the combined model [0.80 (95% CI, 0.66-0.95)]. The T1WI ML model can be used to assess the postoperative efficacy of STA-MCA bypass surgery for MMD.
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PURPOSE: To investigate the feasibility of deep learning (DL) based on conventional MRI to differentiate tuberculous spondylitis (TS) from brucellar spondylitis (BS). METHODS: A total of 383 patients with TS (n = 182) or BS (n = 201) were enrolled from April 2013 to May 2023 and randomly divided into training (n = 307) and validation (n = 76) sets. Sagittal T1WI, T2WI, and fat-suppressed (FS) T2WI images were used to construct single-sequence DL models and combined models based on VGG19, VGG16, ResNet18, and DenseNet121 network. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The AUC of DL models was compared with that of two radiologists with different levels of experience. RESULTS: The AUCs based on VGG19, ResNet18, VGG16, and DenseNet121 ranged from 0.885 to 0.973, 0.873 to 0.944, 0.882 to 0.929, and 0.801 to 0.933, respectively, and VGG19 models performed better. The diagnostic efficiency of combined models outperformed single-sequence DL models. The combined model of T1WI, T2WI, and FS T2WI based on VGG19 achieved optimal performance, with an AUC of 0.973. In addition, the performance of all combined models based on T1WI, T2WI, and FS T2WI was better than that of two radiologists (P<0.05). CONCLUSION: The DL models have potential guiding value in the diagnosis of TS and BS based on conventional MRI and provide a certain reference for clinical work.
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Brucelosis , Aprendizaje Profundo , Imagen por Resonancia Magnética , Espondilitis , Humanos , Masculino , Femenino , Imagen por Resonancia Magnética/métodos , Espondilitis/diagnóstico por imagen , Espondilitis/microbiología , Persona de Mediana Edad , Adulto , Brucelosis/diagnóstico por imagen , Diagnóstico Diferencial , Anciano , Estudios de Factibilidad , Tuberculosis de la Columna Vertebral/diagnóstico por imagen , Algoritmos , Adulto Joven , Sensibilidad y EspecificidadRESUMEN
Background: Distinguishing between prostatic cancer (PCa) and chronic prostatitis (CP) is sometimes challenging, and Gleason grading is strongly associated with prognosis in PCa. The continuous-time random-walk diffusion (CTRW) model has shown potential in distinguishing between PCa and CP as well as predicting Gleason grading. Purpose: This study aimed to quantify the CTRW parameters (α, ß & Dm) and apparent diffusion coefficient (ADC) of PCa and CP tissues; and then assess the diagnostic value of CTRW and ADC parameters in differentiating CP from PCa and low-grade PCa from high-grade PCa lesions. Study type: Retrospective (retrospective analysis using prospective designed data). Population: Thirty-one PCa patients undergoing prostatectomy (mean age 74 years, range 64-91 years), and thirty CP patients undergoing prostate needle biopsies (mean age 68 years, range 46-79 years). Field strength/Sequence: MRI scans on a 3.0T scanner (uMR790, United Imaging Healthcare, Shanghai, China). DWI were acquired with 12 b-values (0, 50, 100, 150, 200, 500, 800, 1200, 1500, 2000, 2500, 3000 s/mm2). Assessment: CTRW parameters and ADC were quantified in PCa and CP lesions. Statistical tests: The Mann-Whitney U test was used to evaluate the differences in CTRW parameters and ADC between PCa and CP, high-grade PCa, and low-grade PCa. Spearman's correlation of the pathologic grading group (GG) with CTRW parameters and ADC was evaluated. The usefulness of CTRW parameters, ADC, and their combinations (Dm, α and ß; Dm, α, ß, and ADC) to differentiate PCa from CP and high-grade PCa from low-grade PCa was determined by logistic regression and receiver operating characteristic curve (ROC) analysis. Delong test was used to compare the differences among AUCs. Results: Significant differences were found for the CTRW parameters (α, Dm) between CP and PCa (all P<0.001), high-grade PCa, and low-grade PCa (α:P=0.024, Dm:P=0.021). GG is correlated with certain CTRW parameters and ADC(α:P<0.001,r=-0.795; Dm:P<0.001,r=-0.762;ADC:P<0.001,r=-0.790). Moreover, CTRW parameters (α, ß, Dm) combined with ADC showed the best diagnostic efficacy for distinguishing between PCa and CP as well as predicting Gleason grading. The differences among AUCs of ADC, CTRW parameters and their combinations were not statistically significant (P=0.051-0.526). Conclusion: CTRW parameters α and Dm, as well as their combination were beneficial to distinguish between CA and PCa, low-grade PCa and high-grade PCa lesions, and CTRW parameters and ADC had comparable diagnostic performance.
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This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.
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RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.
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The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.
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Neoplasias Encefálicas , Aprendizaje Profundo , Neoplasias Pulmonares , Imagen por Resonancia Magnética , Humanos , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Encefálicas/secundario , Neoplasias Encefálicas/diagnóstico por imagen , Masculino , Femenino , Persona de Mediana Edad , Estudios Retrospectivos , Anciano , Imagen por Resonancia Magnética/métodos , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/patología , Carcinoma de Pulmón de Células no Pequeñas/secundario , Adulto , Interpretación de Imagen Asistida por Computador/métodos , Carcinoma Pulmonar de Células Pequeñas/diagnóstico por imagen , Carcinoma Pulmonar de Células Pequeñas/patología , Carcinoma Pulmonar de Células Pequeñas/secundario , Estudios de Factibilidad , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Curva ROCRESUMEN
RATIONALE AND OBJECTIVES: Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI. MATERIALS AND METHODS: First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression. RESULTS: The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression. CONCLUSION: The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.
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Adenoma , Neoplasias Hipofisarias , Humanos , Neoplasias Hipofisarias/diagnóstico por imagen , Radiómica , Antígeno Ki-67 , Imagen por Resonancia Magnética , Adenoma/diagnóstico por imagen , Adenoma/cirugía , Estudios RetrospectivosRESUMEN
PURPOSE: To retrospectively examine the imaging characteristics of chest-computed tomography (CT) following percutaneous microwave ablation (MWA) of the ground-glass nodule (GGN)-like lung cancer and its dynamic evolution over time. MATERIALS AND METHODS: From June 2020 to May 2021, 147 patients with 152 GGNs (51 pure GGNs and 101 mixed GGNs, mean size 15.0 ± 6.3 mm) were enrolled in this study. One hundred and forty-seven patients underwent MWA procedures. The imaging characteristics were evaluated at predetermined time intervals: immediately after the procedure, 24-48 h, 1, 3, 6, 12, and ≥18 months (47 GGNs). RESULTS: This study population included 147 patients with 152 GGNs, as indicated by the results: 43.5% (66/152) adenocarcinoma in situ, 41.4% (63/152) minimally invasive adenocarcinoma, and 15.1% (23/152) invasive adenocarcinoma. Immediate post-procedure tumor-level analysis revealed that the most common CT features were ground-glass opacities (93.4%, 142/152), hyperdensity within the nodule (90.7%, 138/152), and fried egg sign or reversed halo sign (46.7%, 71/152). Subsequently, 24-48 h post-procedure, ground-glass attenuations, hyperdensity, and the fried egg sign remained the most frequent CT findings, with incidence rates of 75.0% (114/152), 71.0% (108/152), and 54.0% (82/152), respectively. Cavitation, pleural thickening, and consolidation were less frequent findings. At 1 month after the procedure, consolidation of the ablation region was the most common imaging feature. From 3 to 12 months after the procedure, the most common imaging characteristics were consolidation, involutional parenchymal bands and pleural thickening. At ≥18 months after the procedure, imaging features of the ablation zone revealed three changes: involuting fibrosis (80.8%, 38/47), consolidation nodules (12.8%, 6/47), and disappearance (6.4%, 3/47). CONCLUSIONS: This study outlined the anticipated CT imaging characteristics of GGN-like lung cancer following MWA. Diagnostic and interventional radiologists should be familiar with the expected imaging characteristics and dynamic evolution post-MWA in order to interpret imaging changes with a reference image.
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Adenocarcinoma , Neoplasias Pulmonares , Lesiones Precancerosas , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/cirugía , Neoplasias Pulmonares/patología , Estudios Retrospectivos , Microondas/uso terapéutico , Pulmón/patología , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma/cirugía , Adenocarcinoma/patologíaRESUMEN
Astrocyte elevated gene-1 (AEG-1) is an important oncogene that overexpresses in gliomas and plays a vital role in their occurrence and progression. However, few reports have shown which biomarkers could reflect the level of AEG-1 expression in vivo so far. In recent years, intracellular metabolites monitored by proton magnetic resonance spectroscopy (1H MRS) as non-invasive imaging biomarkers have been applied to the precise diagnosis and therapy feedback of gliomas. Therefore, understanding the correlation between 1H MRS metabolites and AEG-1 gene expression in U251 cells may help to identify relevant biomarkers. This study constructed three monoclonal AEG-1-knockout U251 cell lines using the clustered regularly interspaced short palindromic repeat (CRISPR) /Cas9 technique and evaluated the biological behaviors and metabolite ratios of these cell lines. With the decline in AEG-1 expression, the apoptosis rate of the AEG-1-knockout cell lines increased. At the same time, the metastatic capacities decreased, and the relative contents of total choline (tCho) and lactate (Lac) were also reduced. In conclusion, deviations in AEG-1 expression influence the apoptosis rate and metastasis capacity of U251 cells, which the 1H MRS metabolite ratio could monitor. The tCho/creatinine(Cr) and Lac/Cr ratios positively correlated with the AEG-1 expression and malignant cell behavior. This study may provide potential biomarkers for accurate preoperative diagnosis and future AEG-1-targeting treatment evaluation of gliomas in vivo.
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Astrocitos , Glioma , Humanos , Colina , Expresión Génica , Ácido Láctico , OncogenesRESUMEN
PURPOSE: To assess the continuous-time random-walk (CTRW) model's diagnostic value in breast lesions and to explore the associations between the CTRW parameters and breast cancer pathologic factors. METHOD: This retrospective study included 85 patients (70 malignant and 18 benign lesions) who underwent 3.0T MRI examinations. Diffusion-weighted images (DWI) were acquired with 16b-values to fit the CTRW model. Three parameters (Dm, α, and ß) derived from CTRW and apparent diffusion coefficient (ADC) from DWI were compared among the benign/malignant lesions, molecular prognostic factors, and molecular subtypes by Mann-Whitney U test. Spearman correlation was used to evaluate the associations between the parameters and prognostic factors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) based on the diffusion parameters. RESULTS: All parameters, ADC, Dm, α, and ß were significantly lower in the malignant than benign lesions (P < 0.05). The combination of all the CTRW parameters (Dm, α, and ß) provided the highest AUC (0.833) and the best sensitivity (94.3%) in differentiating malignant status. And the positive status of estrogen receptor (ER) and progesterone receptor (PR) showed significantly lower ß compared with the negative counterparts (P < 0.05). The high Ki-67 expression produced significantly lower Dm and ADC values (P < 0.05). Additionally, combining multiple CTRW parameters improved the performance of diagnosing molecular subtypes of breast cancer. Moreover, Spearman correlations analysis showed that ß produced significant correlations with ER, PR and Ki-67 expression (P < 0.05). CONCLUSIONS: The CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.
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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/patología , Pronóstico , Estudios Retrospectivos , Antígeno Ki-67 , Sensibilidad y Especificidad , Interpretación de Imagen Asistida por Computador/métodos , Reproducibilidad de los Resultados , Imagen por Resonancia Magnética , Imagen de Difusión por Resonancia Magnética/métodos , Receptores de Estrógenos , Mama/patologíaRESUMEN
This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.
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Neoplasias Encefálicas , Aprendizaje Profundo , Glioblastoma , Humanos , Glioblastoma/diagnóstico por imagen , Glioblastoma/patología , Estudios Retrospectivos , Sensibilidad y Especificidad , Imagen por Resonancia Magnética/métodos , Imagen de Difusión por Resonancia Magnética/métodos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patologíaRESUMEN
Objective: To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs). Materials and methods: Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created. Results: Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful. Conclusion: Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.
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BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. PURPOSE: To distinguish primary site of BM and identify the best DL models. STUDY TYPE: Retrospective. POPULATION: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. FIELD STRENGTH/SEQUENCE: A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE). ASSESSMENT: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps. STATISTICAL TESTS: The area under the receiver operating characteristics curve (AUC) assess each classification performance. RESULTS: 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions. DATA CONCLUSION: DL models may help to distinguish the origins of BM based on MRI data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Neoplasias Encefálicas , Neoplasias de la Mama , Carcinoma de Pulmón de Células no Pequeñas , Aprendizaje Profundo , Neoplasias Pulmonares , Humanos , Femenino , Persona de Mediana Edad , Imagen de Difusión por Resonancia Magnética/métodos , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Estudios Retrospectivos , Neoplasias Pulmonares/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patologíaRESUMEN
RATIONALE AND OBJECTIVES: To develop, validate, and test a comprehensive radiomics prediction model to distinguish parotid polymorphic adenomas (PAs) and warthin tumors (WTs) using clinical data and enhanced computed tomography (CT) from a multicenter cohort. MATERIALS AND METHODS: A total of 267 patients with PAs (n =172) or WTs (n = 95) from two hospitals were randomly divided into training (n =188) and validation (n =79) datasets. Radiomics features were extracted from the enhanced CT (arterial phase) followed by dimensionality reduction. Clinical and CT features were combined to establish a prediction model. A radiomics nomogram was constructed by combining RadScore and clinical factors. Moreover, an independent dataset of 31 patients from a third hospital was employed to test the model. Thus, the performance of the nomogram, radiomics signature, and clinical models was evaluated on the training, validation, and the independent testing datasets. Receiver operating characteristic (ROC) curves were used to compare the performance, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the model. RESULTS: A total of 15 radiomics features were selected from CT data as the imaging markers to generate RadScores, and demographics or clinical data like age, sex, and smoking factors combined with RadScores were used to distinguish PAs and WTs based on multivariate logistic regression analyses. The results showed that radiomics nomograms combining clinical factors and RadScores provided satisfactory predictive values for distinguishing PAs from WTs, with areas under ROC curves (AUC) of 0.979, 0.922, and 0.903 for the training, validation, and the independent testing datasets, respectively. Decision curve analysis revealed that the radiomics nomogram outperformed the clinical factor models in terms of accuracy and effectiveness. CONCLUSION: CT-based radiomics nomograms combining RadScores and clinical factors can be used to identify PAs and WTs, which may help tumor management by clinicians.
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Adenolinfoma , Adenoma , Humanos , Nomogramas , Adenolinfoma/diagnóstico por imagen , Tomografía Computarizada por Rayos X , Arterias , Adenoma/diagnóstico por imagen , Estudios RetrospectivosRESUMEN
RATIONALE AND OBJECTIVES: To explore the feasibility of differentiating three predominant metastatic tumor types using lung computed tomography (CT) radiomics features based on supervised machine learning. MATERIALS AND METHODS: This retrospective analysis included 252 lung metastases (LM) (from 78 patients), which were divided into the training (n = 176) and test (n = 76) cohort randomly. The metastases originated from colorectal cancer (n = 97), breast cancer (n = 87), and renal carcinoma (n = 68). An additional 77 LM (from 35 patients) were used for external validation. All radiomics features were extracted from lung CT using an open-source software called 3D slicer. The least absolute shrinkage and selection operator (LASSO) method selected the optimal radiomics features to build the model. Random forest and support vector machine (SVM) were selected to build three-class and two-class models. The performance of the classification model was evaluated with the area under the receiver operating characteristic curve (AUC) by two strategies: one-versus-rest and one-versus-one. RESULTS: Eight hundred and fifty-one quantitative radiomics features were extracted from lung CT. By LASSO, 23 optimal features were extracted in three-class, and 25, 29, and 35 features in two-class for differentiating every two of three LM (colorectal cancer vs. renal carcinoma, colorectal cancer vs. breast cancer, and breast cancer vs. renal carcinoma, respectively). The AUCs of the three-class model were 0.83 for colorectal cancer, 0.79 for breast cancer, and 0.91 for renal carcinoma in the test cohort. In the external validation cohort, the AUCs were 0.77, 0.83, and 0.81, respectively. Swarmplot shows the distribution of radiomics features among three different LM types. In the two-class model, high accuracy and AUC were obtained by SVM. The AUC of discriminating colorectal cancer LM from renal carcinoma LM was 0.84, and breast cancer LM from colorectal cancer LM and renal carcinoma LM were 0.80 and 0.94, respectively. The AUCs were 0.77, 0.78, and 0.84 in the external validation cohort. CONCLUSION: Quantitative radiomics features based on Lung CT exhibited good discriminative performance in LM of primary colorectal cancer, breast cancer, and renal carcinoma.
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Neoplasias de la Mama , Carcinoma de Células Renales , Neoplasias Colorrectales , Neoplasias Renales , Neoplasias Pulmonares , Humanos , Femenino , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias Renales/diagnóstico por imagen , Neoplasias Colorrectales/diagnóstico por imagenRESUMEN
Purpose: To develop and validate a clinical-radiomics nomogram based on clinical risk factors and CT radiomics feature to predict hypertensive intracerebral hemorrhage (HICH) prognosis. Methods: A total of 195 patients with HICH treated in our hospital from January 2018 to January 2022 were retrospectively enrolled and randomly divided into two cohorts for training (n = 138) and validation (n = 57) according to the ratio of 7 : 3. All CT radiomics features were extracted from intrahematomal, perihematomal, and combined intra- and perihematomal regions by using free open-source software called 3D slicer. The least absolute shrinkage and selection operator method was used to select the optimal radiomics features, and the radiomics score (Rad-score) was calculated. The relationship between Rad-score, clinical risk factors, and the HICH prognosis was analyzed by univariate and multivariate logistic regression analyses, and the clinical-radiomics nomogram was built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the clinical-radiomics nomogram in predicting the prognosis of HICH. Results: A total of 1702 radiomics features were extracted from the CT images of each patient for analysis. By univariate and stepwise multivariate logistic regression analyses, age, sex, RBC, serum glucose, D-dimer level, hematoma volume, and midline shift were clinical risk factors for the prognosis of HICH. Rad-score and clinical risk factors developed the clinical-radiomics nomogram. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.95, 95% confidence interval (CI), 0.92 to 0.98) and the validation cohort (AUC = 0.90, 95% CI, 0.82 to 0.98). The calibration curve indicated that the clinical-radiomics nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice. Conclusions: The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.
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Hemorragia Intracraneal Hipertensiva , Humanos , Nomogramas , Estudios Retrospectivos , Factores de Riesgo , Tomografía Computarizada por Rayos XRESUMEN
Purpose: To develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC). Methods: Two hundred and thirty-five female patients with BC were enrolled from July 2018 to February 2022 and divided into a training group (from center I, 115 patients), internal validation group (from center I, 49 patients), and external validation group (from centers II and III, 71 patients). The preoperative MRI of all patients was obtained, and radiomics features were extracted by a free open-source software called 3D Slicer. The Least Absolute Shrinkage and Selection Operator regression model was used to identify the most useful features. The radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical-radiomics nomogram combining clinical factors and Rad-score was developed through multivariate logistic regression analysis. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 2,553 radiomics features were extracted, and 21 radiomics features were selected as the most useful radiomics features. Multivariate logistic regression analysis indicated that Rad-score, progesterone receptor (PR), and Ki-67 were independent parameters to distinguish HER2 status. The clinical-radiomics nomogram, which comprised Rad-score, PR, and Ki-67, showed a favorable classification capability, with AUC of 0.87 [95% confidence internal (CI), 0.80 to 0.93] in the training group, 0.81 (95% CI, 0.69 to 0.94) in the internal validation group, and 0.84 (95% CI, 0.75 to 0.93) in the external validation group. DCA illustrated that the nomogram was useful in clinical practice. Conclusions: The nomogram combined with Rad-score, PR, and Ki-67 can identify the HER2 status of BC.
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Objective: To evaluate the safety and effectiveness of ultrasound-guided dry needling for trigger point inactivation in the treatment of postherpetic neuralgia (PHN) mixed with myofascial pain syndrome (MPS). Methods: A prospective and controlled clinical study was conducted. From January 2020 to December 2020, among the 100 patients who received PHN treatment in the pain department, 54 patients complicated with MPS were randomly divided into the dry needling group D (n = 28) and pharmacotherapeutic group P (n = 26). Visual analogue score (VAS) and McGill Pain Questionnaire (MPQ) were taken as primary indicators. Ultrasound-guided inactivation of myofascial trigger points (MTrPs) with dry needling and intradermal needling combined with press needling were applied on group D and pharmacotherapeutic only treatment on group P respectively. The VAS score <3 and/or the MPQ score <2 represents effective treatment. The VAS score >3 and/or the MPQ score >2 represents recurrent in follow-up study three months after the treatment. Results: After four weeks treatment, the effective rate of one month later of the group D was 92.9% and the effective rate of group P was 38.5%, respectively. The recurrent rate of group D was 7.1% and 34.6% for group P, respectively, for follow-up three months later. The satisfactory rate of group D was higher than that of group P. Conclusion: Ultrasound-guided dry needling and intradermal needling combined with press needling were more effective than only pharmacotherapeutic treatment for PHN mixed with MPS, with lower recurrent rate and higher patient's satisfactory rate.
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Punción Seca , Fibromialgia , Síndromes del Dolor Miofascial , Neuralgia Posherpética , Fibromialgia/terapia , Estudios de Seguimiento , Humanos , Síndromes del Dolor Miofascial/terapia , Neuralgia Posherpética/terapia , Estudios Prospectivos , Puntos Disparadores , Ultrasonografía IntervencionalRESUMEN
Purpose: To build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD). Methods: Fifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC). Results: Of the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively. Conclusion: The TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.