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PURPOSE: We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS: A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS: In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION: The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.
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Inteligência Artificial , Neoplasias da Próstata , Masculino , Humanos , Neoplasias da Próstata/diagnóstico por imagem , Próstata/diagnóstico por imagem , Próstata/patologia , Biópsia , Ultrassonografia/métodosRESUMO
OBJECTIVE: To study the effect of cluster nursing care based on 10S continuous quality improvement (CQI) on the incidence of postoperative delirium in patients with BPH. METHODS: This study included 96 BPH patients undergoing transurethral resection of the prostate (TURP) in our department from August 2021 to February 2023. We randomly divided the patients into two groups of equal number to receive routine postoperative nursing care (the control group) and postoperative cluster nursing care based on the 10S DQI mode (the observation group), respectively. We recorded and compared the delirium scores of the patients at 2, 6, 12 and 24 hours after operation, their status of recovery, scores on Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS) and quality of life (QOL), and incidence of complications between the two groups. RESULTS: Compared with the controls, the patients in the observation group showed significantly lower delirium scores at 2 h (12.72±3.54 vs 10.65±2.87, P<0.05), 6 h (20.17±4.92 vs 14.19±4.64, P<0.01), 12 h (16.82±4.24 vs 10.69±3.18, P<0.01) and 24 h (13.61±2.86 vs 9.13±2.12, P<0.01) after operation, and shorter time to ambulation (ï¼»3.65±1.41ï¼½ vs ï¼»2.84±0.83ï¼½ d, P<0.01) and time of postoperative catheterization (ï¼»6.28±1.65ï¼½ vs ï¼»4.28±1.14ï¼½ d, P<0.01), bladder irrigation (ï¼»3.41±1.08ï¼½ vs ï¼»2.25±0.71ï¼½ d, P<0.01) and hospitalization (ï¼»10.33±2.41ï¼½ vs ï¼»7.82±2.06ï¼½ d, P<0.01). No statistically significant differences were observed between the two groups in either the SAS and SDS scores (P >0.05) or the QOL scores before operation (P >0.05), but the former two were dramatically decreased (P<0.01) while the latter one increased in the observation group postoperatively (P<0.01). Postoperative complications included delirium, bladder spasm, urethral pain, and secondary bleeding, with a significantly lower total incidence rate in the observation than in the control group (12.50% vs 52.08%, P<0.01). CONCLUSION: Cluster nursing care based on 10S CQI can promote the postoperative recovery of BPH patients, improve their psychological status and quality of life, and reduce the incidence of delirium and complications.
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Delírio , Complicações Pós-Operatórias , Hiperplasia Prostática , Melhoria de Qualidade , Qualidade de Vida , Humanos , Masculino , Delírio/prevenção & controle , Delírio/epidemiologia , Delírio/etiologia , Complicações Pós-Operatórias/prevenção & controle , Complicações Pós-Operatórias/epidemiologia , Incidência , Hiperplasia Prostática/cirurgia , Ressecção Transuretral da Próstata , Cuidados de Enfermagem , IdosoRESUMO
BACKGROUND: Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. PATIENTS AND METHODS: This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. RESULTS: In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. CONCLUSIONS: PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.
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Neoplasias da Mama , Imageamento por Ressonância Magnética Multiparamétrica , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/patologia , Meios de Contraste , Sensibilidade e Especificidade , Estudos Prospectivos , Ultrassonografia Mamária/métodos , Imageamento por Ressonância Magnética/métodosRESUMO
PURPOSES: To develop a nomogram model for distinguishing benign from malignant ampullary lesions more intuitive and accurate. MATERIALS AND METHODS: A total of 124 patients with periampullary lesions from January 2016 to June 2020 were enrolled in this retrospective study. Their clinical information, ultrasound (US), dual contrast-enhanced ultrasound (DCEUS) and MRI image features were used for research. Twenty features were collected in our study. Random forest was used to select the first five most important indicators to construct the prediction model. RESULTS: Patients' age, common bile duct (CBD) diameter, the shape, vascularity, and boundary of lesion, lesion size with or without enlarged after CEUS, the enhancement patterns of arterial phase, the washout patterns of venous phase, CEUS diagnosis, and MRI diagnosis were statistically significant (p < 0.05). After screening for statistically significant indicators by random forest, the first five most important indicators were age, CBD diameter, the enhancement patterns of arterial phase, the washout patterns of venous phase, lesion size with or without enlarged after CEUS, which were used to construct nomogram. The area under curves (AUC) and 95% confidence intervals (CI) for nomogram, MRI + MRCP + DCEUS, DCEUS, MRI + MRCP were 0.98(0.94-1.00), 0.91(0.84-0.97), 0.89(0.80-0.98), 0.68(0.60-0.77), respectively. The sensitivity and specificity were 100.00% and 84.62% for nomogram, 88.29% and 92.31% for MRI + MRCP+DCEUS, 86.49% and 92.31% for DCEUS, 51.35%, and 100.00% for MRI + MRCP. CONCLUSIONS: We combined clinical indicators, gray-scale ultrasound characteristics, and CEUS characteristics to build the nomogram, which can be intuitively and accurately used for preoperative malignant prediction of ampullary lesion patients, worthy of clinical generalizability and application.
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Meios de Contraste , Nomogramas , Humanos , Imageamento por Ressonância Magnética/métodos , Estudos Retrospectivos , Ultrassonografia/métodosRESUMO
OBJECTIVES: This study aimed to evaluate the clinical value of the elastographic Q-analysis score (EQS) in assisting real-time elastography- and transrectal US-guided prostate biopsy. METHODS: A total of 125 patients with 301 lesions were enrolled in this study; all were confirmed by pathologic results. The patients underwent transrectal US and elastographic examinations before biopsy. Elastographic Q-analysis score analysis software was used for measuring the mean EQS of the elastic images. First, the suspicious regions on elastography underwent biopsy. Then 12-core systematic prostate biopsy was performed. An EQS curve was used to calculate the mean EQS, and a receiver operating characteristic curve was drawn to find the cutoff point for the EQS to predict prostate cancer. RESULTS: Of the 301 lesions in this study, 125 were malignant, and 176 were benign. The mean EQS values of benign and malignant lesions ± SD were 1.47 ± 0.75 and 2.98 ± 1.06, respectively. The difference was statistically significant (P < .05). The area under the receiver operating characteristic curve was 0.87. When the cutoff point was 1.95 for diagnosing malignant and benign lesions, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were 83.5%, 84.4%, 76.8%, 89.2%, 5.35, and 0.20. CONCLUSIONS: The EQS could be used as a way to predict benign and malignant lesions and thus could serve as guidance for adding targeted biopsy.
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Técnicas de Imagem por Elasticidade/métodos , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/patologia , Ultrassonografia de Intervenção/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Biópsia , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Próstata/diagnóstico por imagem , Próstata/patologia , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e EspecificidadeRESUMO
Abnormal expression of long noncoding RNAs (lncRNAs) has been confirmed to be associated with many diseases, including chronic obstructive pulmonary disease (COPD). To gain better understanding of the mechanism of COPD, we investigated the lncRNA and mRNA profiles in the lung tissue of patients with COPD. According to the analysis, one of the significantly different lncRNAs, COPDA1, might participate in the occurrence and development of COPD. Lung tissues were collected from nonsmokers, smokers, or smokers with COPD for RNA sequencing. Bioinformatic analysis and cell experiments were used to define the function of COPDA1, and the effects of COPDA1 on intracellular Ca2+ concentration and cell proliferation were examined after knockdown or overexpression of COPDA1. A number of variations of lncRNAs were found in the comparison of nonsmokers, smokers, and smokers with COPD. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses indicated that smoking was involved in the activation of cytokines and the cell cycle, which is associated with COPD. According to the lncRNA-mRNA-coexpressing network and enrichment analysis, COPDAz1 and one of its target genes, MS4A1 (membrane-spanning 4-domains family, subfamily A) were investigated, and we discovered that the expression of MS4A1 was closely associated with lncRNA COPDA1 expression in human bronchial smooth muscle cells (HBSMCs). Further study showed that lncRNA COPDA1 upregulated the expression of MS4A1 to increase store-operated calcium entry in the HBSMCs, resulting in the promotion of the proliferation of smooth muscle cells as well as of airway remodeling. COPDA1 might be involved in the regulation of certain signaling pathways in COPD, might promote the proliferation of HBSMCs, and might also be involved in facilitating airway remodeling.
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Remodelação das Vias Aéreas/genética , Proliferação de Células/genética , Doença Pulmonar Obstrutiva Crônica/genética , RNA Longo não Codificante/genética , Proliferação de Células/efeitos dos fármacos , Feminino , Perfilação da Expressão Gênica/métodos , Humanos , Pulmão/metabolismo , Masculino , Miócitos de Músculo Liso/metabolismo , Fumar/metabolismoRESUMO
OBJECTIVES: This study retrospectively evaluated the prognostic performance of the ultrasound elastographic Q-analysis score (EQS) combined with the Prostate Imaging Reporting and Data System (PI-RADS) for malignancy risk stratification in prostate nodules based on transrectal ultrasound-magnetic resonance imaging fusion imaging. METHODS: Sixty-two patients who were suspected to have PCa between October 2017 and May 2018 in our hospital were retrospectively evaluated. The performance of the EQS and PI-RADS was evaluated by patients' receiver operating characteristic curves in differentiating malignant and benign prostate nodules. The combination of the EQS and PI-RADS methods for prostate imaging was evaluated. RESULTS: Sixty-two prostate nodules in 62 patients were included. All of the patients underwent biopsy; 29 cases were prostate cancer, and the rest were benign prostate lesions. Both the EQS and PI-RADS were significantly higher in malignant nodules than in benign nodules. The sensitivity, specificity, area under the curve, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, and Youden index of an EQS cutoff of 2.05 were 86.2%, 81.8%, 85.9%, 4.73, 0.169, 80.6%, 87.1%, and 68%, respectively. The corresponding numbers for a PI-RADS cutoff of 4 were 82.7%, 69.7%, 84.2%, 2.72, 0.25, 70.6%, 82.1%, and 52.4%. The "tandem" method had a higher diagnostic specificity (87.9%), positive likelihood ratio (6.55), and positive predictive value (85.1%). The "parallel" method had a higher diagnostic sensitivity (96.5%), negative likelihood ratio (0.06), and negative predictive value (95.2%). CONCLUSIONS: both the EQS and PI-RADS had good diagnostic performance in differentiating between malignant and benign prostate lesions. The combination of the EQS and PI-RADS improved the diagnostic performance to a certain degree.
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Imageamento por Ressonância Magnética/métodos , Imagem Multimodal/métodos , Neoplasias da Próstata/diagnóstico por imagem , Sistemas de Informação em Radiologia , Ultrassonografia/métodos , Idoso , Sistemas de Dados , Diagnóstico Diferencial , Técnicas de Imagem por Elasticidade/métodos , Humanos , Masculino , Pessoa de Meia-Idade , Próstata/diagnóstico por imagem , Reprodutibilidade dos Testes , Estudos Retrospectivos , Medição de Risco , Sensibilidade e EspecificidadeRESUMO
BACKGROUND: The contribution of airway remodeling in chronic obstructive pulmonary disease (COPD) has been well documented, with airway smooth muscle cell proliferation and migration playing a role in the remodeling process. Here, we aimed to verify the effects of fine particulate matter (PM2.5) on human bronchial smooth muscle cell (HBSMC) migration and to explore the underlying signaling pathways. METHODS: HBSMC apoptosis, proliferation and migration were measured using flow cytometry, cell counting and transwell migration assays, respectively. The role of the hedgehog pathway in cell migration was assessed by western blotting to measure the expression of Sonic hedgehog (Shh), Gli1 and Snail. Furthermore, siRNA was used to knock down Gli1 or Snail expression. RESULTS: PM2.5 induced HBSMC apoptosis in a dose-dependent manner, although certain concentrations of PM2.5 did not induce HBSMC proliferation or apoptosis. Interestingly, cell migration was stimulated by PM2.5 doses far below those that induced apoptosis. Additional experiments revealed that these PM2.5 doses enhanced the expression of Shh, Gli1 and Snail in HBSMCs. Furthermore, PM2.5-induced cell migration and protein expression were enhanced by recombinant Shh and attenuated by cyclopamine. Similar results were obtained by knocking down Gli1 or Snail. CONCLUSIONS: These findings suggest that PM2.5, which may exert its effects through the Shh signaling pathway, is necessary for the migration of HBSMCs. These data define a novel role for PM2.5 in airway remodeling in COPD.
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Brônquios/metabolismo , Movimento Celular/fisiologia , Proteínas Hedgehog/metabolismo , Miócitos de Músculo Liso/metabolismo , Material Particulado/toxicidade , Transdução de Sinais/fisiologia , Brônquios/efeitos dos fármacos , Brônquios/patologia , Movimento Celular/efeitos dos fármacos , Células Cultivadas , Relação Dose-Resposta a Droga , Humanos , Miócitos de Músculo Liso/efeitos dos fármacos , Transdução de Sinais/efeitos dos fármacosRESUMO
BACKGROUND/AIMS: The proliferation of human bronchial smooth muscle cells (HBSMCs) is a key pathophysiological component of airway remodeling in chronic obstructive pulmonary disease (COPD) for which pharmacotherapy is limited, and only slight improvements in survival have been achieved in recent decades. Cigarette smoke is a well-recognized risk factor for COPD; however, the pathogenesis of cigarette smoke-induced COPD remains incompletely understood. This study aimed to investigate the mechanisms by which nicotine affects HBSMC proliferation. METHODS: Cell viability was assessed with a CCK-8 assay. Proliferation was measured by cell counting and EdU immunostaining. Fluorescence calcium imaging was performed to measure intracellular Ca2+ concentration ([Ca2+]i). RESULTS: The results showed that nicotine promotes HBSMC proliferation, which is accompanied by elevated store-operated calcium entry (SOCE), receptor-operated calcium entry (ROCE) and basal [Ca2+]i in HBSMCs. Moreover, we also confirmed that canonical transient receptor potential protein 6 (TRPC6) and α7 nicotinic acetylcholine receptor (α7 nAChR) are involved in nicotine-induced upregulation of cell proliferation. Furthermore, we verified that activation of the PI3K/Akt signaling pathway plays a pivotal role in nicotine-enhanced proliferation and calcium influx in HBSMCs. Inhibition of α7 nAChR significantly decreased Akt phosphorylation levels, and LY294002 inhibited the protein expression levels of TRPC6. CONCLUSION: Herein, these data provide compelling evidence that calcium entry via the α7 nAChR-PI3K/Akt-TRPC6 signaling pathway plays an important role in the physiological regulation of airway smooth muscle cell proliferation, representing an important target for augmenting airway remodeling.
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Cálcio/metabolismo , Proliferação de Células/efeitos dos fármacos , Nicotina/toxicidade , Canal de Cátion TRPC6/metabolismo , Receptor Nicotínico de Acetilcolina alfa7/metabolismo , Sobrevivência Celular/efeitos dos fármacos , Células Cultivadas , Cromonas/farmacologia , Diglicerídeos/farmacologia , Humanos , Morfolinas/farmacologia , Miócitos de Músculo Liso/citologia , Miócitos de Músculo Liso/metabolismo , Imagem Óptica , Fosfatidilinositol 3-Quinases/metabolismo , Inibidores de Fosfoinositídeo-3 Quinase , Fosforilação , Proteínas Proto-Oncogênicas c-akt/metabolismo , Interferência de RNA , RNA Interferente Pequeno/metabolismo , Transdução de Sinais/efeitos dos fármacos , Canal de Cátion TRPC6/antagonistas & inibidores , Canal de Cátion TRPC6/genética , Regulação para Cima/efeitos dos fármacos , Receptor Nicotínico de Acetilcolina alfa7/antagonistas & inibidores , Receptor Nicotínico de Acetilcolina alfa7/genéticaRESUMO
BACKGROUND: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.
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Neoplasias da Mama , Calcinose , Carcinoma Ductal de Mama , Carcinoma Intraductal não Infiltrante , Patologia Cirúrgica , Humanos , Feminino , Carcinoma Intraductal não Infiltrante/diagnóstico por imagem , Carcinoma Intraductal não Infiltrante/cirurgia , Estudos Retrospectivos , Mamografia , Neoplasias da Mama/diagnóstico por imagemRESUMO
Background: This study developed and validated an ultrasound nomogram based on conventional ultrasound and dual-mode elastography to differentiate breast masses. Methods: The data of 234 patients were collected before they underwent breast mass puncture or surgery at 4 different centers between 2016 and 2021. Patients were divided into 5 datasets: internal validation and development sets from the same hospital, and external validation sets from the 3 other hospitals. In the development cohort, age and 294 different ultrasound and elastography features were obtained from ultrasound images. Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used for data reduction and visualization. Multivariable logistic regression analysis was used to develop the prediction model and ultrasound nomogram. Receiver operating characteristic (ROC) curve analysis, calibration curves, integrated discrimination improvement, and the net reclassification index were used to evaluate nomogram performance; decision curve analysis (DCA) and clinical impact curves were used to estimate clinical usefulness. Results: In the development cohort, margin, posterior features, shape, vascularity, (the mean shear wave elastography value of 1.5 mm surrounding tissues in a breast mass) divided by (the mean shear wave elastography value of the breast mass)-shell mean/A mean1.5(E), (the ratio of strain elastography of adipose tissue near a breast mass) divided by [the ratio of strain elastography of (the breast mass adds the 1.5 mm surrounding tissues in the breast mass)]-B/A'1.5 were selected as predictors in multivariable logistic regression analysis, comprising Model 1. Among the 5 cohorts, Model 1 performed best, with areas under the curve (AUC) of 0.92, 0.84, 0.87, 0.93, and 0.89, respectively. The AUCs were 0.90, 0.82, 0.83, 0.91, and 0.85, respectively, in Model 2 (margin + posterior features + shape + vascularity) and 0.80, 0.76, 0.77, 0.87, and 0.80, respectively, in Model 3 [shell mean/A mean1.5(E) + B/A'1.5]. Conclusions: Our ultrasound nomograms facilitate exposure to the features and visualization of breast cancer. Shell mean/A mean1.5(E), B/A'1.5 integrated with margin, posterior features, shape, and vascularity are superior at identifying breast cancer, and are worthy of further clinical investigation.
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The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.
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Purpose: The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer. Materials and methods: During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions. Results: The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagnosis performance at 448×448pixel REZ. Conclusion: Our study reveals a significant correlation between image resolution and breast cancer diagnosis accuracy. Through the comparison of MobileNet and DenseNet121, it is highlighted that lightweight neural networks (LW-CNNs) can achieve model performance similar to or even slightly better than large neural networks models (HW-CNNs) in ultrasound images, and LW-CNNs' prediction time per image is lower.
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BACKGROUND AND OBJECTIVE: The value of implementing artificial intelligence (AI) on ultrasound screening for thyroid cancer has been acknowledged, with numerous early studies confirming AI might help physicians acquire more accurate diagnoses. However, the black box nature of AI's decision-making process makes it difficult for users to grasp the foundation of AI's predictions. Furthermore, explainability is not only related to AI performance, but also responsibility and risk in medical diagnosis. In this paper, we offer Explainer, an intrinsically explainable framework that can categorize images and create heatmaps highlighting the regions on which its prediction is based. METHODS: A dataset of 19341 thyroid ultrasound images with pathological results and physician-annotated TI-RADS features is used to train and test the robustness of the proposed framework. Then we conducted a benign-malignant classification study to determine whether physicians perform better with the assistance of an explainer than they do alone or with Gradient-weighted Class Activation Mapping (Grad-CAM). RESULTS: Reader studies show that the Explainer can achieve a more accurate diagnosis while explaining heatmaps, and that physicians' performances are improved when assisted by the Explainer. Case study results confirm that the Explainer is capable of locating more reasonable and feature-related regions than the Grad-CAM. CONCLUSIONS: The Explainer offers physicians a tool to understand the basis of AI predictions and evaluate their reliability, which has the potential to unbox the "black box" of medical imaging AI.
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Médicos , Neoplasias da Glândula Tireoide , Humanos , Inteligência Artificial , Reprodutibilidade dos Testes , Ultrassonografia , Neoplasias da Glândula Tireoide/diagnóstico por imagemRESUMO
BACKGROUND: This study aimed to assess the diagnostic value of dual-mode elastography for benign and malignant breast lesions and determine whether this technique can improve the diagnostic ability of physicians with different levels of experience. METHODS: One hundred and eighty-three breast lesions were analyzed retrospectively, and the following values were calculated for the lesions with various shells: shear modulus (G), Young's modulus (E), shear wave velocity (Cs), and strain ratio (SR). A random forest algorithm was used to select the optimal modes for elastography. A receiver operating characteristic curve was used to assess the diagnostic efficacy for benign and malignant breast lesions. Sensitivity and specificity values were calculated to evaluate any improvements in the diagnostic efficacy of physicians with different levels of experience (junior, intermediate-level, and senior) in the evaluation of malignant breast lesions using dual-mode elastography. RESULTS: The best-performing mode of shear wave elastography (SWE) in the diagnosis of breast lesions was the A'min 1.0 (Cs) mode (minimum shear wave velocity of the area of interest and 1.0 mm around the area of interest), and the best-performing mode of strain elastography (SE) was the B/A' 0.5 (ratio of fat to the elasticity of the area of interest and 0.5 mm around the area of interest). When the two methods were used in series, results showed high specificity (98%), positive likelihood ratio (PLR) (21.2), and positive predictive value (PPV) (95%). Series means that if SE and SWE were malignant, the result in series was malignant, and that if either SE or SWE was benign, the result in series was benign. When the methods were used in parallel, the results showed high sensitivity (91%), negative likelihood ratio (NLR) (0.15), and negative predictive value (NPV) (89%). Parallel means that if SE and SWE were benign, the result in parallel was benign, and that if either SE or SWE was malignant, the result in parallel was malignant. When conventional ultrasound was combined with dual-mode elastography, the intermediate-level and junior physicians' diagnoses of breast lesions showed a higher sensitivity, specificity, and area under the curve than conventional ultrasound diagnosis alone. CONCLUSIONS: Dual-mode elastography is effective in the diagnosis of breast lesions. The sensitivity and specificity values in this study show that diagnoses made by junior and intermediate-level physicians improve when dual-mode elastography is used, although diagnoses made by senior physicians do not improve significantly.
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Introduction: We compare the differences in the diagnostic results of S-thyroid, a computer-aided diagnosis (CAD) software, based on two mutually perpendicular planes. Methods: Initially, 149 thyroid nodules confirmed by surgical pathology were enrolled in our study. CAD in our study was based on the ACR TI-RADS lexicon. t test, rank-sum test, and Chi-square test were used. The interclass correlation coefficient and Cohen's kappa were used to explore the correlation between CAD features. Receiver operating characteristic was plotted for different combinations of CAD features. Results: The patient's age, transverse diameter, longitudinal diameter, shape, margin, echogenicity, echogenic foci, composition, TI-RADS classification, and risk probability of nodules in the transverse and longitudinal planes were related to thyroid cancer (p < 0.05). The AUC (95%CI) of TI-RADS classification in the transverse plane of CAD is better than that of the longitudinal plane [0.90 (0.84-0.95) vs. 0.83 (0.77-0.90), p = 0.04]. The AUC (95%CI) of risk probability of nodules in the transverse planes shows no difference from that in the longitudinal plane statistically [0.90 (0.85-0.95) vs. 0.88 (0.82-0.94), p = 0.52]. The AUC (95% CI), specificity, sensitivity, and accuracy [TI-RADS classification (transverse plane) + TI-RADS classification (longitudinal plane) + risk (transverse plane) + risk (longitudinal plane)] are 0.93 (0.89-0.97), 86.15%, 90.48%, and 88.59%, respectively. Conclusion: The diagnosis of thyroid cancer in the CAD transverse plane was superior to that in the CAD longitudinal plane when using the TI-RADS classification, but there was no difference in the diagnosis between the two planes when using risk. However, the combination of CAD transverse and longitudinal planes had the best diagnostic ability.
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Purpose: The purpose of this study was to explore the performance of different parameter combinations of deep learning (DL) models (Xception, DenseNet121, MobileNet, ResNet50 and EfficientNetB0) and input image resolutions (REZs) (224 × 224, 320 × 320 and 488 × 488 pixels) for breast cancer diagnosis. Methods: This multicenter study retrospectively studied gray-scale ultrasound breast images enrolled from two Chinese hospitals. The data are divided into training, validation, internal testing and external testing set. Three-hundreds images were randomly selected for the physician-AI comparison. The Wilcoxon test was used to compare the diagnose error of physicians and models under P=0.05 and 0.10 significance level. The specificity, sensitivity, accuracy, area under the curve (AUC) were used as primary evaluation metrics. Results: A total of 13,684 images of 3447 female patients are finally included. In external test the 224 and 320 REZ achieve the best performance in MobileNet and EfficientNetB0 respectively (AUC: 0.893 and 0.907). Meanwhile, 448 REZ achieve the best performance in Xception, DenseNet121 and ResNet50 (AUC: 0.900, 0.883 and 0.871 respectively). In physician-AI test set, the 320 REZ for EfficientNetB0 (AUC: 0.896, P < 0.1) is better than senior physicians. Besides, the 224 REZ for MobileNet (AUC: 0.878, P < 0.1), 448 REZ for Xception (AUC: 0.895, P < 0.1) are better than junior physicians. While the 448 REZ for DenseNet121 (AUC: 0.880, P < 0.05) and ResNet50 (AUC: 0.838, P < 0.05) are only better than entry physicians. Conclusion: Based on the gray-scale ultrasound breast images, we obtained the best DL combination which was better than the physicians.
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BACKGROUND: Pregnancy and childbirth are the main causes of pelvic floor dysfunction (PFD). Although pelvic floor muscle tension is typically measured at 42 days postpartum to assess the severity of PFD and provide timely rehabilitation, it is still impossible to predict PFD and take targeted preventive measures in clinical practice. A PFD prediction model based on big data obtained in prenatal check-ups was established in this study to allow the formulation of personalized preventive strategies to reduce the incidence of PFD. METHODS: A total of 1,500 women who underwent regular prenatal checkups and examinations for PFD at 42 days postpartum at the Zhuji Maternal and Child Health Hospital between May 2015 and May 2020 were selected. The data from 1,000 of them were selected as the training cohort, and the data from 500 of them were used as the validation cohort. The women were divided into a PFD group and a non-PFD group according to whether PFD was diagnosed at 42 days postpartum. A nomogram prediction model was created using the influencing factors that lead to PFD, and the discrimination and calibration of the nomogram were evaluated through internal and external validation. RESULTS: A total of 389 cases (38.9%) of PFD were included in the training cohort. Multivariate analysis showed that age (odds ratio (OR) =1.896, P<0.001), history of childbirth (OR =4.531, P<0.001), history of constipation (OR =2.475, P<0.001), urinary incontinence during pregnancy (OR =4.416, P<0.001), and biparietal diameter at 32 weeks of gestation (OR =51.672, P=0.012) were independent influencing factors of PFD at 42 days postpartum. These factors were used to establish a nomogram prediction model. This prediction model maintained good discrimination between the training cohort and the external validation cohort (the area under the curve was 0.893 and 0.842 for the training and validation cohorts, respectively). CONCLUSIONS: The study validated that the nomogram prediction model based on the factors influencing PFD can be used to predict PFD at 32 weeks of gestation for timely intervention and prevention of PFD.
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Distúrbios do Assoalho Pélvico , Disfunções Sexuais Fisiológicas , Big Data , Criança , Feminino , Humanos , Recém-Nascido , Nomogramas , Diafragma da Pelve , Período Pós-Parto , GravidezRESUMO
BACKGROUND: This study sought to develop and validate a nomogram combining the elastographic Q-analysis score (EQS), the Prostate Imaging Reporting and Data System (PI-RADS) score, and clinical parameters for the stratification of patients with prostate cancer (PCa). METHODS: A retrospective study was conducted of 375 patients with 375 lesions who underwent volume-navigation transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (MP-MRI)-fusion targeted biopsies between April 2017 and January 2020. Based on a multivariate logistic regression model, a nomogram was created to assess any PCa and high-risk PCa [Gleason score (GS) ≥4+3] using data from patients diagnosed between April 2017 and June 2019 (n=271), and was validated in patients diagnosed after July 2019 (n=104). The nomogram's performance was evaluated based on its discrimination, calibration, and clinical usefulness. RESULTS: The areas under the curve (AUCs) of the nomogram for predicting any PCa and high-risk PCa were 0.949 [95% confidence interval (CI), 0.921 to 0.978] and 0.936 (95% CI, 0.906 to 0.965), respectively, in the training cohort, and 0.946 (95% CI, 0.894 to 0.997) and 0.971 (95% CI, 0.9331 to 1), respectively, in the validation cohort. The nomogram was well calibrated, and no significant difference was found between the predicted and observed probabilities. A decision curve analysis (DCA) for the nomogram with and without the EQS showed that the threshold probability of for any PCa was <67%. CONCLUSIONS: The nomogram that combined elastography-derived and MP-MRI data was more clinically useful than the model based on PI-RADS and clinical parameters alone. Our nomogram could aid urologists to make decisions and avoid unnecessary biopsies.
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BACKGROUND: Urologists face a dilemma when deciding whether prostate biopsy is required for patients with prostate-specific antigen (PSA) levels in the grey zone (4 to 10 ng/mL). METHODS: We retrospectively analyzed data from consecutive patients with PSA levels in grey zone, who underwent targeted multiparametric magnetic resonance imaging (MP-MRI)/transrectal ultrasound (TRUS) fusion biopsy with elastography between November 2017 and December 2019 in our hospital. The patientse data including age, PSA, fPSA (free PSA), fPSA/PSA, PSA density (PSAD), prostate volume, elastography Q-analysis score (EQS), and prostate imaging-reporting and data system (PI-RADS) score were collected. The nomogram was built using logistic regression and the final cohort of patients was randomly divided into a training cohort (70%) and a validation cohort (30%) by R software. The models were evaluated by receiver operating characteristic curve (ROC) analysis and calibration curve analysis. The nomogram was constructed from the best model. RESULTS: The final study cohort consisted of 155 patients (training cohort, 109 patients; validation cohort, 46 patients) with PSA in the grey zone, of which 36 patients were pathologically diagnosed with PCa. The EQS model, -EQS model, +EQS model were built. The +EQS model that consisted of fPSA/PSA, EQS, and PI-RADS score had the best PCa diagnostic accuracy (development and validation, 0.783 and 0.781) and probability score (development and validation, 0.939 vs. 0.622). The new nomogram based on this model was constructed, in which fPSA/PSA ratio had the largest impact, followed by PI-RADS and EQS. CONCLUSIONS: Elastography and pre-biopsy MP-MRI has clinical significance for patients with PSA in the grey zone. The new nomogram, which is based on pre biopsy data including serological analysis, PI-RADS score, and EQS, can be helpful for clinical decision-making to avoid unnecessary biopsy.