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Purpose@#Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin–stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. @*Materials and Methods@#A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study. @*Results@#The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis. @*Conclusion@#In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.
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Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
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Objective@#To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. @*Materials and Methods@#This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1–7 according to acquisition conditions. CT images in groups 2–7 were converted into the target CT sty le (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. @*Results@#Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2–7 improved after CT conversion (original vs. converted: 0.63vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists’ scores were significantly higher (P < 0.001) and less variable on converted CT. @*Conclusion@#CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.
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Objective@#To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent twojaw orthognathic surgery. @*Methods@#A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. @*Results@#The total mean error was 1.17 mm without significant difference among the four timepoints (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. @*Conclusions@#The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.
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Background@#To deliver therapeutics into the brain, it is imperative to overcome the issue of the blood-brain-barrier (BBB). One of the ways to circumvent the BBB is to administer therapeutics directly into the brain parenchyma. To enhance the treatment efficacy for chronic neurodegenerative disorders, repeated administration to the target location is required. However, this increases the number of operations that must be performed. In this study, we developed the IntraBrain Injector (IBI), a new implantable device to repeatedly deliver therapeutics into the brain parenchyma. @*Methods@#We designed and fabricated IBI with medical grade materials, and evaluated the efficacy and safety of IBI in 9 beagles. The trajectory of IBI to the hippocampus was simulated prior to surgery and the device was implanted using 3D-printed adaptor and surgical guides. Ferumoxytol-labeled mesenchymal stem cells (MSCs) were injected into the hippocampus via IBI, and magnetic resonance images were taken before and after the administration to analyze the accuracy of repeated injection. @*Results@#We compared the planned vs. insertion trajectory of IBI to the hippocampus.With a similarity of 0.990 ± 0.001 (mean ± standard deviation), precise targeting of IBI was confirmed by comparing planned vs. insertion trajectories of IBI. Multiple administrations of ferumoxytol-labeled MSCs into the hippocampus using IBI were both feasible and successful (success rate of 76.7%). Safety of initial IBI implantation, repeated administration of therapeutics, and long-term implantation have all been evaluated in this study. @*Conclusion@#Precise and repeated delivery of therapeutics into the brain parenchyma can be done without performing additional surgeries via IBI implantation.
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Objective@#The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. @*Methods@#Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). @*Results@#In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. @*Conclusions@#Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.
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Objective@#Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. @*Materials and Methods@#A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. @*Results@#The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV 1) and FEV 1/forced vital capacity (FVC) (r = -0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV 1 and FEV 1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. @*Conclusion@#The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.
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Tumor localization is challenging in the context of ductal carcinoma in situ (DCIS) treated with breast-conserving surgery. Conventional localization methods are generally performed under the guidance of ultrasonography or mammography and are rarely performed with magnetic resonance imaging (MRI), which is more sensitive than the aforementioned modalities in detecting DCIS. Here, we report the application of MRI-based individualized 3-dimensional (3D)-printed breast surgical guides (BSGs) for patients with breast cancer.We successfully resected indeterminate and suspicious lesions that were only detected using preoperative MRI, and the final histopathologic results confirmed DCIS with clear resection margins. MRI guidance combined with 3D-printed BSGs can be used for DCIS localization, especially for lesions easily detectable using MRI only.
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Tumor localization in patients receiving neoadjuvant chemotherapy (NACT) is challenging because substantial therapeutic remission of the original tumor after NACT is often noted.Currently, there is no guidance device that allows for an accurate estimation of the resection range in breast-conserving surgery after NACT. To increase the accuracy of tumor resection, we used a 3-dimensional-printed breast surgical guide based on magnetic resonance imaging (MRI) in the supine position for a breast cancer patient who underwent breast-conserving surgery after NACT. Using this device, the breast tumor with apparent therapeutic changes after NACT on imaging was successfully removed with clear resection margins by identifying the original tumor site in the affected breast. Irrespective of whether the residual tumor area after NACT is well defined, it is possible to confirm and target the tumor area on pre-NACT MRI using this device.
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Objective@#Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. @*Materials and Methods@#A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. @*Results@#The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV 1) and FEV 1/forced vital capacity (FVC) (r = -0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV 1 and FEV 1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. @*Conclusion@#The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.
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Tumor localization is challenging in the context of ductal carcinoma in situ (DCIS) treated with breast-conserving surgery. Conventional localization methods are generally performed under the guidance of ultrasonography or mammography and are rarely performed with magnetic resonance imaging (MRI), which is more sensitive than the aforementioned modalities in detecting DCIS. Here, we report the application of MRI-based individualized 3-dimensional (3D)-printed breast surgical guides (BSGs) for patients with breast cancer.We successfully resected indeterminate and suspicious lesions that were only detected using preoperative MRI, and the final histopathologic results confirmed DCIS with clear resection margins. MRI guidance combined with 3D-printed BSGs can be used for DCIS localization, especially for lesions easily detectable using MRI only.
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Tumor localization in patients receiving neoadjuvant chemotherapy (NACT) is challenging because substantial therapeutic remission of the original tumor after NACT is often noted.Currently, there is no guidance device that allows for an accurate estimation of the resection range in breast-conserving surgery after NACT. To increase the accuracy of tumor resection, we used a 3-dimensional-printed breast surgical guide based on magnetic resonance imaging (MRI) in the supine position for a breast cancer patient who underwent breast-conserving surgery after NACT. Using this device, the breast tumor with apparent therapeutic changes after NACT on imaging was successfully removed with clear resection margins by identifying the original tumor site in the affected breast. Irrespective of whether the residual tumor area after NACT is well defined, it is possible to confirm and target the tumor area on pre-NACT MRI using this device.
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Objective@#To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). @*Materials and Methods@#The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). @*Results@#The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002).On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. @*Conclusion@#The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.
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Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.
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Objective@#To assess the regional ventilation in patients with asthma-chronic obstructive pulmonary disease (COPD) overlapsyndrome (ACOS) using xenon-ventilation dual-energy CT (DECT), and to compare it to that in patients with COPD. @*Materials and Methods@#Twenty-one patients with ACOS and 46 patients with COPD underwent xenon-ventilation DECT. Theventilation abnormalities were visually determined to be 1) peripheral wedge/diffuse defect, 2) diffuse heterogeneous defect,3) lobar/segmental/subsegmental defect, and 4) no defect on xenon-ventilation maps. Emphysema index (EI), airway wallthickness (Pi10), and mean ventilation values in the whole lung, peripheral lung, and central lung areas were quantified andcompared between the two groups using the Student’s t test. @*Results@#Most patients with ACOS showed the peripheral wedge/diffuse defect (n = 14, 66.7%), whereas patients with COPDcommonly showed the diffuse heterogeneous defect and lobar/segmental/subsegmental defect (n = 21, 45.7% and n = 20,43.5%, respectively). The prevalence of ventilation defect patterns showed significant intergroup differences (p< 0.001). Thequantified ventilation values in the peripheral lung areas were significantly lower in patients with ACOS than in patients withCOPD (p= 0.045). The quantified Pi10 was significantly higher in patients with ACOS than in patients with COPD (p= 0.041);however, EI was not significantly different between the two groups. @*Conclusion@#The ventilation abnormalities on the visual and quantitative assessments of xenon-ventilation DECT differed betweenpatients with ACOS and patients with COPD. Xenon-ventilation DECT may demonstrate the different physiologic changes ofpulmonary ventilation in patients with ACOS and COPD.
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Objective@#Patients with chronic obstructive pulmonary disease (COPD) are known to be at risk of osteoporosis. The purpose of this study was to evaluate the association between thoracic vertebral bone density measured on chest CT (DThorax) and clinical variables, including survival, in patients with COPD. @*Materials and Methods@#A total of 322 patients with COPD were selected from the Korean Obstructive Lung Disease (KOLD) cohort. DThorax was measured by averaging the CT values of three consecutive vertebral bodies at the level of the left main coronary artery with a round region of interest as large as possible within the anterior column of each vertebral body using an in-house software. Associations between DThorax and clinical variables, including survival, pulmonary function test (PFT) results, and CT densitometry, were evaluated. @*Results@#The median follow-up time was 7.3 years (range: 0.1–12.4 years). Fifty-six patients (17.4%) died. DThroax differed significantly between the different Global Initiative for Chronic Obstructive Lung Disease stages. DThroax correlated positively with body mass index (BMI), some PFT results, and the six-minute walk distance, and correlated negatively with the emphysema index (EI) (all p < 0.05). In the univariate Cox analysis, older age (hazard ratio [HR], 3.617; 95% confidence interval [CI], 2.119–6.173, p < 0.001), lower BMI (HR, 3.589; 95% CI, 2.122–6.071, p < 0.001), lower forced expiratory volume in one second (FEV1) (HR, 2.975; 95% CI, 1.682–5.262, p < 0.001), lower diffusing capacity of the lung for carbon monoxide corrected with hemoglobin (DLCO) (HR, 4.595; 95% CI, 2.665–7.924, p < 0.001), higher EI (HR, 3.722; 95% CI, 2.192–6.319, p < 0.001), presence of vertebral fractures (HR, 2.062; 95% CI, 1.154–3.683, p = 0.015), and lower DThorax (HR, 2.773; 95% CI, 1.620–4.746, p < 0.001) were significantly associated with all-cause mortality and lung-related mortality. In the multivariate Cox analysis, lower DThorax (HR, 1.957; 95% CI, 1.075–3.563, p = 0.028) along with older age, lower BMI, lower FEV1, and lower DLCO were independent predictors of all-cause mortality. @*Conclusion@#The thoracic vertebral bone density measured on chest CT demonstrated significant associations with the patients’ mortality and clinical variables of disease severity in the COPD patients included in KOLD cohort.
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In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.
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Purpose@#Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. @*Materials and Methods@#A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). @*Results@#The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. @*Conclusion@#In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting
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Subject(s)
Humans , Emergencies , Emergency Service, Hospital , Emigrants and Immigrants , Ethnicity , Health Services , Healthcare Disparities , Information Systems , Insurance , Intensive Care Units , Mortality , Odds Ratio , Population Groups , Socioeconomic Factors , Wounds and InjuriesABSTRACT
Three-dimensional (3D) printing technology, with additive manufacturing, can aid in the production of various kinds of patient-specific medical devices and implants in medical fields, which cannot be covered by mass production systems for producing conventional devices/implants. The simulator-based medical image demonstrates the anatomical structure of the disease, which can be used for education, diagnosis, preparation of treatment plan and preoperative surgical guide, etc. The surgical guide is used as a patient-specific medical device for guiding incision, resection, insertion, and marking. As 3D printers can output materials that can be inserted into the human body, the patient-specific implant device that reflects the patient's anatomy and surgical plan could be of relevance. In addition, patient-specific aids, including gibs, splints, prostheses, and epitheses, could be used for a better outcome. Finally, bio-printing is also used to cultivate cells to produce functional artificial tissues.