Axillary Lymphadenopathy after Pfizer-BioNTech and Moderna COVID-19 Vaccination: MRI Evaluation.

Background COVID-19 vaccination-related axillary lymphadenopathy has become an important problem in cancer imaging. Data are needed to update or support imaging guidelines for conducting appropriate follow-up. Purpose To investigate the prevalence, predisposing factors, and MRI characteristics of COVID-19 vaccination-related axillary lymphadenopathy. Materials and Methods Prospectively collected prevaccination and postvaccination chest MRI scans were secondarily analyzed. Participants who underwent two doses of either the Pfizer-BioNTech or Moderna COVID-19 vaccine and chest MRI from June to October 2021 were included. Enlarged axillary lymph nodes were identified on postvaccination MRI scans compared with prevaccination scans. The lymph node diameter, signal intensity with T2-weighted imaging, and apparent diffusion coefficient (ADC) of the largest enlarged lymph nodes were measured. These values were compared between prevaccination and postvaccination MRI by using the Wilcoxon signed-rank test. Results Overall, 433 participants (mean age, 65 years ± 11 [SD]; 300 men and 133 women) were included. The prevalence of axillary lymphadenopathy in participants 1-14 days after vaccination was 65% (30 of 46). Participants with lymphadenopathy were younger than those without lymphadenopathy (P < .001). Female sex and the Moderna vaccine were predisposing factors (P = .005 and P = .003, respectively). Five or more enlarged lymph nodes were noted in 2% (eight of 433) of participants. Enlarged lymph nodes greater than or equal to 10 mm in the short axis were noted in 1% (four of 433) of participants. The median signal intensity relative to the muscle on T2-weighted images was 4.0; enlarged lymph nodes demonstrated a higher signal intensity (P = .002). The median ADC of enlarged lymph nodes after vaccination in 90 participants was 1.1 × 10-3 mm2/sec (range, 0.6-2.0 × 10-3 mm2/sec), thus ADC values remained normal. Conclusion Axillary lymphadenopathy after the second dose of the Pfizer-BioNTech or Moderna COVID-19 vaccines was frequent within 2 weeks after vaccination, was typically less than 10 mm in size, and had a normal apparent diffusion coefficient. © RSNA, 2022.

Computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists.

BACKGROUND: Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. PURPOSE: To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT). MATERIAL AND METHODS: Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers' sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test. RESULTS: With CAD, less experienced radiologists' sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% (P = 0.007), while the experienced radiologists' sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist (P < 0.001). CONCLUSION: CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.

Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.

BACKGROUND: It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports. METHODS: We performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance. RESULTS: Without order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists. CONCLUSIONS: BERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

Unsupervised Deep Anomaly Detection in Chest Radiographs.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as "Normal," "No Opacity/Not Normal," or "Opacity" by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images.

Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.

BACKGROUND: The usefulness of computer-assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. PURPOSE: To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. STUDY TYPE: Retrospective study. SUBJECTS: There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. FIELD STRENGTH/SEQUENCE: Noncontrast-enhanced 3D time-of-flight (TOF) MRA on 3T MR scanners. ASSESSMENT: In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. STATISTICAL TESTS: Free-response receiver operating characteristic (FROC) analysis. RESULTS: Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. DATA CONCLUSION: We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:948-953.

Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.

We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.

Reversible alterations of the neuronal activity in spontaneous intracranial hypotension.

AIM: The aim of this article is to investigate the pathophysiology underlying the alternation of the cognitive function and neuronal activity in spontaneous intracranial hypotension (SIH). METHODS: Fifteen patients with SIH underwent resting-state functional magnetic resonance imaging and working-memory (WM) test one day before and one month after a surgical operation. Alternation of the cognitive function and spontaneous neuronal activity measured as amplitude of the low-frequency fluctuations (ALFF) and the functional connectivity of the default-mode network (DMN) and frontoparietal networks (FPNs) were evaluated. RESULTS: WM performance significantly improved post-operatively. Whole-brain linear regression analysis of the ALFF revealed a positive correlation between cognitive performance change and ALFF change in the precuneus while a negative correlation was found in the bilateral orbitofrontal cortices (OFCs) and right medial frontal cortex (MFC). The ALFF changes normalised with the WM performance improvement post-operatively. The FPN activity in the right OFC was also increased pre-operatively. Partial correlation analysis revealed a significant correlation between WM performance and right OFC activity controlled for right FPN activity. CONCLUSIONS: The abnormal activity of the OFCs and MFC that is not originating from the synchronous intrinsic network activity, together with the decreased activity of the central node of the DMN, could lead to cognitive impairment in SIH that is reversible through restoration of the cerebrospinal fluid.

The effect of expert knowledge on medical search: medical experts have specialized abilities for detecting serious lesions.

How does domain-specific knowledge influence the experts' performance in their domain of expertise? Specifically, can visual search experts find, with uniform efficiency, any type of target in their domain of expertise? We examined whether acquired knowledge of target importance influences an expert's visual search performance. In some professional searches (e.g., medical screenings), certain targets are rare; one aim of this study was to examine the extent to which experts miss such targets in their searches. In one experiment, radiologists (medical experts) engaged in a medical lesion search task in which both the importance (i.e., seriousness/gravity) and the prevalence of targets varied. Results showed decreased target detection rates in the low prevalence conditions (i.e., the prevalence effect). Also, experts were better at detecting important (versus unimportant) lesions. Results of an experiment using novices ruled out the possibility that decreased performance with unimportant targets was due to low target noticeability/visibility. Overall, the findings suggest that radiologists do not have a generalized ability to detect any type of lesion; instead, they have acquired a specialized ability to detect only those important lesions relevant for effective medical practices.

Investigation of distributed learning for automated lesion detection in head MR images.

In this study, we investigated the application of distributed learning, including federated learning and cyclical weight transfer, in the development of computer-aided detection (CADe) software for (1) cerebral aneurysm detection in magnetic resonance (MR) angiography images and (2) brain metastasis detection in brain contrast-enhanced MR images. We used datasets collected from various institutions, scanner vendors, and magnetic field strengths for each target CADe software. We compared the performance of multiple strategies, including a centralized strategy, in which software development is conducted at a development institution after collecting de-identified data from multiple institutions. Our results showed that the performance of CADe software trained through distributed learning was equal to or better than that trained through the centralized strategy. However, the distributed learning strategies that achieved the highest performance depend on the target CADe software. Hence, distributed learning can become one of the strategies for CADe software development using data collected from multiple institutions.

Axillary Lymphadenopathy after COVID-19 Vaccination: Follow-up for Enlarged Lymph Nodes on MR Imaging.

PURPOSE: The purpose of this study was to investigate the longitudinal MRI characteristic of COVID-19-vaccination-related axillary lymphadenopathy by evaluating the size, T2-weighted signal intensity, and apparent diffusion coefficient (ADC) values. METHODS: COVID-19-vaccination-related axillary lymphadenopathy was observed in 90 of 433 health screening program participants on the chest region of whole-body axial MRIs in 2021, as reported in our previous study. Follow-up MRI was performed at an interval of approximately 1 year after the second vaccination dose from 2022 to 2023. The diameter, signal intensity on T2-weighted images, and ADC of the largest enlarged lymph nodes were measured on chest MRI. The values were compared between the post-vaccination MRI and the follow-up MRI, and statistically analyzed. RESULTS: Out of the 90 participants who had enlarged lymph nodes of 5 mm or larger in short axis after the second vaccination dose, 76 participants (45 men and 31 women, mean age: 61 years) were enrolled in the present study. The median short- and long-axis diameter of the enlarged lymph nodes was 7 mm and 9 mm for post-vaccination MRI and 4 mm and 6 mm for follow-up MRI, respectively. The median signal intensity relative to the muscle on T2-weighted images decreased (5.1 for the initial post-vaccination MRI and 3.6 for the follow-up MRI, P < .0001). The ADC values did not show a notable change and remained in a normal range. CONCLUSION: The enlarged axillary lymph nodes decreased both in size and in signal intensity on T2-weighted images of follow-up MRI. The ADC remained unchanged. Our findings may provide important information to establish evidence-based guidelines for conducting proper assessment and management of post-vaccination lymphadenopathy.

Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study.

BACKGROUND: Previous research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images. OBJECTIVE: We aim to evaluate the image recognition capability of generative pretrained transformer (GPT)-4V, a recent multimodal LLM developed by OpenAI, in the medical field by testing how visual information affects its performance to answer questions in the 117th Japanese National Medical Licensing Examination. METHODS: We focused on 108 questions that had 1 or more images as part of a question and presented GPT-4V with the same questions under two conditions: (1) with both the question text and associated images and (2) with the question text only. We then compared the difference in accuracy between the 2 conditions using the exact McNemar test. RESULTS: Among the 108 questions with images, GPT-4V's accuracy was 68% (73/108) when presented with images and 72% (78/108) when presented without images (P=.36). For the 2 question categories, clinical and general, the accuracies with and those without images were 71% (70/98) versus 78% (76/98; P=.21) and 30% (3/10) versus 20% (2/10; P≥.99), respectively. CONCLUSIONS: The additional information from the images did not significantly improve the performance of GPT-4V in the Japanese National Medical Licensing Examination.

Development and evaluation of an integrated liver nodule diagnostic method by combining the liver segment division and lesion localization/classification models for enhanced focal liver lesion detection.

The purpose of the study was to develop a liver nodule diagnostic method that accurately localizes and classifies focal liver lesions and identifies the specific liver segments in which they reside by integrating a liver segment division algorithm using a four-dimensional (4D) fully convolutional residual network (FC-ResNet) with a localization and classification model. We retrospectively collected data and divided 106 gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance examinations into Case-sets 1, 2, and 3. A liver segment division algorithm was developed using a 4D FC-ResNet and trained with semi-automatically created silver-standard annotations; performance was evaluated using manually created gold-standard annotations by calculating the Dice scores for each liver segment. The performance of the liver nodule diagnostic method was assessed by comparing the results with those of the original radiology reports. The mean Dice score between the output of the liver segment division model and the gold standard was 0.643 for Case-set 2 (normal liver contours) and 0.534 for Case-set 1 (deformed liver contours). Among the 64 lesions in Case-set 3, the diagnostic method localized 37 lesions, classified 33 lesions, and identified the liver segments for 30 lesions. A total of 28 lesions were true positives, matching the original radiology reports. The liver nodule diagnostic method, which integrates a liver segment division algorithm with a lesion localization and classification model, exhibits great potential for localizing and classifying focal liver lesions and identifying the liver segments in which they reside. Further improvements and validation using larger sample sizes will enhance its performance and clinical applicability.

GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination.

PURPOSE: To assess the performance of GPT-4 Turbo with Vision (GPT-4TV), OpenAI's latest multimodal large language model, by comparing its ability to process both text and image inputs with that of the text-only GPT-4 Turbo (GPT-4 T) in the context of the Japan Diagnostic Radiology Board Examination (JDRBE). MATERIALS AND METHODS: The dataset comprised questions from JDRBE 2021 and 2023. A total of six board-certified diagnostic radiologists discussed the questions and provided ground-truth answers by consulting relevant literature as necessary. The following questions were excluded: those lacking associated images, those with no unanimous agreement on answers, and those including images rejected by the OpenAI application programming interface. The inputs for GPT-4TV included both text and images, whereas those for GPT-4 T were entirely text. Both models were deployed on the dataset, and their performance was compared using McNemar's exact test. The radiological credibility of the responses was assessed by two diagnostic radiologists through the assignment of legitimacy scores on a five-point Likert scale. These scores were subsequently used to compare model performance using Wilcoxon's signed-rank test. RESULTS: The dataset comprised 139 questions. GPT-4TV correctly answered 62 questions (45%), whereas GPT-4 T correctly answered 57 questions (41%). A statistical analysis found no significant performance difference between the two models (P = 0.44). The GPT-4TV responses received significantly lower legitimacy scores from both radiologists than the GPT-4 T responses. CONCLUSION: No significant enhancement in accuracy was observed when using GPT-4TV with image input compared with that of using text-only GPT-4 T for JDRBE questions.

Performance changes due to differences among annotating radiologists for training data in computerized lesion detection.

PURPOSE: The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. METHODS: We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. RESULTS: The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. CONCLUSIONS: Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used.

Automated volume measurement of abdominal adipose tissue from entire abdominal cavity in Dixon MR images using deep learning.

The purpose of this study was to realize an automated volume measurement of abdominal adipose tissue from the entire abdominal cavity in Dixon magnetic resonance (MR) images using deep learning. Our algorithm involves a combination of extraction of the abdominal cavity and body trunk regions using deep learning and extraction of a fat region based on automatic thresholding. To evaluate the proposed method, we calculated the Dice coefficient (DC) between the extracted regions using deep learning and labeled images. We also compared the visceral adipose tissue (VAT) and subcutaneous adipose tissue volumes calculated by employing the proposed method with those calculated from computed tomography (CT) images scanned on the same day using the automatic calculation method previously developed by our group. We implemented our method as a plug-in in a web-based medical image processing platform. The DCs of the abdominal cavity and body trunk regions were 0.952 ± 0.014 and 0.995 ± 0.002, respectively. The VAT volume measured from MR images using the proposed method was almost equivalent to that measured from CT images. The time required for our plug-in to process the test set was 118.9 ± 28.0 s. Using our proposed method, the VAT volume measured from MR images can be an alternative to that measured from CT images.

Preliminary study of generalized semiautomatic segmentation for 3D voxel labeling of lesions based on deep learning.

PURPOSE: The three-dimensional (3D) voxel labeling of lesions requires significant radiologists' effort in the development of computer-aided detection software. To reduce the time required for the 3D voxel labeling, we aimed to develop a generalized semiautomatic segmentation method based on deep learning via a data augmentation-based domain generalization framework. In this study, we investigated whether a generalized semiautomatic segmentation model trained using two types of lesion can segment previously unseen types of lesion. METHODS: We targeted lung nodules in chest CT images, liver lesions in hepatobiliary-phase images of Gd-EOB-DTPA-enhanced MR imaging, and brain metastases in contrast-enhanced MR images. For each lesion, the 32 × 32 × 32 isotropic volume of interest (VOI) around the center of gravity of the lesion was extracted. The VOI was input into a 3D U-Net model to define the label of the lesion. For each type of target lesion, we compared five types of data augmentation and two types of input data. RESULTS: For all considered target lesions, the highest dice coefficients among the training patterns were obtained when using a combination of the existing data augmentation-based domain generalization framework and random monochrome inversion and when using the resized VOI as the input image. The dice coefficients were 0.639 ± 0.124 for the lung nodules, 0.660 ± 0.137 for the liver lesions, and 0.727 ± 0.115 for the brain metastases. CONCLUSIONS: Our generalized semiautomatic segmentation model could label unseen three types of lesion with different contrasts from the surroundings. In addition, the resized VOI as the input image enables the adaptation to the various sizes of lesions even when the size distribution differed between the training set and the test set.

Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI.

PURPOSE: Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) has high diagnostic accuracy in the detection of liver lesions. There is a demand for computer-aided detection/diagnosis software for Gd-EOB-DTPA-enhanced MRI. We propose a deep learning-based method using one three-dimensional fully convolutional residual network (3D FC-ResNet) for liver segmentation and another 3D FC-ResNet for simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI. METHODS: We prepared a five-phase (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary phases) series as the input image sets and labeled focal liver lesion (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scars) images as the output image sets. We used 100 cases to train our model, 42 cases to determine the hyperparameters of our model, and 42 cases to evaluate our model. We evaluated our model by free-response receiver operating characteristic curve analysis and using a confusion matrix. RESULTS: Our model simultaneously detected and classified focal liver lesions. In the test cases, the detection accuracy for whole focal liver lesions had a true-positive ratio of 0.6 at an average of 25 false positives per case. The classification accuracy was 0.790. CONCLUSION: We proposed the simultaneous detection and classification of a focal liver lesion in Gd-EOB-DTPA-enhanced MRI using multichannel 3D FC-ResNet. Our results indicated simultaneous detection and classification are possible using a single network. It is necessary to further improve detection sensitivity to help radiologists.

Performance changes due to differences in training data for cerebral aneurysm detection in head MR angiography images.

PURPOSE: The performance of computer-aided detection (CAD) software depends on the quality and quantity of the dataset used for machine learning. If the data characteristics in development and practical use are different, the performance of CAD software degrades. In this study, we investigated changes in detection performance due to differences in training data for cerebral aneurysm detection software in head magnetic resonance angiography images. MATERIALS AND METHODS: We utilized three types of CAD software for cerebral aneurysm detection in MRA images, which were based on 3D local intensity structure analysis, graph-based features, and convolutional neural network. For each type of CAD software, we compared three types of training pattern, which were two types of training using single-site data and one type of training using multisite data. We also carried out internal and external evaluations. RESULTS: In training using single-site data, the performance of CAD software largely and unpredictably fluctuated when the training dataset was changed. Training using multisite data did not show the lowest performance among the three training patterns for any CAD software and dataset. CONCLUSION: The training of cerebral aneurysm detection software using data collected from multiple sites is desirable to ensure the stable performance of the software.

Prospective Study of Spatial Distribution of Missed Lung Nodules by Readers in CT Lung Screening Using Computer-assisted Detection.

PURPOSE: To evaluate the spatial patterns of missed lung nodules in a real-life routine screening environment. MATERIALS AND METHODS: In a screening institute, 4,822 consecutive adults underwent chest CT, and each image set was independently interpreted by two radiologists in three steps: (1) independently interpreted without computer-assisted detection (CAD) software, (2) independently referred to the CAD results, (3) determined by the consensus of the two radiologists. The locations of nodules and the detection performance data were semi-automatically collected using a CAD server integrated into the reporting system. Fisher's exact test was employed for evaluating findings in different lung divisions. Probability maps were drawn to illustrate the spatial distribution of radiologists' missed nodules. RESULTS: Radiologists significantly tended to miss lung nodules in the bilateral hilar divisions (p < 0.01). Some radiologists had their own spatial pattern of missed lung nodules. CONCLUSION: Radiologists tend to miss lung nodules present in the hilar regions significantly more often than in the rest of the lung.

Computer-aided detection of cerebral aneurysms with magnetic resonance angiography: usefulness of volume rendering to display lesion candidates.

PURPOSE: The clinical usefulness of computer-aided detection of cerebral aneurysms has been investigated using different methods to present lesion candidates, but suboptimal methods may have limited its usefulness. We compared three presentation methods to determine which can benefit radiologists the most by enabling them to detect more aneurysms. MATERIALS AND METHODS: We conducted a multireader multicase observer performance study involving six radiologists and using 470 lesion candidates output by a computer-aided detection program, and compared the following three different presentation methods using the receiver operating characteristic analysis: (1) a lesion candidate is encircled on axial slices, (2) a lesion candidate is overlaid on a volume-rendered image, and (3) combination of (1) and (2). The response time was also compared. RESULTS: As compared with axial slices, radiologists showed significantly better detection performance when presented with volume-rendered images. There was no significant difference in response time between the two methods. The combined method was associated with a significantly longer response time, but had no added merit in terms of diagnostic accuracy. CONCLUSION: Even with the aid of computer-aided detection, radiologists overlook many aneurysms if the presentation method is not optimal. Overlaying colored lesion candidates on volume-rendered images can help them detect more aneurysms.