Success Detection Rate Research Articles

Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning

Objective. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization strategies for percutaneous coronary intervention (PCI). We propose a bifurcation detection method using vision transformer (ViT) based deep learning in IVOCT. Approach. Instead of relying on lumen segmentation, the proposed method identifies the bifurcation image using a ViT-based classification model and then estimate bifurcation ostium points by a ViT-based landmark detection model. Main results. By processing 8640 clinical images, the Accuracy and F1-score of bifurcation identification by the proposed ViT-based model are 2.54% and 16.08% higher than that of traditional non-deep learning methods, are similar to the best performance of convolutional neural networks (CNNs) based methods, respectively. The ostium distance error of the ViT-based model is 0.305 mm, which is reduced 68.5% compared with the traditional non-deep learning method and reduced 24.81% compared with the best performance of CNNs based methods. The results also show that the proposed ViT-based method achieves the highest success detection rate are 11.3% and 29.2% higher than the non-deep learning method, and 4.6% and 2.5% higher than the best performance of CNNs based methods when the distance section is 0.1 and 0.2 mm, respectively. Significance. The proposed ViT-based method enhances the performance of bifurcation detection of IVOCT images, which maintains a high correlation and consistency between the automatic detection results and the expert manual results. It is of great significance in guiding the selection of PCI treatment strategies.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. Studies that employed AI for 2D or 3D cephalometric landmark detection were included. The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2mm (1.39mm; 95% confidence interval: 0.85-1.92mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. PROSPERO: CRD42022328800.

Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model's explainability.

The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values. We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-oriented gradient (HOG) values. The study includes evaluation and comparison of these feature descriptions calculated at different contexts namely local, pyramid, and global. The feature descriptor obtained using individual combinations is used to discern between landmark and nonlandmark pixels using classification method. Additionally, this study addresses the opacity of LGBM ensemble tree models across landmarks, introducing SHAP values to enhance interpretability. The performance of feature combinations was assessed using metrics like mean radial error, standard deviation, success detection rate (SDR) (2 mm), and test time. Remarkably, among all the combinations explored, both the HOG and gradient direction operations demonstrated significant performance across all context combinations. At the contextual level, the global texture outperformed the others, although it came with the trade-off of increased test time. The HOG in the local context emerged as the top performer with an SDR of 75.84% compared to others. The presented analysis enhances the understanding of the significance of different features and their combinations in the realm of landmark annotation but also paves the way for further exploration of landmark-specific feature combination methods, facilitated by explainability.

Using a New Deep Learning Method for 3D Cephalometry in Patients With Hemifacial Microsomia.

Deep learning algorithms based on automatic 3D cephalometric marking points about people without craniomaxillofacial deformities have achieved good results. However, there has been no previous report about hemifacial microsomia (HFM). The purpose of this study is to apply a new deep learning method based on a 3D point cloud graph convolutional neural network to predict and locate landmarks in patients with HFM based on the relationships between points. The authors used a PointNet++ model to investigate the automatic 3D cephalometry. And the mean distance error (MDE) of the center coordinate position and the success detection rate (SDR) were used to evaluate the accuracy of systematic labeling. A total of 135 patients were enrolled. The MDE for all 32 landmarks was 1.46 ± 1.308 mm, and 10 landmarks showed SDRs at 2 mm over 90%, and only 4 landmarks showed SDRs at 2 mm under 60%. Compared with the manual reproducibility, the standard distance deviation and coefficient of variation values for the MDE of the artificial intelligence system was 0.67 and 0.43, respectively. In summary, our training sets were derived from HFM computed tomography to achieve accurate results. The 3D cephalometry system based on the graph convolutional network algorithm may be suitable for the 3D cephalometry system in HFM cases. More accurate results may be obtained if the HFM training set is expanded in the future.

Automated Cephalometric Landmark Detection Using Deep Reinforcement Learning.

Accurate cephalometric landmark detection leads to accurate analysis, diagnosis, and surgical planning. Many studies on automated landmark detection have been conducted, however reinforcement learning-based networks have not yet been applied. This is the first study to apply deep Q-network (DQN) and double deep Q-network (DDQN) to automated cephalometric landmark detection to the best of our knowledge. The performance of the DQN-based network for cephalometric landmark detection was evaluated using the IEEE International Symposium of Biomedical Imaging (ISBI) 2015 Challenge data set and compared with the previously proposed methods. Furthermore, the clinical applicability of DQN-based automated cephalometric landmark detection was confirmed by testing the DQN-based and DDQN-based network using 500-patient data collected in a clinic. The DQN-based network demonstrated that the average mean radius error of 19 landmarks was smaller than 2mm, that is, the clinically accepted level, without data augmentation and additional preprocessing. Our DQN-based and DDQN-based approaches tested with the 500-patient data set showed the average success detection rate of 67.33% and 66.04% accuracy within 2mm, respectively, indicating the feasibility and potential of clinical application.

Using a New Deep Learning Method for 3D Cephalometry in Patients With Cleft Lip and Palate.

Deep learning algorithms based on automatic 3-dimensional (D) cephalometric marking points about people without craniomaxillofacial deformities has achieved good results. However, there has been no previous report about cleft lip and palate. The purpose of this study is to apply a new deep learning method based on a 3D point cloud graph convolutional neural network to predict and locate landmarks in patients with cleft lip and palate based on the relationships between points. The authors used the PointNet++ model to investigate the automatic 3D cephalometric marking points. And the mean distance error of the center coordinate position and the success detection rate (SDR) were used to evaluate the accuracy of systematic labeling. A total of 150 patients were enrolled. The mean distance error for all 27 landmarks was 1.33mm, and 9 landmarks (30%) showed SDRs at 2mm over 90%, and 3 landmarks (35%) showed SDRs at 2mm under 70%. The automatic 3D cephalometric marking points take 16 seconds per dataset. In summary, our training sets were derived from the cleft lip with/without palate computed tomography to achieve accurate results. The 3D cephalometry system based on the graph convolutional neural network algorithm may be suitable for 3D cephalometry system in cleft lip and palate cases. More accurate results may be obtained if the cleft lip and palate training set is expanded in the future.

Viral genome prediction from raw human DNA sequence samples by combining natural language processing and machine learning techniques

Infection with a virus can lead to a range of illnesses in humans, including cancer. When viruses infect a host, they may disrupt normal host function and cause deadly diseases. Understanding complicated viral illnesses requires novel viral genome prediction. Since many of the sequences in assembled contigs from human samples are not identical to known genomes, many assembled contigs are labeled “unknown” by conventional alignments. In this study, sequences from 19 metagenomic investigations were used to create the model proposed here, and these sequences were examined and classified using BLAST. We implemented k-mer counting and the bag-of-words technique using CountVectorizer. As far as we are aware, this work represents the first framework that combines natural language processing (NLP) along with traditional ML classification approaches on raw metagenomic contigs to automatically identify viruses in a variety of human biospecimens. The suggested models are general rather than specialized to a particular viral family. Since the proposed methodology is precise and simple, we may incorporate it into computer-aided diagnosis (CAD) systems to make day-to-day hospital activities easier. In the last stage, binary classification of deoxyribonucleic acid (DNA) with normal and viral genomes was performed using traditional ML classifiers. Using the KNN classifier, the suggested model achieved 98.6% classification accuracy along with 98.5% precision, 98.6% recall, 0.984 F1 score, 0.896 Matthews correlation coefficient, 0.895 kappa, 0.97 classification success index and detection rate of 98.6% for the prediction of viral genomes in DNA. Compared to previously developed ML techniques, the model achieved a significantly greater performance for viral genome prediction.

Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks

IntroductionCone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. MethodsCBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. ResultsThe success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. ConclusionsAlthough PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.

Performance of a Convolutional Neural Network- Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection.

The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically en- abling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used routinely in the diagnosis and treatment of dental and skeletal disorders. In this study, 1620 lateral cephalograms were obtained and 21 landmarks were included. The coordinates of all landmarks in the 1620 films were obtained to establish a labeled data set: 1360 were used as a training set, 140 as a validation set, and 180 as a testing set. A convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection was developed. Mean radial error and success detection rate within the range of 2 mm, 2.5 mm, 3 mm, and 4 mm were used to eval- uate the performance of the model. Presented artificial intelligence system (CranioCatch, Eskişehir, Turkey) could detect 21 anatomic landmarks in a lateral ceph- alometric radiograph. The highest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the sella point as 98.3, 99.4, 99.4, and 99.4, respectively. The mean radial error ± standard deviation value of the sella point was found as 0.616 ± 0.43. The lowest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the Gonion point as 48.3, 62.8, 73.9, and 87.2, respectively. The mean radial error ± standard deviation value of Gonion point was found as 8.304 ± 2.98. Although the success of the automatic landmark detection using the developed artificial intelligence model was not in- sufficient for clinical use, artificial intelligence-based cephalometric analysis systems seem promising to cephalometric analysis which provides a basis for diagnosis, treatment planning, and following-up in clinical orthodontics practice.

Clinical application of MIIRMR as a salvage method in gadolinium-enhanced MRI after intra-tympanic injection

Background Three-dimensional fluid-attenuated inversion-recovery (3 D-FLAIR) and real inversion-recovery (3 D-real IR) sequences are used to detect endolymphatic hydrops (EH), but medium inversion-time inversion-recovery imaging with magnitude reconstruction (MIIRMR) may be more sensitive. Aims We investigated the inner-ear visualisation success rate and EH detection rates of 3 D-FLAIR and 3 D-real IR, and whether salvage MIIRMR could improve EH detection. Materials and Methods Fifty-one patients (102 ears) with episodic or chronic vestibular syndrome were injected intra-tympanically with 8-fold diluted gadolinium, and 3 D-FLAIR and 3 D-real IR images obtained 24-h post-injection. If 3 D-FLAIR inner-ear visualisation failed, additional MIIRMR was performed. The success and EH detection rate increase by MIIRMR was calculated. The diagnostic performance of combined MIIRMR + 3D-FLAIR + 3D-real IR for Meniere’s disease (MD) was evaluated. Results The success rates of 3 D-FLAIR and 3 D-real IR were 88.90% and 72.55%, respectively. MIIRMR increased the success and EH detection rates by 11.10% and 6.86%, respectively. In MD, MIIRMR increased these rates by 10.53% and 10.53%, respectively. 3 D-FLAIR + 3D-real IR + MIIRMR had 92.11% sensitivity, 79.68% specificity, 72.92% positive-predictive value, and 94.44% negative-predictive value for MD diagnosis. Conclusion and significance MIIRMR can improve success and EH detection rates when 3 D-FLAIR fails. Combined MIIRMR + 3D-FLAIR + 3D-real IR is more valuable for diagnosing MD than conventional sequences.

Comparative study of two software for the detection of cephalometric landmarks by artificial intelligence

Manual, tedious cephalometric analyzes of a lack of productivity (errors in plotting and measurement) making the prospect of a fully automated algorithm turning out attractive. The objectives of the study were to evaluate the positioning (accuracy and precision) of common landmarks by two software programs offering detection by artificial intelligence (WebCeph™ and DentaliQ®) compared to a manual reference and then to the comparators. 68 lateral cephalograms were selected, 22 landmarks were distributed manually and then the differences between this gold standard and the points detected by each software were measured, as well as the success detection rate (SDR). Statistical analysis was carried out by “confidence ellipses” and two-tailed t-test (p-value of 5%). In terms of accuracy, WebCeph™ and DentaliQ® show a 2 mm SDR of 57.2% and 66.5% respectively. In terms of trueness, the best results are obtained for S, Na and the incisal edges. Large random errors are found for the points Po, So, ENA, ENP, Ba and Go. Other points like Pog and B show a large vertical dispersion. Overall, a slight advantage goes to DentaliQ® even if the difference is not significant. The detection precision still seems insufficient for an utilization without human supervision. The results are promising on the detection of certain points. The artificial intelligence saves time for the clinician but the change of positioning of points should still be possible. Advances are rapid and will probably be soon for an effective clinical use.

Detecting Essential Landmarks Directly in Thermal Images for Remote Body Temperature and Respiratory Rate Measurement With a Two-Phase System

Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. The IEC 80601-2-59:2017 standard suggests that the regions medially adjacent to the inner canthi of the eyes are robust BT measurement sites. The nostril regions, on the other hand, are often used for RR estimation. However, it is more difficult to automatically identify inner canthi and nostrils in thermal images than in visible-light images, which are rich with exploitable features. In this paper, a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases is introduced. In Phase I, original thermal images were processed in four different ways to enhance facial features to facilitate inner canthus and nostril detection. In Phase II, landmarks of the inner canthi and outer nostril edges were detected in two steps: (1) face detection using the Single Shot Multibox Detector (SSD) and (2) facial landmark detection to locate the inner canthi and outer nostril edges. The face detection, facial landmark detection, and overall system accuracies were evaluated using the intersection over union, normalized Euclidean distance, and success detection rate metrics on a set of 36 thermal images collected from 12 subjects using three different IRTs. Additional validation was performed on a subset of 40 random thermal images from the publicly available Tufts Face Database. The results revealed that the processed images—referred to as ICLIP images—yielded the highest landmark localization accuracy from the four types of processed thermal images, verifying that the system can automatically and accurately estimate the inner canthus and nostril locations in thermal images. The proposed system can be applied in IRT algorithms to provide reliable temperature measurements and RR estimates during infectious disease outbreaks.

Automatic Localization of Cephalometric Landmarks using Convolutional Neural Networks

Experts have brought forward interesting and effective methods to address critical medical analysis problems. One of these fields of research is cephalometric analysis. During the analysis of tooth and the skeletal relationships of the human skull, cephalometric analysis plays an important role as it facilitates the interpretation of bone, tooth, and soft tissue structures of the patient. It is used during oral, craniofacial, and maxillofacial surgery and during treatments in orthodontic and orthopedic departments. The automatic localization of cephalometric landmarks reduces possible human errors and is time saving. To performed automatic localization of cephalometric landmarks, a deep learning model has been proposed inspired by the U-Net model. 19 cephalometric landmarks that are generally manually determined by experts are automatically obtained using this model. The cephalometric X-ray image dataset created under the context of IEEE 2015 International Symposium on Biomedical Imaging (ISBI 2015) is used and data augmentation is applied to it for this experiment. A Success Detection Rate SDR of 74% was achieved in the range of 2 mm, 81.4% in the range of 2.5mm, 86.3% in the range of 3mm, and 92.2% in the range of 4mm.

A universal Wi-Fi fingerprint localization method based on machine learning and sample differences

Wi-Fi technology has become an important candidate for localization due to its low cost and no need of additional installation. The Wi-Fi fingerprint-based positioning is widely used because of its ready hardware and acceptable accuracy, especially with the current fingerprint localization algorithms based on Machine Learning (ML) and Deep Learning (DL). However, there exists two challenges. Firstly, the traditional ML methods train a specific classification model for each scene; therefore, it is hard to deploy and manage it on the cloud. Secondly, it is difficult to train an effective multi-classification model by using a small number of fingerprint samples. To solve these two problems, a novel binary classification model based on the samples’ differences is proposed in this paper. We divide the raw fingerprint pairs into positive and negative samples based on each pair’s distance. New relative features (e.g., sort features) are introduced to replace the traditional pair features which use the Media Access Control (MAC) address and Received Signal Strength (RSS). Finally, the boosting algorithm is used to train the classification model. The UJIndoorLoc dataset including the data from three different buildings is used to evaluate our proposed method. The preliminary results show that the floor success detection rate of the proposed method can reach 99.54% (eXtreme Gradient Boosting, XGBoost) and 99.22% (Gradient Boosting Decision Tree, GBDT), and the positioning error can reach 3.460 m (XGBoost) and 4.022 m (GBDT). Another important advantage of the proposed algorithm is that the model trained by one building’s data can be well applied to another building, which shows strong generalizable ability.

Automatic localization of cephalometric landmarks based on convolutional neural network

Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient. In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method. The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%. In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.

Mean of Subarrays in the Angular Estimation for the Millimeter Wave Communication

In the hierarchical codebook, changing the codeword's beamwidth depends on the codeword position in this codebook. Some articles take two steps to find the desired Angles of Departures and Angles of Arrivals, the coarse search, then the refined search. In any case of searching, the chosen codeword should create the beam space having the same gain in all directions. The subarrays method has been considered to increase beamwidth, ensuring all—constant directional beam gain for the codeword. This paper explores that using the mean of subarray beams rather than complex integration for subarray beams. The simulation also proves that the beams' average success detection rate versus the SNR is equivalent to the subarray beams integration while keeping the higher channel capacity and reduced computations.

Evaluation of automated cephalometric analysis based on the latest deep learning method

To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI). This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR). SDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures. This latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.

MLPAM: A Machine Learning and Probabilistic Analysis Based Model for Preserving Security and Privacy in Cloud Environment

The organizational valuable data needs to be shared with multiple parties and stakeholders in a cloud environment for storage, analysis, and data utilization. However, to ensure the security, preserve privacy while sharing the data effectively among various parties have become formidable challenges. In this article, by utilizing encryption, machine learning, and probabilistic approaches, we propose a novel model that supports multiple participants to securely share their data for distinct purposes. The model defines the access policy and communication protocol among the involved multiple untrusted parties to process the owners' data. The proposed model minimizes the risk associated with the leakage by providing a robust mechanism for prevention coupled with detection. The experimental results demonstrate the efficiency of the proposed model for different classifiers over various datasets. The proposed model ensures high accuracy and precision up to 97% and 100% relatively and secures a significant improvement up to 0.01%, 103%, 151%, 87%, 96%, 43%, and 186% for average probability, average success rate, detection rate, accuracy, precision, recall, and specificity, respectively, compared to the prior works that prove its effectiveness.

Hierarchical codebook using the last layer to improve the angular estimation in MIMO system

Abstract The use of hierarchical codebooks in radio communication is of great interest today. However, using this codebook in transmitters and receivers in real-world transmission environments is a challenge nowadays because it is only suitable for discrete transmission directions. Therefore, the problem is to propose a discrete transmission model with a limited number of physical paths (virtual models), which is typical for the actual environment with many obstacles. The proposed use of hierarchical codebook in combination with the above virtual model can be compared with other traditional beamforming models applied to this model. The Success Detection Rate can be improved by this use of hierarchical codebook.

Utility of circulating tumor DNA (ctDNA) versus tumor tissue genotyping for enrollment of patients with metastatic colorectal cancer (mCRC) to matched clinical trials: SCRUM-Japan GI-SCREEN and GOZILA combined analysis.

4071 Background: We recently reported that ctDNA genotyping had advantages compared with tumor tissue testing in terms of enrollment to matched clinical trials across a wide range of GI cancers (Nakamura Y, et al. ASCO-GI 2020). Here, we investigated the utility of ctDNA genotyping in mCRC in a SCRUM-Japan GI-SCREEN and GOZILA combined analysis. Methods: In GI-SCREEN, tumor tissue genotyping was performed using a next generation sequencing (NGS)-based assay, Oncomine Comprehensive Assay since Feb 2015. In GOZILA, NGS-based ctDNA genotyping was performed using Guardant360 since Feb 2018. All tests were conducted centrally in a CLIA-certified and CAP-accredited laboratory. Patients with actionable alterations were enrolled into matched company-sponsored or investigator-initiated interventional clinical trials. Results: As of Apr 2019, 2,791 mCRC patients (2,754 eligible for analysis) in GI-SCREEN and 470 (464 eligible for analysis) in GOZILA were enrolled. There were no significant differences in baseline patient characteristics between GI-SCREEN and GOZILA. Most of trials affiliated with GI-SCREEN (81%) or GOZILA (78%) targeted the RTK/RAS/RAF pathway. Compared with tumor testing, ctDNA genotyping significantly improved turnaround time (median, 12 vs. 34 days, P < 0.0001), sequencing success rate (96.1 vs. 92.3%, P = 0.002), and detection rate of actionable alterations (73.3 vs. 62.2%, P = 0.02). Among patients with actionable alterations, enrollment to matched clinical trials was achieved in 5.0% in GI-SCREEN and 12.1% in GOZILA ( P < 0.0001). Median time from enrollment in the respective screening study to enrollment in a matched clinical trial was 6.5 months in GI-SCREEN and 0.9 months in GOZILA, respectively ( P < 0.0001). Objective response rate and progression-free survival were similar in both groups (tissue vs. ctDNA; ORR: 18.8 vs. 17.1%, P = 1.00; median PFS: 2.2 vs. 2.2 months, HR=1.05 [95% CI, 0.71–1.55], P = 0.79). Conclusions: For patients with mCRC, ctDNA genotyping had advantages over tissue genotyping with shorter turnaround time and higher sequencing success and actionable alteration detection rate, which were associated with improved clinical trial enrollment without compromising the efficacy. Funding: SCRUM-Japan Funds. Clinical trial information: UMIN000029315 .