Clinical feature, GALC variant spectrum, and genotype-phenotype correlation in Korean Krabbe disease patients: Multicenter experience over 13 years.

Krabbe disease (KD) is an autosomal recessive neurodegenerative disorder caused by deficiency of the galactocerebrosidase (GALC) due to variants in the GALC gene. Here, we provide the first and the largest comprehensive analysis of clinical and genetic characteristics, and genotype-phenotype correlations of KD in Korean in comparison with other ethnic groups. From June 2010 to June 2023, 10 patients were diagnosed with KD through sequencing of GALC. Clinical features, and results of GALC sequencing, biochemical test, neuroimaging, and neurophysiologic test were obtained from medical records. An additional nine previously reported Korean KD patients were included for review. In Korean KD patients, the median age of onset was 2 years (3 months-34 years) and the most common phenotype was adult-onset (33%, 6/18) KD, followed by infantile KD (28%, 5/18). The most frequent variants were c.683_694delinsCTC (23%) and c.1901T>C (23%), while the 30-kb deletion was absent. Having two heterozygous pathogenic missense variants was associated with later-onset phenotype. Clinical features were similar to those of other ethnic groups. In Korean KD patients, the most common phenotype was the adult-onset type and the GALC variant spectrum was different from that of the Caucasian population. This study would further our understanding of KD.

Deep learning-based fully automated grading system for dry eye disease severity.

There is an increasing need for an objective grading system to evaluate the severity of dry eye disease (DED). In this study, a fully automated deep learning-based system for the assessment of DED severity was developed. Corneal fluorescein staining (CFS) images of DED patients from one hospital for system development (n = 1400) and from another hospital for external validation (n = 94) were collected. Three experts graded the CFS images using NEI scale, and the median value was used as ground truth. The system was developed in three steps: (1) corneal segmentation, (2) CFS candidate region classification, and (3) estimation of NEI grades by CFS density map generation. Also, two images taken on different days in 50 eyes (100 images) were compared to evaluate the probability of improvement or deterioration. The Dice coefficient of the segmentation model was 0.962. The correlation between the system and the ground truth data was 0.868 (p<0.001) and 0.863 (p<0.001) for the internal and external validation datasets, respectively. The agreement rate for improvement or deterioration was 88% (44/50). The fully automated deep learning-based grading system for DED severity can evaluate the CFS score with high accuracy and thus may have potential for clinical application.

Enhancing the Reliability of PMP22 Copy Number Variation Detection with an Inherited Peripheral Neuropathy Panel.

The utility of the next-generation sequencing (NGS) panel could be increased in hereditary peripheral neuropathies, given that the duplication of PMP22 is a major abnormality. In the present study, the analytical performance of an algorithm for detecting PMP22 copy number variation (CNV) from the NGS panel data was evaluated. The NGS panel covers 141 genes, including PMP22 and five genes within 1.5-megabase duplicated region at 17p11.2. CNV calling was performed using a laboratory-developed algorithm. Among the 92 cases subjected to targeted NGS panel from March 2018 to January 2021, 26 were suggestive of PMP22 CNV. Multiplex ligation-dependent probe amplification analysis was performed in 58 cases, and the results were 100% concordant with the NGS data (23 duplications, 2 deletions, and 33 negatives). Analytical performance of the pipeline was further validated by another blind data set, including 14 positive and 20 negative samples. Reliable detection of PMP22 CNV was possible by analyzing not only PMP22 but also the adjacent genes within the 1.5-megabase region of 17p11.2. On the basis of the high accuracy of CNV calling for PMP22, the testing strategy for diagnosis of peripheral polyneuropathies could be simplified by reducing the need for multiplex ligation-dependent probe amplification.

CRYAB stop-loss variant causes rare syndromic dilated cardiomyopathy with congenital cataract: expanding the phenotypic and mutational spectrum of alpha-B crystallinopathy.

Missense mutations in the alpha-B crystallin gene (CRYAB) have been reported in desmin-related myopathies with or without cardiomyopathy and have also been reported in families with only a cataract phenotype. Dilated cardiomyopathy (DCM) is a disorder with a highly heterogeneous genetic etiology involving more than 60 causative genes, hindering genetic diagnosis. In this study, we performed whole genome sequencing on 159 unrelated patients with DCM and identified an unusual stop-loss pathogenic variant in NM_001289808.2:c.527A>G of CRYAB in one patient. The mutant alpha-B crystallin protein is predicted to have an extended strand with addition of 19 amino acid residues, p.(Ter176TrpextTer19), which may contribute to aggregation and increased hydrophobicity of alpha-B crystallin. The proband, diagnosed with DCM at age 32, had a history of bilateral congenital cataracts but had no evidence of myopathy or associated symptoms. He also has a 10-year-old child diagnosed with bilateral congenital cataracts with the same CRYAB variant. This study expands the mutational spectrum of CRYAB and deepens our understanding of the complex phenotypes of alpha-B crystallinopathies.

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNN based heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.

Clinical Practice Guideline for Blood-based Circulating Tumor DNA Assays.

Circulating tumor DNA (ctDNA) has emerged as a promising tool for various clinical applications, including early diagnosis, therapeutic target identification, treatment response monitoring, prognosis evaluation, and minimal residual disease detection. Consequently, ctDNA assays have been incorporated into clinical practice. In this review, we offer an in-depth exploration of the clinical implementation of ctDNA assays. Notably, we examined existing evidence related to pre-analytical procedures, analytical components in current technologies, and result interpretation and reporting processes. The primary objective of this guidelines is to provide recommendations for the clinical utilization of ctDNA assays.

A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture.

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.

Overcoming challenges associated with identifying FBN1 deep intronic variants through whole-genome sequencing.

BACKGROUND: Marfan syndrome (MFS), caused by pathogenic variants of FBN1 (fibrillin-1), is a systemic connective tissue disorder with variable phenotypes and treatment responsiveness depending on the variant. However, a significant number of individuals with MFS remain genetically unexplained. In this study, we report novel pathogenic intronic variants in FBN1 in two unrelated families with MFS. METHODS: We evaluated subjects with suspected MFS from two unrelated families using Sanger sequencing or multiplex ligation-dependent probe amplification of FBN1 and/or panel-based next-generation sequencing. As no pathogenic variants were identified, whole-genome sequencing was performed. Identified variants were analyzed by reverse transcription-PCR and targeted sequencing of FBN1 mRNA harvested from peripheral blood or skin fibroblasts obtained from affected probands. RESULTS: We found causative deep intronic variants, c.6163+1484A>T and c.5788+36C>A, in FBN1. The splicing analysis revealed an insertion of in-frame or out-of-frame intronic sequences of the FBN1 transcript predicted to alter function of calcium-binding epidermal growth factor protein domain. Family members carrying c.6163+1484A>T had high systemic scores including prominent skeletal features and aortic dissection with lesser aortic dilatation. Family members carrying c.5788+36C>A had more severe aortic root dilatation without aortic dissection. Both families had ectopia lentis. CONCLUSION: Variable penetrance of the phenotype and negative genetic testing in MFS families should raise the possibility of deep intronic FBN1 variants and the need for additional molecular studies. This study expands the mutation spectrum of FBN1 and points out the importance of intronic sequence analysis and the need for integrative functional studies in MFS diagnosis.

Differential Diagnosis of Pleural Effusion Using Machine Learning.

Rationale: Differential diagnosis of pleural effusion is challenging in clinical practice. Objectives: We aimed to develop a machine learning model to classify the five common causes of pleural effusions. Methods: This retrospective study collected 49 features from clinical information, blood, and pleural fluid of adult patients who underwent diagnostic thoracentesis between October 2013 and December 2018. Pleural effusions were classified into the following five categories: transudative, malignant, parapneumonic, tuberculous, and other. The performance of five different classifiers, including multinomial logistic regression, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGB), was evaluated in terms of accuracy and area under the receiver operating characteristic curve through fivefold cross-validation. Hybrid feature selection was applied to determine the most relevant features for classifying pleural effusion. Results: We analyzed 2,253 patients (training set, n = 1,459; validation set, n = 365; extra-validation set, n = 429) and found that the LGB model achieved the best performance in both validation and extra-validation sets. After feature selection, the accuracy of the LGB model with the selected 18 features was equivalent to that with all 49 features (mean ± standard deviation): 0.818 ± 0.012 and 0.777 ± 0.007 in the validation and extra-validation sets, respectively. The model's mean area under the receiver operating characteristic curve was as high as 0.930 ± 0.042 and 0.916 ± 0.044 in the validation and extra-validation sets, respectively. In our model, pleural lactate dehydrogenase, protein, and adenosine deaminase levels were the most important factors for classifying pleural effusions. Conclusions: Our LGB model showed satisfactory performance for differential diagnosis of the common causes of pleural effusions. This model could provide clinicians with valuable information regarding the major differential diagnoses of pleural diseases.

Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.

BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF). METHODS: Optical coherence tomography (OCT) images were obtained at baseline and after the loading phase. The first recurrence was defined as the initial appearance of a new retinal hemorrhage or intra/subretinal fluid accumulation after the initial resolution of exudative changes after three loading injections. Standard U-Net architecture was used to identify the three retinal fluid compartments, which include pigment epithelial detachment, subretinal fluid, and intraretinal fluid. To predict the first recurrence of nAMD, classification learning was conducted to determine whether the first recurrence occurred within three months after the loading phase. The recurrence classification architecture was built using ResNet50. The model with retinal regions of interest of the entire region and fluid region on OCT at baseline and after the loading phase is presented. RESULTS: A total of 1,444 eyes of 1,302 patients were included. The mean duration until the first recurrence after the loading phase was 8.20 ± 15.56 months. The recurrence classification system revealed that the model with the fluid region of OCT after the loading phase provided the highest classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.725 ± 0.012. Heatmap analysis revealed that three pathological fluids, subsided choroidal neovascularization lesions, and hyperreflective foci were important areas for the first recurrence. CONCLUSIONS: The deep learning algorithm allowed for the prediction of the first recurrence for three months after the loading phase with adequate feasibility. An automated prediction system may assist in establishing patient-specific treatment plans and the provision of individualized medical care for patients with nAMD.

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data.

Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories-signal property, inter-/intra-position correlation, peak value/interval variability, and demographics-were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning.

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

Reclassification of variants of tumor suppressor genes based on Sanger RNA sequencing without NMD inhibition.

Introduction: RNA sequence analysis can be effectively used to identify aberrant splicing, and tumor suppressor genes are adequate targets considering their loss-of-function mechanisms. Sanger sequencing is the simplest method for RNA sequence analysis; however, because of its insufficient sensitivity in cases with nonsense-mediated mRNA decay (NMD), the use of cultured specimens with NMD inhibition has been recommended, hindering its wide adoption. Method: The results of Sanger sequencing of peripheral blood RNA without NMD inhibition performed on potential splicing variants of tumor suppressor genes were retrospectively reviewed. For negative cases, in which no change was identified in the transcript, the possibility of false negativity caused by NMD was assessed through a review of the up-to-date literature. Results: Eleven potential splice variants of various tumor suppressor genes were reviewed. Six variants were classified as pathogenic or likely pathogenic based on the nullifying effect identified by Sanger RNA sequencing. Four variants remained as variants of uncertain significance because of identified in-frame changes or normal expression of both alleles. The result of one variant was suspected to be a false negative caused by NMD after reviewing a recent study that reported the same variant as causing a nullifying effect on the affected transcript. Conclusion: Although RNA changes found in the majority of cases were expected to undergo NMD by canonical rules, most cases (10/11) were interpretable by Sanger RNA sequencing without NMD inhibition due to incomplete NMD efficiency or allele-specific expression despite highly efficient NMD.

Genetic Diagnosis of Children With Neurodevelopmental Disorders Using Whole Genome Sequencing.

BACKGROUND: Neurodevelopmental disorders (NDDs) have diverse phenotypes. Their genetic diagnoses are often challenged by difficulties of targeting causative genes due to heterogeneous genetic etiologies. The objective of this study was to perform genetic diagnosis of children with NDDs using whole genome sequencing. METHODS: This study included 78 pediatric patients with NDDs and their 152 family members for whole genome sequencing (WGS). All cases except one were families with at least two members. Seventy-five patients had previously undergone other genetic tests besides WGS. Detected variants were classified according to the guidelines of the American College of Medical Genetics and Genomics. RESULTS: Among 78 probands, 26 patients were genetically diagnosed with NDDs through WGS, showing a diagnostic rate of 33.3%. Of them, 22 cases had de novo variants (DNVs) identified through trio analysis. Of these DNVs, half were novel variants. Three structural variants, including a multiexon deletion, a contiguous gene deletion involving 13 Mb, and a retrotransposon insertion, were revealed by WGS. All cases except one had defects in different genes, consistent with the phenotypically diverse nature of NDDs. In addition, three patients were inconclusive, two of them had one likely pathogenic variant in a gene associated with autosomal recessive disease and the other one had no clinical phenotypes associated with the detected DNV. CONCLUSIONS: Our experience demonstrates the advantage of WGS in the diagnosis of NDDs, including detection of copy number variations and also the advantage of trio sequencing for interpretation of DNVs.

Analysis of MicroRNA Signature Differentially Expressed in Pancreatic Islet Cells Treated with Pancreatic Cancer-Derived Exosomes.

Since the majority of patients with pancreatic cancer (PC) develop insulin resistance and/or diabetes mellitus (DM) prior to PC diagnosis, PC-induced diabetes mellitus (PC-DM) has been a focus for a potential platform for PC detection. In previous studies, the PC-derived exosomes were shown to contain the mediators of PC-DM. In the present study, the response of normal pancreatic islet cells to the PC-derived exosomes was investigated to determine the potential biomarkers for PC-DM, and consequently, for PC. Specifically, changes in microRNA (miRNA) expression were evaluated. The miRNA specimens were prepared from the untreated islet cells as well as the islet cells treated with the PC-derived exosomes (from 50 patients) and the healthy-derived exosomes (from 50 individuals). The specimens were subjected to next-generation sequencing and bioinformatic analysis to determine the differentially expressed miRNAs (DEmiRNAs) only in the specimens treated with the PC-derived exosomes. Consequently, 24 candidate miRNA markers, including IRS1-modulating miRNAs such as hsa-miR-144-5p, hsa-miR-3148, and hsa-miR-3133, were proposed. The proposed miRNAs showed relevance to DM and/or insulin resistance in a literature review and pathway analysis, indicating a potential association with PC-DM. Due to the novel approach used in this study, additional evidence from future studies could corroborate the value of the miRNA markers discovered.

Effects of Unripe Black Raspberry Extract Supplementation on Male Climacteric Syndrome and Voiding Dysfunction: A Pilot, Randomized, Double-Blind, Placebo-Controlled Trial.

Male climacteric syndrome (MCS) is a medical condition that can affect middle-aged men whose testosterone levels begin to decline considerably. These symptoms may include fatigue, decreased libido, mood swings, and disturbed sleep. MCS can be managed with lifestyle modifications and testosterone replacement. However, testosterone therapy may cause number of side effects, including an increased risk of cardiovascular issues. This study aims to evaluate the efficacy and safety of unripe black raspberry extract (BRE) against MCS and voiding dysfunction in men with andropause symptoms. A total of 30 subjects were enrolled and randomly assigned to the BRE group (n = 15) or the placebo group (n = 15). Participants were supplemented with 4800 mg BRE or placebo twice daily for 12 weeks. The impact of BRE was assessed using the Aging Male's Symptoms (AMS scale), International Prostate Symptom Score (IPSS) and the IPSS quality of life index (IPSS-QoL). Additionally, male sex hormones, lipid profiles, and anthropometric indices were assessed 6 and 12 weeks after treatment. The AMS scores did not differ significantly between the two groups. In the BRE group, the total IPSS and IPSS-QoL scores decreased significantly after 12 weeks compared to baseline (p < 0.05), but there was no significant difference compared to the placebo group. However, a significant difference was observed in the IPSS voiding symptoms sub-score compared to the placebo group. Furthermore, LDL-C and TC levels were also significantly lower in the BRE group than in the placebo group (p < 0.05). Collectively, the study provides strong evidence supporting the safety of BRE as a functional food and its supplementation potentially enhances lipid metabolism and alleviates MCS and dysuria symptoms, limiting the development of BPH.

Whole-genome sequencing in clinically diagnosed Charcot-Marie-Tooth disease undiagnosed by whole-exome sequencing.

Whole-genome sequencing is the most comprehensive form of next-generation sequencing method. We aimed to assess the additional diagnostic yield of whole-genome sequencing in patients with clinically diagnosed Charcot-Marie-Tooth disease when compared with whole-exome sequencing, which has not been reported in the literature. Whole-genome sequencing was performed on 72 families whose genetic cause of clinically diagnosed Charcot-Marie-Tooth disease was not revealed after the whole-exome sequencing and 17p12 duplication screening. Among the included families, 14 (19.4%) acquired genetic diagnoses that were compatible with their phenotypes. The most common factor that led to the additional diagnosis in the whole-genome sequencing was genotype-driven analysis (four families, 4/14), in which a wider range of genes, not limited to peripheral neuropathy-related genes, were analysed. Another four families acquired diagnosis due to the inherent advantage of whole-genome sequencing such as better coverage than the whole-exome sequencing (two families, 2/14), structural variants (one family, 1/14) and non-coding variants (one family, 1/14). In conclusion, an evident gain in diagnostic yield was obtained from whole-genome sequencing of the whole-exome sequencing-negative cases. A wide range of genes, not limited to inherited peripheral neuropathy-related genes, should be targeted during whole-genome sequencing.

The Diagnostic Yield and Difficulties of Utilizing Soft-clipped Read Clusters Encountered in Clinical Exome Sequencing.

BACKGROUND: Despite the wide use of next generation sequencing, there are still many difficulties in detecting structural variants. A split read is one of the clues of structural variants and is represented as a soft-clipped read in the raw sequencing data. Considering that most of the breakpoints of structural variants reside in non-coding regions, split read information has not been routinely used in exome sequencing or targeted panel sequencing. Recently, SCRAMble, a software capable of detecting mobile element insertion (MEI) and deletion based on soft-clipped read clusters (SCRCs), was shown to provide an additional diagnostic yield of 0.03 - 0.25%. SCRAMble is the only software that can be used for exome sequencing or targeted panel sequencing to detect structural variants based on SCRC information. The aim of present study was to establish a working procedure of utilizing SCRC information using SCRAMble in clinical exome sequencing and to assess its diagnostic yield. METHODS: Raw sequencing data of clinical exome sequencing were retrospectively analyzed using SCRAMble to search MEIs and deletions. SCRAMble software was installed according to the manufacturer's instructions and default parameters except for one, mei-score, which was adjusted for sensitivity, were used. RefSeq gene annotation was performed for both MEI and deletion calls using ANNOVAR. Blacklist-based filtering was used to reduce candidate MEI/deletion calls. Clinical relevance was manually evaluated for the remaining variant calls. RESULTS: One diagnostic MEI, which is a founder variant in East Asia, was detected in two cases (2/266, 0.75%). In addition, two diagnostic deletions, which had been previously detected in depth-of-coverage (DOC)-based copy number variant (CNV) callers, were detected (2/266, 0.75%). Base-level breakpoints that could not be derived by the DOC-based callers were identified for these two deletions using SCRAMble. Most SCRCs were repetitive among cases and blacklist-based filtering reduced candidate MEI/deletion calls by 49.5 - 94.5%, leaving a considerable number of variant calls to be manually validated. CONCLUSIONS: SCRC screening in exome or targeted panel sequencing may provide additional diagnostic yield either by pathogenic MEI detection or reassurance of deletions identified by DOC-based CNV callers. Development of an efficient filtering algorithm is warranted.

Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis.

Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false positive). Here we showed that transfer learning was more effective than scratch learning in terms of area under curve, sensitivity, specificity and negative/positive predictive value, that about 3900 images were minimally required to demonstrate an acceptable performance, and that algorithm performance can be customized according to the population characteristics by adjusting threshold value.

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.

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.