Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method.

BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC). METHODS: We proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs. RESULTS: In the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926). CONCLUSIONS: Comparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.

Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.

BACKGROUND: Histological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice. OBJECTIVES: Establish an expeditious histopathological analysis of mutant mice testicular sections stained with commonly available hematoxylin and eosin (H&E) via enhanced deep learning model MATERIALS AND METHODS: Automated segmentation and cellular composition analysis on the testes of six mouse reproductive mutants of key reproductive gene family, DAZ and PUMILIO gene family via H&E-stained mouse testicular sections. RESULTS: We improved the deep learning model with human interaction to achieve better pixel accuracy and reduced annotation time for histologists; revealed distinctive cell composition features consistent with previously published phenotypes for four mutants and novel spermatogenic defects in two newly generated mutants; established a fast spermatogenic defect detection protocol for quantitative and qualitative assessment of testicular defects within 2.5-3 h, requiring as few as 8 H&E-stained testis sections; uncovered novel defects in AcDKO and a meiotic arrest defect in HDBKO, supporting the synergistic interaction of Sertoli Pum1 and Pum2 as well as redundant meiotic function of Dazl and Boule. DISCUSSION: Our testicular compositional analysis not only could reveal spermatogenic defects from staged seminiferous tubules but also from unstaged seminiferous tubule sections. CONCLUSION: Our SCSD-Net model offers a rapid protocol for detecting reproductive defects from H&E-stained testicular sections in as few as 3 h, providing both quantitative and qualitative assessments of spermatogenic defects. Our analysis uncovered evidence supporting the synergistic interaction of Sertoli PUM1 and PUM2 in maintaining average testis size, and redundant roles of DAZ family proteins DAZL and BOULE in meiosis.

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.

BACKGROUND: Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival. METHODS: This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC. RESULTS: Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77. CONCLUSION: This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset.

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform.

Case report: hunting the hidden: surgical treatment of chronic silent thrombus in the left ventricle in a young alcoholic patient with myocardial bridging.

BACKGROUND: A silent left ventricular thrombus is dangerous. The current standard anticoagulation therapy was ineffective in our case or similar, and the outcome was poor. CASE PRESENTATION: A 33-year-old man with a silent left ventricular thrombus was detected incidentally by transthoracic echocardiography. After admission, anti-coagulation with low-molecular-weight heparin therapy was carried out. The CAG revealed 70% systolic stenosis in the middle of the right coronary artery along with myocardial bridging. Unfortunately, an acute left temporal embolism emerged 5 days later, then the patient was transferred to the neurology department for further treatment. One month later, the patient underwent left ventricular thrombectomy, ventricular aneurysm resection, and coronary artery bypass grafting (CABG) and was discharged uneventfully after surgery. CONCLUSIONS: Surgical treatment should be a priority for patients with giant or hypermobile left ventricular thrombus or recurrent systemic emboli.

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

[Multi-tissue segmentation model of whole slide image of pancreatic cancer based on multi task and attention mechanism].

Accurate segmentation of whole slide images is of great significance for the diagnosis of pancreatic cancer. However, developing an automatic model is challenging due to the complex content, limited samples, and high sample heterogeneity of pathological images. This paper presented a multi-tissue segmentation model for whole slide images of pancreatic cancer. We introduced an attention mechanism in building blocks, and designed a multi-task learning framework as well as proper auxiliary tasks to enhance model performance. The model was trained and tested with the pancreatic cancer pathological image dataset from Shanghai Changhai Hospital. And the data of TCGA, as an external independent validation cohort, was used for external validation. The F1 scores of the model exceeded 0.97 and 0.92 in the internal dataset and external dataset, respectively. Moreover, the generalization performance was also better than the baseline method significantly. These results demonstrate that the proposed model can accurately segment eight kinds of tissue regions in whole slide images of pancreatic cancer, which can provide reliable basis for clinical diagnosis.

A novel pipeline for computerized mouse spermatogenesis staging.

MOTIVATION: Differentiating 12 stages of the mouse seminiferous epithelial cycle is vital towards understanding the dynamic spermatogenesis process. However, it is challenging since two adjacent spermatogenic stages are morphologically similar. Distinguishing Stages I-III from Stages IV-V is important for histologists to understand sperm development in wildtype mice and spermatogenic defects in infertile mice. To achieve this, we propose a novel pipeline for computerized spermatogenesis staging (CSS). RESULTS: The CSS pipeline comprises four parts: (i) A seminiferous tubule segmentation model is developed to extract every single tubule; (ii) A multi-scale learning (MSL) model is developed to integrate local and global information of a seminiferous tubule to distinguish Stages I-V from Stages VI-XII; (iii) a multi-task learning (MTL) model is developed to segment the multiple testicular cells for Stages I-V without an exhaustive requirement for manual annotation; (iv) A set of 204D image-derived features is developed to discriminate Stages I-III from Stages IV-V by capturing cell-level and image-level representation. Experimental results suggest that the proposed MSL and MTL models outperform classic single-scale and single-task models when manual annotation is limited. In addition, the proposed image-derived features are discriminative between Stages I-III and Stages IV-V. In conclusion, the CSS pipeline can not only provide histologists with a solution to facilitate quantitative analysis for spermatogenesis stage identification but also help them to uncover novel computerized image-derived biomarkers. AVAILABILITY AND IMPLEMENTATION: https://github.com/jydada/CSS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish.

Salmon and Cod are economically significant world-class fish that have high economic value. It is difficult to accurately sort and process them by appearance during harvest and transportation. Conventional chemical detection means are time-consuming and costly, which greatly affects the cost and efficiency of Fishery production. Therefore, there is an urgent need for smart Fisheries methods which use for the classification of mixed fish. In this paper, near-infrared spectroscopy (NIRS) was used to assess salmon and cod samples. This study aims to evaluate feasibility of a back-propagation neural network (BPNN) and a convolutional neural network (CNN) for identifying different species of fishes by the corresponding spectra in comparison to traditional chemometrics Partial Least Squares. After comparing the effects of different batch sizes, number of convolutional kernels, number of convolutional layers, and number of pooling layers on the classification of NIRS spectra comparing different structures of one-dimensional (1D)-CNN, we propose the 1D-CNN-8 model that is most suitable for the classification of mixed fish. Compared with the results of traditional chemometrics methods and BPNN, the prediction model of the 1D-CNN model can reach 98.00% Accuracy and the parameters are significantly better than others. Meanwhile, the parameters and floating-point operations of the optimal model are both small. Therefore, the improved CNN model based on the NIRS can effectively and quickly identify different kinds of fish samples and contribute to realizing edge computing at the same time.

Deep SED-Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules.

The development of mouse spermatozoa is a continuous process from spermatogonia, spermatocytes, spermatids to mature sperm. Those developing germ cells (spermatogonia, spermatocyte, and spermatids) together with supporting sertoli cells are all enclosed inside seminiferous tubules of the testis, their identification is key to testis histology and pathology analysis. Automated segmentation of all these cells is a challenging task because of their dynamical changes in different stages. The accurate segmentation of testicular cells is critical in developing computerized spermatogenesis staging. In this paper, we present a novel segmentation model, SED-Net, which incorporates a squeeze-and-excitation (SE) module and a dense unit. The SE module optimizes and obtains features from different channels, whereas the dense unit uses fewer parameters to enhance the use of features. A human-in-the-loop strategy, named deep interactive learning, is developed to achieve better segmentation performance while reducing the workload of manual annotation and time consumption. Across a cohort of 274 seminiferous tubules from stages VI to VIII, the SED-Net achieved a pixel accuracy of 0.930, a mean pixel accuracy of 0.866, a mean intersection over union of 0.710, and a frequency weighted intersection over union of 0.878, respectively, in terms of four types of testicular cell segmentation. There is no significant difference between manual annotated tubules and segmentation results by SED-Net in cell composition analysis for tubules from stages VI to VIII. In addition, we performed cell composition analysis on 2346 segmented seminiferous tubule images from 12 segmented testicular section results. The results provided quantitation of cells of various testicular cell types across 12 stages. The rule reflects the cell variation tendency across 12 stages during development of mouse spermatozoa. The method could enable us to not only analyze cell morphology and staging during the development of mouse spermatozoa but also potentially could be applied to the study of reproductive diseases such as infertility.

Exploration of compressive sensing in the classification of frozen fish based on two-dimensional correlation spectrum.

In order to classify imported frozen fish, effectively a spectral data compression method was presented based on two-dimensional correlation spectroscopy. In the experiment, the near-infrared spectral data of Oncorhynchus keta, Oncorhynchus nerka and Oncorhynchus gorbuscha of Salmonidae were collected. And two-dimensional correlation spectroscopy among the three fish samples was constructed. The study found that the auto-correlation peaks intensities at 650 nm, 1724 nm and 1908 nm were almost zero, which were taken as the separation point of the spectra. Therefore, each spectral data is divided into 4 segments and the integral of each segment is obtained. The original spectra of 201 points in each group were compressed into 4 points. Then, the compressed spectral data were input into the support vector machine to establish the discriminant model of three kinds of frozen fish. At the same time, the Competitive Adaptive Reweighted Sampling and the Successive Projections Algorithm were used to screen the original spectra. The classification results were compared with the result of the spectral data compression method of two-dimensional correlation spectroscopy. The result shows: the compression rate of the proposed method is 98.01%; the accuracy rate of support vector machine training set is 100%; the accuracy rate of validation set is up to 100%. The results shows that the proposed spectral data compression method based on two-dimensional correlation spectral technology has high compression rate and accurate classification.

Deep embeddings and logistic regression for rapid active learning in histopathological images.

BACKGROUND AND OBJECTIVE: Recognizing different tissue components is one of the most fundamental and essential works in digital pathology. Current methods are often based on convolutional neural networks (CNNs), which need numerous annotated samples for training. Creating large-scale histopathological datasets is labor-intensive, where interactive data annotation is a potential solution. METHODS: We propose DELR (Deep Embedding-based Logistic Regression) to enable rapid model training and inference for histopathological image analysis. DELR utilizes a pretrained CNN to encode images as compact embeddings with low computational cost. The embeddings are then used to train a Logistic Regression model efficiently. We implemented DELR in an active learning framework, and validated it on three histopathological problems (binary, 4-category, and 8-category classification challenge for lung, breast, and colorectal cancer, respectively). We also investigated the influence of active learning strategy and type of the encoder. RESULTS: On all the three datasets, DELR can achieve an area under curve (AUC) metric higher than 0.95 with only 100 image patches per class. Although its AUC is slightly lower than a fine-tuned CNN counterpart, DELR can be 536, 316, and 1481 times faster after pre-encoding. Moreover, DELR is proved to be compatible with a variety of active learning strategies and encoders. CONCLUSIONS: DELR can achieve comparable accuracy to CNN with rapid running speed. These advantages make it a potential solution for real-time interactive data annotation.

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images.

BACKGROUND AND OBJECTIVE: Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better risk stratification, prognosis prediction, and therapy management. In this paper, we focused on the automatic evaluation of TME in giga-pixel digital histopathology whole-slide images. METHODS: A convolutional neural network is used to recognize nine different content presented in colon cancer whole-slide images. Several implementation details, including the foreground filtering and stain normalization are discussed. Based on the whole-slide segmentation, several TME descriptors are quantified and correlated with the clinical outcome by Kaplan-Meier analysis and Cox regression. Specifically, the stroma, tumor, necrosis, and lymphocyte components are discussed. RESULTS: We validated the method on colon adenocarcinoma cases from The Cancer Genome Atlas project. The result shows that the stroma is an independent predictor of progression-free interval (PFI) after corrected by age and pathological stage, with a hazard ratio of 1.665 (95%CI: 1.110~2.495, p = 0.014). High-level necrosis component and lymphocytes component tend to be correlated with poor PFI, with a hazard ratio of 1.552 (95%CI: 0.943~2.554, p = 0.084) and 1.512 (95%CI: 0.979~2.336, p = 0.062), respectively. CONCLUSIONS: The result reveals the complex role of the tumor microenvironment in colon adenocarcinoma, and the quantified descriptors are potential predictors of disease progression. The method could be considered for risk stratification and targeted therapy and extend to other types of cancer, leading to a better understanding of the tumor microenvironment.

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury.

Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.

Outcomes of Common Atrioventricular Valve Repair in Patients With Single-Ventricle Physiology　- Indication, Timing and Repair Techniques.

BACKGROUND: Common atrioventricular valve (CAVV) repair in patients with a single ventricle remains a great challenge and a refractory issue for pediatric cardiac surgeons. Methods and Results: From January 2007 to April 2018, 37 consecutive patients with a single ventricle who underwent CAVV repair were included in the study group. Patients were divided into 2 groups based on the repair technique: patients in Group A were treated using the bivalvation technique, and patients in Group B underwent conventional repair techniques; baseline data were similar between groups. The inhospital and follow-up mortality were 5.4% (2/37) and 11.4% (4/35), respectively. After a follow-up of 65.5±29.3 months, the estimated 1-, 5-, and 10-year overall survival rates were 94.6%, 83.4%, and 77.0%, respectively. The rates of freedom from CAVV failure were 94.3%, 72.7%, and 62.9% after 1, 5, and 10 years, respectively. In the multivariate analysis, the independent factors for CAVV repair failure were repair technique (P=0.004) and heterotaxy syndrome (P=0.003). A total of 30 patients (81.1%) completed total cavopulmonary connection (TCPC); 3 patients required re-intervention; 24 of 31 patients (77.4%) were in New York Heart Association classes II and I at the latest follow-up. CONCLUSIONS: Outcomes of CAVV repair in patients palliated by single-ventricular surgery are acceptable. The bivalvation technique is a simple and effective technique.

Whether Pulmonary Valve Replacement in Asymptomatic Patients With Moderate or Severe Regurgitation After Tetralogy of Fallot Repair Is Appropriate: A Case-Control Study.

Background Although right ventricular ( RV ) volume was significantly decreased in symptomatic patients with repaired tetralogy of Fallot ( rTOF ) after pulmonary valve replacement ( PVR ), RV size was still enlarged along with RV dysfunction. Methods and Results A prospective case-control study was conducted in a tertiary hospital; 81 asymptomatic repaired tetralogy of Fallot patients with moderate or severe pulmonary regurgitation were enrolled. The enrolled cohort was divided into 2 groups: PVR group (n=41) and medication group (n=40). Cardiac magnetic resonance, transthoracic echocardiography, and electrocardiography were scheduled after recruitment and 6 months after PVR or recruitment. Adverse events were recorded during follow-up. Three deaths, 1 heart transplantation, 3 PVR s, and 2 symptomatic heart failures in medication group and 1 redo PVR in the PVR group were observed during follow-up. Compared with the medication group, the PVR group had significantly lower adverse events rate ( P=0.023; odds ratio, 0.086; 95% CI, 0.010-0.716), and RV function was significantly improved ( P<0.05). Binary logistic regression analysis identified preoperative RV end-systolic volume index (10-mL/m2 increment, P=0.009; odds ratio, 0.64; 95% CI, 0.457-0.893) was an independent predictor of normalization of RV size after PVR . A preoperative RV end-systolic volume index cut-off value of 120 mL/m2 (area under curve, 0.819; sensitivity, 90.3%; specificity, 70%) was analyzed by receiver operating characteristic curves for normalized RV size after PVR . Conclusions PVR in asymptomatic repaired tetralogy of Fallot patients is appropriate and effective in reducing right ventricular size and preserving right ventricular function. The recommended criterion of RV end-systolic volume index for PVR is 120 mL/m2.