Latest Issue
Volume 36 ， Issue 3 ， 2021
Guest Editer‘s Letter
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Abstract：Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide. China covers over half of cases, leading HCC to be a vital threaten to public health. Despite advances in diagnosis and treatments, high recurrence rate remains a major obstacle in HCC management. Multi-omics currently facilitates surveillance, precise diagnosis, and personalized treatment decision making in clinical setting. Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes. Radiomics has been widely used in histopathological diagnosis prediction, treatment response evaluation, and prognosis prediction. High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC, which would reveal the complex multistep process of the pathophysiology. The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics, and show potential to convert surgical/intervention treatment into an antitumorigenic one, which would greatly advance precision medicine in HCC management.Keywords：hepatocellular carcinoma;radiomics;proteomics;genomics;multi-omics50|886|0Updated：2024-04-10 - Abstract：The recent spring up of the antineoplastic agents and the prolonged survival bring both challenge and chance to radiological practice. Radiological methods including CT, MRI and PET play an increasingly important role in evaluating the efficacy of these antineoplastic drugs. However, different antineoplastic agents potentially induce different radiological signs, making it a challenge for radiological response evaluation, which depends mainly on one-sided morphological response evaluation criteria in solid tumors (RECIST) in the status quo of clinical practice. This brings opportunities for the development of radiomics, which is promising to serve as a surrogate for response evaluations of anti-tumor treatments. In this article, we introduce the basic concepts of radiomics, review the state-of-art radiomics researches with highlights of radiomics application in predictions of molecular biomarkers, treatment response, and prognosis. We also provide in-depth analyses on major obstacles and future direction of this new technique in clinical investigations on new antineoplastic agents.Keywords：radiomics;deep learning;machine learning;antineoplastic agents;response evaluation23|393|0Updated：2024-04-10
- Abstract：Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women’s cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.Keywords：artificial intelligence;machine learning;ovarian cancer;radiomics;algorithm;medical imaging47|938|0Updated：2024-04-10
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Abstract：Fuwai Hospital was established in 1956 and the Anesthesia Department of Fuwai Hospital was one of the earliest anesthesia departments then in China. Under the leadership of several department directors and with the concerted efforts of all generations of colleagues, the Anesthesia Department of Fuwai Hospital has dramatically transformed, upgraded and modernized. For more than six decades, the Anesthesia Department has been providing high-quality peri-operative anesthesia care for cardiovascular surgeries, conducting innovative experimental and clinical researches, and offering comprehensive training on cardiovascular anesthesiology for professionals across China. Currently, Fuwai Hospital is the National Center for Cardiovascular Diseases of China and one of the largest cardiovascular centers in the world. The present review introduces the Anesthesia Department of Fuwai Hospital, summarizes its current practice of anesthesia management, the outcomes of cardiovascular surgeries at Fuwai Hospital, accumulates relevant evidence, and provides prospects for future development of cardiovascular anesthesiology.Keywords：anesthesia;management;cardiac surgery;outcome307|6845|0Updated：2024-04-10
REVIEW
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Abstract：Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists’ consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.Keywords：artificial intelligence;deep learning;algorithm;gastritis;whole-slide pathological images35|523|0Updated：2024-04-10 -
Abstract：Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine, etc. Results In the external validation, the lesion-level PPV and sensitivity of the model were 57.9% and 67.0%, respectively. On average, the model detected 2.1 findings per set, and among them, 0.9 were false positives. ULDor worked well for detecting liver lesions, with a PPV of 78.9% and a sensitivity of 92.7%, followed by kidney, with a PPV of 70.0% and a sensitivity of 58.3%. In internal validation with NLST test set, ULDor obtained a PPV of 75.3% and a sensitivity of 52.0% despite the relatively high noise level of soft tissue on images. Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs. With further optimisation and iterative upgrades, ULDor may be well suited for extensive application to external data.Keywords：lesion detection;computer-aided diagnosis;convolutional neural network;deep learning17|564|0Updated：2024-04-10 -
Abstract：Objective To establish a nomogram for predicting the distant metastasis risk of pancreatic neuroendocrine tumors (pNETs) in elderly patients. Methods We extracted data of patients with diagnosis of pNETs at age ≥65 years old between 1973 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. All eligible patients were divided randomly into a training cohort and validation cohort. Uni- and multivariate logistic regression analyses were performed on the training cohort to identify independent factors for distant metastasis. A nomogram was developed based on the independent risk factors using rms packages of R software, and was validated internally by the training cohort and externally by the validation cohort using C-index and calibration curves. Results A total of 411 elderly patients were identified, of which 260 were assigned to training cohort and 151 to validation cohort. Univariate and multivariate logistic regression analyses indicated the tumor site (body/tail of pancreas: odds ratio [OR]=2.282; 95% confidence interval [CI]: 1.174 - 4.436, P<0.05), histological grade (poorly differentiated/undifferentiated: OR=2.600, 95% CI: 1.266-5.339, P<0.05), T stage (T2: OR=8.913, 95% CI: 1.985-40.010, P<0.05; T3: OR=11.830, 95% CI: 2.530-55.350, P<0.05; T4: OR=68.650, 95% CI: 8.020-587.600, P<0.05), and N stage (N1: OR=3.480, 95% CI: 1.807-6.703, P<0.05) were identified as independent risk factors for distant metastasis of pNETs in elderly. The nomogram exhibited good predicting accuracy, with a C-index of 0.809 (95% CI: 0.757 - 0.861) in internal validation and 0.795 (95% CI: 0.723 - 0.867) in external validation, respectively. The predicted distant metastasis rates were in satisfactory agreement with the observed values by the calibration curves. Conclusion The nomogram we established showed high discriminative ability and accuracy in evaluation of distant metastasis risk in elderly pNETs patients, and could provide a reference for individualized tumor evaluation and treatment decision in elderly pNETs patients.Keywords：nomogram;pancreatic neuroendocrine tumor;distant metastasis;elderly patients24|755|0Updated：2024-04-10 -
Abstract：Objective This study aimed to assess the protective value of adiponectin (APN) in pancreatic islet injury induced by chronic intermittent hypoxia (CIH). Methods Sixty rats were randomly divided into three groups: normal control (NC) group, CIH group, and CIH with APN supplement (CIH+APN) group. After 5 weeks of CIH exposure, we conducted oral glucose tolerance tests (OGTT) and insulin released test (IRT), examined and compared the adenosine triphosphate (ATP) levels, mitochondrial membrane potential (MMP) levels, reactive oxygen species (ROS) levels, enzymes gene expression levels of Ant1, Cs, Hmox1, and Cox4i1 which represented mitochondrial tricarboxylic acid cycle function, the protein and gene expression levels of DRP1, FIS1, MFN1, and OPA1 which represented mitochondrial fusion and division, and the protein expression levels of BAX, BCL-2, cleaved Caspase-3, and cleaved PARP which represented mitochondrial associated apoptosis pathway of pancreatic islet. Results OGTT and IRT showed blood glucose and insulin levels had no differences among the NC, CIH and CIH+APN groups (both P>0.05) at 0 min, 20 min, 30 min, 60 min, 120 min. However, we found that compared to NC group, CIH increased the ROS level, reduced ATP level and MMP level. The islets of CIH exposed rats showed reduced gene expression levels of Ant1, Cs, Hmox1, and Cox4i1, decreased protein and gene expression levels of MFN1 and OPA1, increased protein and gene expression levels of DRP1 and FIS1, increased protein expression levels of cleaved Caspase-3 and cleaved PARP, with lower ratio of BCL-2/BAX at protein expression level. All the differences among three groups were statistically significant. APN treated CIH rats showed mitigated changes in the above measurements associated with islet injuries. Conclusion APN may ameliorate the pancreatic islet injury induced by CIH via inhibiting the imbalance in mitochondrial fusion and division.Keywords：obstructive sleep apnea hypopnea syndrome;chronic intermittent hypoxia;adiponectin;mitochondrial fusion and division;pancreatic islet14|660|0Updated：2024-04-10
ORIGINAL ARTICLE
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Abstract：Computed tomography (CT) examination is the major measure for detecting and diagnosis of foreign bodies in human body. Although CT has high sensitivity in diagnosis of foreign body, some interference factors may still lead to missed diagnosis or misdiagnosis. Here we report a rare case that a bamboo stick accidentally pierced into the left chest of a young man who was drunk and unware of this hurt. The patient experienced cough, chest pain, fever, hemoptysis, and was misdiagnosed as primary and secondary tuberculosis based on chest CT examinations at a local hospital, although no tubercular bacillus detected by sputum smear. He subsequently received anti-tuberculous treatments in the following three years, but no improvement of his symptoms was observed. Until one month before his death, the bamboo stick was detected by spiral CT examination as well as three-dimensional image reconstruction at another hospital. Postmortem examination revealed pneumonia, pulmonary infarction, and abscess as the causes of his death. We analyze the potential reasons of misdiagnosis in this case, aiming to provide reference for the diagnosis and treatment of pulmonary inflammation associated with foreign body in the future.Keywords：foreign body;pneumonia;pulmonary abscess;pulmonary tuberculosis;misdiagnosis17|287|0Updated：2024-04-10
CASE REPORT
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