The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.

OBJECTIVES: To evaluate the diagnostic value of computer-aided diagnosis (CAD) software on ultrasound in distinguishing benign and malignant breast masses and avoiding unnecessary biopsy. METHODS: This prospective, multicenter study included patients who were scheduled for pathological diagnosis of breast masses between April 2019 and November 2020. Ultrasound images, videos, CAD analysis, and BI-RADS were obtained. The AUC, accuracy, sensitivity, specificity, PPV, and NPV were calculated and compared with radiologists. RESULTS: Overall, 901 breast masses in 901 patients were enrolled in this study. The accuracy, sensitivity, specificity, PPV and NPV of CAD software were 89.6%, 94.2%, 87.0%, 80.4%, and 96.3, respectively, in the long-axis section; 89.0%, 91.4%, 87.7%, 80.8%, and 94.7%, respectively, in the short-axis section. With BI-RADS 4a as the cut-off value, CAD software has a higher AUC (0.906 vs 0.734 vs 0.696, all p < 0.001) than both experienced and less experienced radiologists. With BI-RADS 4b as the cut-off value, CAD software showed better AUC than less experienced radiologists (0.906 vs 0.874, p < 0.001), but not superior to experienced radiologists (0.906 vs 0.883, p = 0.057). After the application of CAD software, the unnecessary biopsy rate of BI-RADS categories 4 and 5 was significantly decreased (33.0% vs 11.9%, 37.8% vs 14.5%), and the malignant rate of biopsy in category 4a was significantly increased (11.6% vs 40.7%, 7.4% vs 34.9%, all p < 0.001). CONCLUSIONS: CAD software on ultrasound can be used as an effective auxiliary diagnostic tool for differential diagnosis of benign and malignant breast masses and reducing unnecessary biopsy. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT03887598) KEY POINTS: • Prospective multicenter study showed that computer-aided diagnosis software provides greater diagnostic confidence for differentiating benign and malignant breast masses. • Computer-aided diagnosis software can help radiologists reduce unnecessary biopsy. • The management of patients with breast masses becomes more appropriate.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience.

OBJECTIVES: To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists. METHODS: From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed. RESULTS: After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05). CONCLUSIONS: Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

[Research on quality difference of standard decoction of raw and fried Paeoniae Radix Alba based on fingerprint and multicomponent determination].

The extract rates, multicomponent content and fingerprint were determined in this study to investigate the quality diffe-rence between standard decoction of raw Paeoniae Radix Alba and fried Paeoniae Radix Alba. UPLC fingerprint was established for 17 batches of standard decoction of raw and fried Paeoniae Radix Alba, and the contents of gallic acid, catechin, albiflorin, paeoniflorin and benzoyl paeoniflorin were determined. The peak areas of standard decoction were analyzed by the independent t-test and orthogonal partial least squares discriminant analysis. There was no significant difference in extract rates between the standard decoction of raw and fried Paeoniae Radix Alba. After fried processing, the content of albiflorin increased by 0.26%, while the contents of gallic acid, catechin, paeoniflorin and benzoyl paeoniflorin decreased by 13.04%, 27.97%, 10.30% and 18.79% respectively. There were 14 common peaks in the fingerprint of standard decoction of raw Paeoniae Radix Alba, and 16 common peaks in the fried Paeoniae Radix Alba. Peak 1 and peak 3 were new ones after processing, among which the peak 3 was 5-hydroxymethylfurfural. The results showed that peak 1, peak 3, peak 11 and peak 15 were the key compounds to distinguish standard decoction of raw and fried Paeoniae Radix Alba. In conclusion, this method is stable and can be used for the study of quantity transfer and quality control in the preparation process of standard decoction, granules and other dosage forms for raw and fried Paeoniae Radix Alba, providing reference for the identification of raw and fried Paeoniae Radix Alba and related preparations.

Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules.

OBJECTIVE: The purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS, TR) from the American College of Radiology (ACR). DESIGN AND METHODS: From June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms. RESULTS: In the independent test set, the DL algorithm of best performance got an AUC of 0.904, 0.845, 0.829 in TR4, TR5, and TR4&5, respectively. The sensitivity and specificity of the optimal model was 0.829, 0.831 on TR4, 0.846, 0.778 on TR5, 0.790, 0.779 on TR4&5, versus the radiologists of 0.686 (P=0.108), 0.766 (P=0.101), 0.677 (P=0.211), 0.750 (P=0.128), and 0.680 (P=0.023), 0.761 (P=0.530), respectively. CONCLUSIONS: The study demonstrated that DL could improve the differentiation of malignant from benign thyroid nodules and had significant potential for clinical application on TR4 and TR5.

Added Value of a New Strain Elastography Technique in Conventional Ultrasound for the Diagnosis of Breast Masses: A Prospective Multicenter Study.

OBJECTIVE: This study aimed to explore the value of elasticity score (ES) and strain ratio (SR) combined with conventional ultrasound in distinguishing benign and malignant breast masses and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions. METHODS: This prospective, multicenter study included 910 patients from nine different hospitals. The acquisition and analysis of conventional ultrasound and strain elastography (SE) were obtained by radiologists with more than 5 years of experience in breast ultrasound imaging. The diagnostic sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) of conventional ultrasound alone and combined tests with ES and/or SR were calculated and compared. RESULTS: The optimal cutoff value of SR for differentiating benign from malignant masses was 2.27, with a sensitivity of 60.2% and a specificity of 84.8%. When combined with ES and SR, the AUC of the new BI-RADS classification increased from 0.733 to 0.824 (p < 0.001); the specificity increased from 48.1% to 68.5% (p < 0.001) without a decrease in the sensitivity (98.5% vs. 96.4%, p = 0.065); and the PPV increased from 52.2% to 63.7% (p < 0.001) without a loss in the NPV (98.2% vs. 97.1%, p = 0.327). All three combinations of conventional ultrasound, ES, and SR could reduce the biopsy rate of category 4a lesions without reducing the malignant rate of biopsy (from 100% to 68.3%, 34.9%, and 50.4%, respectively, all p < 0.001). CONCLUSIONS: SE can be used as a useful and non-invasive additional method to improve the diagnostic performance of conventional ultrasound by increasing AUC and specificity and reducing the unnecessary biopsy of BI-RADS 4a lesions.

RIP mutated ITS genes in populations of Ophiocordyceps sinensis and their implications for molecular systematics.

Different hypotheses have been proposed to interpret the observed unusual ITS (internal transcribed spacer) sequences in Ophiocordyceps sinensis. The coexistence of diverged ITS paralogs in a single genome was previously shown by amplifying the ITS region from mono-ascospore isolates using specific primers designed for different ITS paralog groups. Among those paralogs, are AT-biased ITS sequences which were hypothesized to result from repeat-induced point mutation (RIP). This is a process that detects and mutates repetitive DNA and frequently leads to epigenetic silencing, and these mutations have been interpreted as pseudogenes. Here we investigate the occurrence and frequency of ITS pseudogenes in populations of O. sinensis using large-scale sampling, and discusses the implications of ITS pseudogenes for fungal phylogenetic and evolutionary studies. Our results demonstrate a wide distribution of ITS pseudogenes amongst different geographic populations, and indicate how ITS pseudogenes can contribute to the reconstruction of the evolutionary history of the species.

The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules.

AIMS: To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists. MATERIALS AND METHODS: Between February 2018 and October 2019, 204 thyroid nodules in 181 patients were included. An experienced radiologist performed ultrasound for thyroid nodules and obtained the result of S-Detect. Four radiologists with different experience on thyroid ultrasound (Radiologist 1, 2, 3, 4 with 1, 4, 9, 20 years, respectively) analyzed the conventional ultrasound images of each thyroid nodule and made a diagnosis of "benign" or "malignant" based on the TI-RADS category. After referring to S-Detect results, they re-evaluated the diagnoses. The diagnostic performance of radiologists was analyzed before and after referring to the results of S-Detect. RESULTS: The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of S-Detect were 77.0, 91.3, 65.2, 68.3 and 90.1%, respectively. In comparison with the less experienced radiologists (radiologist 1 and 2), S-Detect had a higher area under receiver operating characteristic curve (AUC), accuracy and specificity (p <0.05). In comparison with the most experienced radiologist, the diagnostic accuracy and AUC were lower (p<0.05). In the less experienced radiologists, the diagnostic accuracy, specificity and AUC were significantly improved when combined with S-Detect (p<0.05), but not for experienced radiologists (radiologist 3 and 4) (p>0.05). CONCLUSIONS: S-Detect may become an additional diagnostic method for the diagnosis of thyroid nodules and improve the diagnostic performance of less experienced radiologists.

NMR-based Metabolomic Techniques Identify the Toxicity of Emodin in HepG2 Cells.

Emodin is a natural anthraquinone derivative that is present in various herbal preparations. The pharmacological effects of emodin include anticancer, hepatoprotective, anti-inflammatory, antioxidant and even antimicrobial activities. However, emodin also has been reported to induce hepatotoxicity, nephrotoxicity, genotoxicity and reproductive toxicity. The mechanism of emodin's adverse effects is complicated and currently not well understood. This study aimed to establish a cell metabonomic method to investigate the toxicity of emodin and explore its potential mechanism and relevant targets. In the present study, metabonomic profiles of cell extracts and cell culture media obtained using the 1H NMR technique were used to assess emodin toxicity in HepG2 cells. Multivariate statistical analyses such as partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to characterize the metabolites that differed between the control and emodin groups. The results indicated that emodin resulted in differences in 33 metabolites, including acetate, arginine, aspartate, creatine, isoleucine, leucine and histidine in the cell extract samples and 23 metabolites, including alanine, formate, glutamate, succinate and isoleucine, in the cell culture media samples. Approximately 8 pathways associated with these metabolites were disrupted in the emodin groups. These results demonstrated the potential for using cell metabonomics approaches to clarify the toxicological effects of emodin, the underlying mechanisms and potential biomarkers. Our findings may help with the development of novel strategies to discover targets for drug toxicity, elucidate the changes in regulatory signal networks and explore its potential mechanism of action.

Effects of BRAF(V600E) mutation on Na(+)/I(-) symporter expression in papillary thyroid carcinoma.

Radioiodine ablation (RIA) therapy is one of the most important treatments for papillary thyroid carcinoma (PTC), but some patients who received (131)I have radioiodine-refractory disease caused by the decreased expression of the Na(+)/I(-) symporter (NIS). BRAF(V600E) mutation is one possible risk factor that can disturb the NIS expression, but the roles are unclear in clinical practice. This research discussed the association of BRAF(V600E) mutation and NIS expression in PTC tissue and the clinical implications in RIA therapy. 134 PTC samples were collected between June 2013 and June 2014 from Tongji Hospital affiliated to Tongji Medical College, and their clinical characteristics were analyzed. RT-PCR was used to detect the BRAF(V600E) mutation from formalin-fixed paraffin-embedded samples, and immunohistochemistry was applied to detect the NIS expression. IPP software was used to calculate the relative expression quantity of NIS. We found that there was no significant correlation between the absorbance (A) values of NIS and clinicopathologic features in these cases, even thyroid stimulating hormone. BRAF(V600E) mutation showed inhibitory effect on the NIS expression without statistically significant difference in all PTC cases (ß=-0.0195, P=0.085), but in the subgroup without hashimoto's thyroiditis (HT), BRAF(V600E) mutation could significantly inhibit the NIS expression (ß=-0.0257, P=0.046). The results indicate that BRAF(V600E) mutation is correlated with a lower expression of NIS in PTCs without HT, suggesting the radioiodine-refractory effects during RIA therapy in these patients.

[Evaluation of zinc deficiency tolerance in different kinds of apple rootstocks].

The objective of this study was to screen and evaluate the zinc deficiency tolerance among eight apple rootstocks, i.e., Malus baccata, M. rockii, M. xiaojinensis, M. sikkimensis, M. sieversii, M. robusta, M. hupehensis and Malus 'Flame'. The experiment took these 8 kinds of root-stocks as the research materials to observe and analyze the index of the rootstock's height, dry biomass, root architecture and zinc concentration, and with help of the fuzzy membership function to work out a comprehensive evaluation on their zinc deficiency tolerance. The result showed that several obvious zinc deficiency symptoms were observed in three kinds of rootstocks (M. rockii, M. sikkimensis and M. sieversii), such as dwarfed plant and newborn small leaves, while such symptoms were not obvious in M. xiaojinensis and M. 'Flame'. The plant biomass, height and zinc accumulation of aerial part greatly decreased under zinc deficiency stress, while smaller reduction was observed in M. xiaojinensis than in other rootstocks. M. xiaojinensis and M. baccata had higher zinc concentrations in leaves than others. According to the fuzzy membership function and cluster analysis, the resistance of the eight apple rootstocks to zinc deficiency was ranked: M. xiaojinensis was the highest one; M. 'Flame' was the second; M. baccata, M. sikkimensis, M. robusta and M. hupehensis were rather weaker; M. rockii and M. sieversii demonstrated the highest sensitivity to zinc deficiency.