Deep regression using 99mTc-DTPA dynamic renal imaging for automatic calculation of the glomerular filtration rate.

OBJECTIVES: To develop and evaluate an artificial intelligence (AI) system that can automatically calculate the glomerular filtration rate (GFR) from dynamic renal imaging without manually delineating the regions of interest (ROIs) of kidneys and the corresponding background. METHODS: This study was a single-center retrospective analysis of the data of 14,634 patients who underwent 99mTc-DTPA dynamic renal imaging. Two systems based on convolutional neural networks (CNN) were developed and evaluated: sGFRa predicts the radioactive counts of ROIs and calculates GFR using the Gates equation and sGFRb directly predicts GFR from dynamic renal imaging without using other information. The root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 were used to evaluate the performance of our approach. RESULTS: sGFRa achieved an RMSE of 5.05, MAE of 4.03, MAPE of 6.07%, and R2 of 0.93 for total GFR while sGFRb achieved an RMSE of 7.61, MAE of 5.92, MAPE of 8.92%, and R2 of 0.85 for total GFR. The accuracy of sGFRa and sGFRb in determining the stage of chronic kidney disease was 87.41% and 82.44%, respectively. CONCLUSIONS: The findings of sGFRa show that automatic GFR calculation based on CNN and using dynamic renal imaging is feasible and efficient and, additionally, can aid clinical diagnosis. Furthermore, the promising results of sGFRb demonstrate that CNN can predict GFR from dynamic renal imaging without additional information. KEY POINTS: • Our CNN-based AI systems can automatically calculate GFR from dynamic renal imaging without manually delineating the ROIs of kidneys and the corresponding background. • sGFRa accurately predicted the radioactive counts of ROIs and calculated GFR using the Gates method. • sGFRb-predicted GFR directly without any parameters related to the Gates equation.

Application of deep learning to identify ductal carcinoma in situ and microinvasion of the breast using ultrasound imaging.

Background: The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images. Methods: The clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data. Results: The characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER-; P=0.003), progesterone receptor negative (PR-; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765. Conclusions: Deep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.

An automatic fine-grained skeleton segmentation method for whole-body bone scintigraphy using atlas-based registration.

PURPOSE: Whole-body bone scintigraphy (WBS) is one of the common imaging methods in nuclear medicine. It is a time-consuming, tedious, and error-prone issue for physicians to determine the location of bone lesions which is important for the qualitative diagnosis of bone lesions. In this paper, an automatic fine-grained skeleton segmentation method for WBS is developed. METHOD: The proposed method contains four steps. In the first step, a novel denoising method is proposed to remove the noise from WBS which benefits the location of the skeleton. In the second step, a restoration method based on gray probability distribution is developed to repair the partial contamination caused by the high local density of radionuclide. Then, the standardization for WBS is performed by the exact histogram matching. Finally, the deformation field between the atlas and the input WBS is calculated by registration, and the segmentation mask of the input WBS is obtained by wrapping the segmentation mask of the atlas with the deformation field. RESULTS: The experimental results show that the proposed method outperforms the traditional registration (Morphon): mean square error decreased from [Formula: see text] to [Formula: see text], peak signal-to-noise ratio increased from 21.26 to 26.92, and mean structural similarity increased from 0.9986 to 0.9998. CONCLUSIONS: Our experiments show that the proposed method can achieve robust and fine-grained results which outperform the traditional registration method, indicating it could be helpful in clinical application. To the best of our knowledge, this is the first work that implements a fully automated fine-grained skeleton segmentation method for WBS.

Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study.

BACKGROUND: 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. METHODS: We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model's performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. RESULTS: The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for 'diffusely increased,' 0.997 for 'diffusely decreased,' 0.998 for 'focal increased,' and 0.945 for 'heterogeneous uptake' in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. CONCLUSIONS: Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs). METHODS: We retrospectively collected the 99mTc-MDP WBS images with confirmed bone lesions from 3352 patients with malignancy. 14,972 bone lesions were delineated manually by physicians and annotated as benign and malignant. The lesion-based differentiating performance of the proposed network was evaluated by fivefold cross validation, and compared with the other three popular CNN architectures for medical imaging. The average sensitivity, specificity, accuracy and the area under receiver operating characteristic curve (AUC) were calculated. To delve the outcomes of this study, we conducted subgroup analyses, including lesion burden number and tumor type for the classifying ability of the CNN. RESULTS: In the fivefold cross validation, our proposed network reached the best average accuracy (81.23%) in identifying suspicious bone lesions compared with InceptionV3 (80.61%), VGG16 (81.13%) and DenseNet169 (76.71%). Additionally, the CNN model's lesion-based average sensitivity and specificity were 81.30% and 81.14%, respectively. Based on the lesion burden numbers of each image, the area under the receiver operating characteristic curve (AUC) was 0.847 in the few group (lesion number n ≤ 3), 0.838 in the medium group (n = 4-6), and 0.862 in the extensive group (n > 6). For the three major primary tumor types, the CNN-based lesion identifying AUC value was 0.870 for lung cancer, 0.900 for prostate cancer, and 0.899 for breast cancer. CONCLUSION: The CNN model suggests potential in identifying suspicious benign and malignant bone lesions from whole-body bone scintigraphic images.

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of 99mTc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis.

Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networks.

Bone scintigraphy is accepted as an effective diagnostic tool for whole-body examination of bone metastasis. However, the manual analysis of bone scintigraphy images requires extensive experience and is exhausting and time-consuming. An automated diagnosis system for such images is therefore much desired. Although automatic or semi-automatic methods for the diagnosis of bone scintigraphy images have been widely studied, they employ various steps to classify the images, including segmentation of the entire skeleton, detection of hot spots, and feature extraction, which are complex and inadequately validated on small datasets, thereby resulting in low accuracy and reliability. In this paper, we describe the development of a deep convolutional neural network to determine the absence or presence of bone metastasis. This model consisting of three sub-networks that aim to extract, aggregate, and classify high-level features in a data-driven manner. There are two main innovations behind this method; First, the diagnosis is performed by jointly analyzing both anterior and posterior views, which leads to high accuracy. Second, a spatial attention feature aggregation operator is proposed to enhance the spatial location information. A large annotated bone scintigraphy image dataset containing 15,474 examinations from 13,811 patients was constructed to train and evaluate the model. The proposed method is compared with three human experts. The high classification accuracy achieved demonstrates the effectiveness of the proposed architecture for the diagnosis of bone scintigraphy images, and that it can be applied as a clinical decision support tool.

Automated diagnosis of breast ultrasonography images using deep neural networks.

Ultrasonography images of breast mass aid in the detection and diagnosis of breast cancer. Manually analyzing ultrasonography images is time-consuming, exhausting and subjective. Automated analyzing such images is desired. In this study, we develop an automated breast cancer diagnosis model for ultrasonography images. Traditional methods of automated ultrasonography images analysis employ hand-crafted features to classify images, and lack robustness to the variation in the shapes, size and texture of breast lesions, leading to low sensitivity in clinical applications. To overcome these shortcomings, we propose a method to diagnose breast ultrasonography images using deep convolutional neural networks with multi-scale kernels and skip connections. Our method consists of two components: the first one is to determine whether there are malignant tumors in the image, and the second one is to recognize solid nodules. In order to let the two networks work in a collaborative way, a region enhance mechanism based on class activation maps is proposed. The mechanism helps to improve classification accuracy and sensitivity for both networks. A cross training algorithm is introduced to train the networks. We construct a large annotated dataset containing a total of 8145 breast ultrasonography images to train and evaluate the models. All of the annotations are proven by pathological records. The proposed method is compared with two state-of-the-art approaches, and outperforms both of them by a large margin. Experimental results show that our approach achieves a performance comparable to human sonographers and can be applied to clinical scenarios.

Intestinal absorption of pallidifloside D are limited by P-glycoprotein in mice.

1. Pallidifloside D, a saponin glycoside constituent from the total saponins of Smilax riparia, had been proved to be very effective in hyperuricemic control. But it is poorly bioavailable after oral administration. Here, we determined the role of P-glycoprotein (P-gp) in the intestinal absorption of Pallidifloside D. 2. We found that Pallidifloside D significantly stimulated P-gp ATPase activity in vitro ATPase assay with a small EC50 value of 0.46 µM. 3. In the single-pass perfused mouse intestine model, the absorption of Pallidifloside D was not favored in the small intestine (duodenum, jejunum and ileum) with a P*w value of 0.35-0.78. By contrast, this compound was well-absorbed in the colon with a P*w value of 1.23. The P-gp inhibitors cyclosporine significantly enhanced Pallidifloside D absorption in all four intestinal segments (duodenum, jejunum, ileum and colon) and the fold change ranged from 5.5 to 15.3. Pharmacokinetic study revealed that cyclosporine increased the systemic exposure of Pallidifloside D by a 2.5-fold after oral administration. 4. These results suggest that P-gp-mediated efflux is a limiting factor for intestinal absorption of Pallidifloside D in mice.

Hypouricemic effect of allopurinol are improved by Pallidifloside D based on the uric acid metabolism enzymes PRPS, HGPRT and PRPPAT.

Allopurinol is a commonly used medication to treat hyperuricemia and its complications. Pallidifloside D, a saponin glycoside constituent from the total saponins of Smilax riparia, had been proved to enhanced hypouricemic effect of allopurinol based on uric acid metabolism enzyme XOD. In this study, we evaluated whether Pallidifloside D (5mg/kg) enhanced hypouricemic effect of allopurinol (5mg/kg) related to others uric acid metabolism enzymes such as PRPS, HGPRT and PRPPAT. We found that, compared with allopurinol alone, the combination of allopurinol and Pallidifloside D significantly up-regulated HGPRT mRNA expression and down-regulated the mRNA expression of PRPS and PRPPAT in PC12 cells (all P<0.01). These results strongly suggest that hypouricemic effect of allopurinol are improved by Pallidifloside D via numerous mechanisms and our data may have a potential value in clinical practice in the treatment of gout and other hyperuricemic conditions.

Pallidifloside D from Smilax riparia enhanced allopurinol effects in hyperuricemia mice.

Pallidifloside D, a saponin glycoside constituent from the total saponins of Smilax riparia, had been proved to be effective in hyperuricemic control. Allopurinol is a commonly used medication to treat hyperuricemia and its complications. In this study, we evaluated whether Pallidifloside D could enhance allopurinol's effects by decreasing the serum uric acid level in a hyperuricemic mouse model induced by potassium oxonate. We found that, compared with allopurinol alone, the combination of allopurinol and Pallidifloside D significantly decreased the serum uric acid level and increased the urine uric acid level (both P<0.05), leading to the normalized serum and urine uric acid concentrations. Data on serum, urine creatinine and BUN supported these observations. Our results showed that the synergistic effects of allopurinol combined with Pallidifloside D were linked to the inhibition of both serum and hepatic xanthine oxidase (XOD), the down-regulation of renal mURAT1 and mGLUT9, and the up-regulation of mOAT1. Our data may have a potential value in clinical practice in the treatment of gout and other hyperuricemic conditions.

Bis[µ-N'-isobutyryl-1-oxidonaphtha-lene-2-carbohydrazidato(3-)]dipyridine-tricopper(II).

The complete mol-ecule of the title complex, [Cu(3)(C(15)H(13)N(2)O(3))(2)(C(5)H(5)N)(2)], is generated by crystallographic twofold symmetry, with the central Cu atom lying on the rotation axis: it is coordinated by two N,O-bidentate ligands in a trans-CuN(2)O(2) distorted square-planar arrangement. The other Cu atom is coordinated by an N,O,O'-tridentate ligand and a pyridine mol-ecule in a distorted trans-CuN(2)O(2) arrangement. In the crystal structure, a C-H⋯π inter-action occurs.

[Studies on transplanting suspension-cultured protocorms of Dendrobium candidum onto solid culture medium].

OBJECTIVE: The suspension-cultured protocorms of Dendrobium candidum were transplanted on the solid culture medium for studying the factors influencing their differentiation and growth. METHOD: The growth and differentiation of protocorms were detected when different base-media, plant phytohormones, carbohydrates and pH were applied. RESULT AND CONCLUSION: The diluted MS medium promotes the growth of protocorms but inhibit differentiation. The phytohormone NAA, IAA, IBA and GA are helpful to the growth and differentiation of protocorms, however, the low pH values and presence of autoclaved fungi have suppressive effects on protocorms. The fungi also induce morphological variation of protocorms.

Studies on transplanting suspension-cultured protocorms of Dendrobium candidum onto solid culture medium / 中国中药杂志

<p><b>OBJECTIVE</b>The suspension-cultured protocorms of Dendrobium candidum were transplanted on the solid culture medium for studying the factors influencing their differentiation and growth.</p><p><b>METHOD</b>The growth and differentiation of protocorms were detected when different base-media, plant phytohormones, carbohydrates and pH were applied.</p><p><b>RESULT AND CONCLUSION</b>The diluted MS medium promotes the growth of protocorms but inhibit differentiation. The phytohormone NAA, IAA, IBA and GA are helpful to the growth and differentiation of protocorms, however, the low pH values and presence of autoclaved fungi have suppressive effects on protocorms. The fungi also induce morphological variation of protocorms.</p>

INFLUENCES OF FUNGUS MYCENAE SP.ON ACTIVITIES OF SOME ENZYMES AND EXTRACELLULAR PH OF PROTOCORMS OF DENDROBIUM CANDIDUM / 微生物学通报

An elicitor from fungus Mycenae sp. enhanced the extracellular pH of protocorms of Dendrobium candidum in two stages and also inspired the activities of PAL, POD and LOX. The different elicitors were different in enhancing the pH. The activities of PAL and POD ascended twice after elicitors were applied. The protocorms treated twice by elicitor had the higher PAL activity.